From 064635d515c448269b854520813fbf4c372cc01e Mon Sep 17 00:00:00 2001 From: "Documenter.jl" Date: Thu, 17 Aug 2023 15:58:19 +0200 Subject: [PATCH] build based on 375c6e4 --- previews/PR26/assets/documenter.js | 331 ++++++++++++++++++ previews/PR26/assets/favicon.ico | Bin 0 -> 112484 bytes previews/PR26/assets/logo.png | Bin 0 -> 3634 bytes previews/PR26/assets/logo.svg | 75 ++++ previews/PR26/assets/search.js | 267 ++++++++++++++ .../PR26/assets/themes/documenter-dark.css | 7 + .../PR26/assets/themes/documenter-light.css | 9 + previews/PR26/assets/themeswap.js | 66 ++++ previews/PR26/assets/warner.js | 49 +++ previews/PR26/devices/a100_sxm2/index.html | 189 ++++++++++ .../PR26/devices/geforce_gtx_1650/index.html | 92 +++++ previews/PR26/devices/v100_sxm2/index.html | 169 +++++++++ .../PR26/examples/data_bandwidth/index.html | 93 +++++ previews/PR26/examples/gpuinfo/index.html | 69 ++++ .../PR26/examples/gpustresstest/index.html | 120 +++++++ .../p2p_memcpy_bidirectional_dgx_profile.png | Bin 0 -> 1440296 bytes .../PR26/examples/p2p_memcpy_dgx_profile.png | Bin 0 -> 575762 bytes .../PR26/examples/peakflops_gpu/index.html | 60 ++++ .../NVIDIA-DGX-A100-Block-Diagram.png | Bin 0 -> 1071569 bytes previews/PR26/explanations/dgx/index.html | 2 + previews/PR26/index.html | 3 + .../PR26/refs/UnitPrefixedBytes/index.html | 10 + previews/PR26/refs/backends/index.html | 3 + previews/PR26/refs/cuda_extension/index.html | 3 + previews/PR26/refs/data_bandwidth/index.html | 6 + previews/PR26/refs/gpuinfo/index.html | 2 + previews/PR26/refs/gpustresstest/index.html | 2 + previews/PR26/refs/monitoring/index.html | 2 + previews/PR26/refs/peakflops_gpu/index.html | 2 + previews/PR26/refs/stresstest_cpu/index.html | 2 + previews/PR26/refs/utility/index.html | 2 + previews/PR26/search/index.html | 2 + previews/PR26/search_index.js | 3 + previews/PR26/siteinfo.js | 1 + 34 files changed, 1641 insertions(+) create mode 100644 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previews/PR26/examples/p2p_memcpy_dgx_profile.png create mode 100644 previews/PR26/examples/peakflops_gpu/index.html create mode 100644 previews/PR26/explanations/NVIDIA-DGX-A100-Block-Diagram.png create mode 100644 previews/PR26/explanations/dgx/index.html create mode 100644 previews/PR26/index.html create mode 100644 previews/PR26/refs/UnitPrefixedBytes/index.html create mode 100644 previews/PR26/refs/backends/index.html create mode 100644 previews/PR26/refs/cuda_extension/index.html create mode 100644 previews/PR26/refs/data_bandwidth/index.html create mode 100644 previews/PR26/refs/gpuinfo/index.html create mode 100644 previews/PR26/refs/gpustresstest/index.html create mode 100644 previews/PR26/refs/monitoring/index.html create mode 100644 previews/PR26/refs/peakflops_gpu/index.html create mode 100644 previews/PR26/refs/stresstest_cpu/index.html create mode 100644 previews/PR26/refs/utility/index.html create mode 100644 previews/PR26/search/index.html create mode 100644 previews/PR26/search_index.js create mode 100644 previews/PR26/siteinfo.js diff --git a/previews/PR26/assets/documenter.js b/previews/PR26/assets/documenter.js new file mode 100644 index 0000000..6adfbbb --- /dev/null +++ b/previews/PR26/assets/documenter.js @@ -0,0 +1,331 @@ +// Generated by Documenter.jl +requirejs.config({ + paths: { + 'highlight-julia': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.5.1/languages/julia.min', + 'headroom': 'https://cdnjs.cloudflare.com/ajax/libs/headroom/0.12.0/headroom.min', + 'jqueryui': 'https://cdnjs.cloudflare.com/ajax/libs/jqueryui/1.12.1/jquery-ui.min', + 'katex-auto-render': 'https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.13.24/contrib/auto-render.min', + 'jquery': 'https://cdnjs.cloudflare.com/ajax/libs/jquery/3.6.0/jquery.min', + 'headroom-jquery': 'https://cdnjs.cloudflare.com/ajax/libs/headroom/0.12.0/jQuery.headroom.min', + 'katex': 'https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.13.24/katex.min', + 'highlight': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.5.1/highlight.min', + 'highlight-julia-repl': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.5.1/languages/julia-repl.min', + }, + shim: { + "highlight-julia": { + "deps": [ + "highlight" + ] + }, + "katex-auto-render": { + "deps": [ + "katex" + ] + }, + "headroom-jquery": { + "deps": [ + "jquery", + "headroom" + ] + }, + "highlight-julia-repl": { + "deps": [ + "highlight" + ] + } +} +}); +//////////////////////////////////////////////////////////////////////////////// +require(['jquery', 'katex', 'katex-auto-render'], function($, katex, renderMathInElement) { +$(document).ready(function() { + renderMathInElement( + document.body, + { + "delimiters": [ + { + "left": "$", + "right": "$", + "display": false + }, + { + "left": "$$", + "right": "$$", + "display": true + }, + { + "left": "\\[", + "right": "\\]", + "display": true + } + ] +} + + ); +}) + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery', 'highlight', 'highlight-julia', 'highlight-julia-repl'], function($) { +$(document).ready(function() { + hljs.highlightAll(); +}) + +}) +//////////////////////////////////////////////////////////////////////////////// +require([], function() { +function addCopyButtonCallbacks() { + for (const el of document.getElementsByTagName("pre")) { + const button = document.createElement("button"); + button.classList.add("copy-button", "fas", "fa-copy"); + el.appendChild(button); + + const success = function () { + button.classList.add("success", "fa-check"); + button.classList.remove("fa-copy"); + }; + + const failure = function () { + button.classList.add("error", "fa-times"); + button.classList.remove("fa-copy"); + }; + + button.addEventListener("click", function () { + copyToClipboard(el.innerText).then(success, failure); + + setTimeout(function () { + button.classList.add("fa-copy"); + button.classList.remove("success", "fa-check", "fa-times"); + }, 5000); + }); + } +} + +function copyToClipboard(text) { + // clipboard API is only available in secure contexts + if (window.navigator && window.navigator.clipboard) { + return window.navigator.clipboard.writeText(text); + } else { + return new Promise(function (resolve, reject) { + try { + const el = document.createElement("textarea"); + el.textContent = text; + el.style.position = "fixed"; + el.style.opacity = 0; + document.body.appendChild(el); + el.select(); + document.execCommand("copy"); + + resolve(); + } catch (err) { + reject(err); + } finally { + document.body.removeChild(el); + } + }); + } +} + +if (document.readyState === "loading") { + document.addEventListener("DOMContentLoaded", addCopyButtonCallbacks); +} else { + addCopyButtonCallbacks(); +} + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery', 'headroom', 'headroom-jquery'], function($, Headroom) { + +// Manages the top navigation bar (hides it when the user starts scrolling down on the +// mobile). +window.Headroom = Headroom; // work around buggy module loading? +$(document).ready(function() { + $('#documenter .docs-navbar').headroom({ + "tolerance": {"up": 10, "down": 10}, + }); +}) + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// Modal settings dialog +$(document).ready(function() { + var settings = $('#documenter-settings'); + $('#documenter-settings-button').click(function(){ + settings.toggleClass('is-active'); + }); + // Close the dialog if X is clicked + $('#documenter-settings button.delete').click(function(){ + settings.removeClass('is-active'); + }); + // Close dialog if ESC is pressed + $(document).keyup(function(e) { + if (e.keyCode == 27) settings.removeClass('is-active'); + }); +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// Manages the showing and hiding of the sidebar. +$(document).ready(function() { + var sidebar = $("#documenter > .docs-sidebar"); + var sidebar_button = $("#documenter-sidebar-button") + sidebar_button.click(function(ev) { + ev.preventDefault(); + sidebar.toggleClass('visible'); + if (sidebar.hasClass('visible')) { + // Makes sure that the current menu item is visible in the sidebar. + $("#documenter .docs-menu a.is-active").focus(); + } + }); + $("#documenter > .docs-main").bind('click', function(ev) { + if ($(ev.target).is(sidebar_button)) { + return; + } + if (sidebar.hasClass('visible')) { + sidebar.removeClass('visible'); + } + }); +}) + +// Resizes the package name / sitename in the sidebar if it is too wide. +// Inspired by: https://github.com/davatron5000/FitText.js +$(document).ready(function() { + e = $("#documenter .docs-autofit"); + function resize() { + var L = parseInt(e.css('max-width'), 10); + var L0 = e.width(); + if(L0 > L) { + var h0 = parseInt(e.css('font-size'), 10); + e.css('font-size', L * h0 / L0); + // TODO: make sure it survives resizes? + } + } + // call once and then register events + resize(); + $(window).resize(resize); + $(window).on('orientationchange', resize); +}); + +// Scroll the navigation bar to the currently selected menu item +$(document).ready(function() { + var sidebar = $("#documenter .docs-menu").get(0); + var active = $("#documenter .docs-menu .is-active").get(0); + if(typeof active !== 'undefined') { + sidebar.scrollTop = active.offsetTop - sidebar.offsetTop - 15; + } +}) + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +function set_theme(theme) { + var active = null; + var disabled = []; + for (var i = 0; i < document.styleSheets.length; i++) { + var ss = document.styleSheets[i]; + var themename = ss.ownerNode.getAttribute("data-theme-name"); + if(themename === null) continue; // ignore non-theme stylesheets + // Find the active theme + if(themename === theme) active = ss; + else disabled.push(ss); + } + if(active !== null) { + active.disabled = false; + if(active.ownerNode.getAttribute("data-theme-primary") === null) { + document.getElementsByTagName('html')[0].className = "theme--" + theme; + } else { + document.getElementsByTagName('html')[0].className = ""; + } + disabled.forEach(function(ss){ + ss.disabled = true; + }); + } + + // Store the theme in localStorage + if(typeof(window.localStorage) !== "undefined") { + window.localStorage.setItem("documenter-theme", theme); + } else { + console.error("Browser does not support window.localStorage"); + } +} + +// Theme picker setup +$(document).ready(function() { + // onchange callback + $('#documenter-themepicker').change(function themepick_callback(ev){ + var themename = $('#documenter-themepicker option:selected').attr('value'); + set_theme(themename); + }); + + // Make sure that the themepicker displays the correct theme when the theme is retrieved + // from localStorage + if(typeof(window.localStorage) !== "undefined") { + var theme = window.localStorage.getItem("documenter-theme"); + if(theme !== null) { + $('#documenter-themepicker option').each(function(i,e) { + e.selected = (e.value === theme); + }) + } else { + $('#documenter-themepicker option').each(function(i,e) { + e.selected = $("html").hasClass(`theme--${e.value}`); + }) + } + } +}) + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// update the version selector with info from the siteinfo.js and ../versions.js files +$(document).ready(function() { + // If the version selector is disabled with DOCUMENTER_VERSION_SELECTOR_DISABLED in the + // siteinfo.js file, we just return immediately and not display the version selector. + if (typeof DOCUMENTER_VERSION_SELECTOR_DISABLED === 'boolean' && DOCUMENTER_VERSION_SELECTOR_DISABLED) { + return; + } + + var version_selector 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a/previews/PR26/assets/logo.svg b/previews/PR26/assets/logo.svg new file mode 100644 index 0000000..69293ad --- /dev/null +++ b/previews/PR26/assets/logo.svg @@ -0,0 +1,75 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + GPU + + + diff --git a/previews/PR26/assets/search.js b/previews/PR26/assets/search.js new file mode 100644 index 0000000..c133f74 --- /dev/null +++ b/previews/PR26/assets/search.js @@ -0,0 +1,267 @@ +// Generated by Documenter.jl +requirejs.config({ + paths: { + 'lunr': 'https://cdnjs.cloudflare.com/ajax/libs/lunr.js/2.3.9/lunr.min', + 'lodash': 'https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.21/lodash.min', + 'jquery': 'https://cdnjs.cloudflare.com/ajax/libs/jquery/3.6.0/jquery.min', + } +}); +//////////////////////////////////////////////////////////////////////////////// +require(['jquery', 'lunr', 'lodash'], function($, lunr, _) { + +$(document).ready(function() { + // parseUri 1.2.2 + // (c) Steven Levithan + // MIT License + function parseUri (str) { + var o = parseUri.options, + m = o.parser[o.strictMode ? "strict" : "loose"].exec(str), + uri = {}, + i = 14; + + while (i--) uri[o.key[i]] = m[i] || ""; + + uri[o.q.name] = {}; + uri[o.key[12]].replace(o.q.parser, function ($0, $1, $2) { + if ($1) uri[o.q.name][$1] = $2; + }); + + return uri; + }; + parseUri.options = { + strictMode: false, + key: ["source","protocol","authority","userInfo","user","password","host","port","relative","path","directory","file","query","anchor"], + q: { + name: "queryKey", + parser: /(?:^|&)([^&=]*)=?([^&]*)/g + }, + parser: { + strict: /^(?:([^:\/?#]+):)?(?:\/\/((?:(([^:@]*)(?::([^:@]*))?)?@)?([^:\/?#]*)(?::(\d*))?))?((((?:[^?#\/]*\/)*)([^?#]*))(?:\?([^#]*))?(?:#(.*))?)/, + loose: /^(?:(?![^:@]+:[^:@\/]*@)([^:\/?#.]+):)?(?:\/\/)?((?:(([^:@]*)(?::([^:@]*))?)?@)?([^:\/?#]*)(?::(\d*))?)(((\/(?:[^?#](?![^?#\/]*\.[^?#\/.]+(?:[?#]|$)))*\/?)?([^?#\/]*))(?:\?([^#]*))?(?:#(.*))?)/ + } + }; + + $("#search-form").submit(function(e) { + e.preventDefault() + }) + + // list below is the lunr 2.1.3 list minus the intersect with names(Base) + // (all, any, get, in, is, only, which) and (do, else, for, let, where, while, with) + // ideally we'd just filter the original list but it's not available as a variable + lunr.stopWordFilter = lunr.generateStopWordFilter([ + 'a', + 'able', + 'about', + 'across', + 'after', + 'almost', + 'also', + 'am', + 'among', + 'an', + 'and', + 'are', + 'as', + 'at', + 'be', + 'because', + 'been', + 'but', + 'by', + 'can', + 'cannot', + 'could', + 'dear', + 'did', + 'does', + 'either', + 'ever', + 'every', + 'from', + 'got', + 'had', + 'has', + 'have', + 'he', + 'her', + 'hers', + 'him', + 'his', + 'how', + 'however', + 'i', + 'if', + 'into', + 'it', + 'its', + 'just', + 'least', + 'like', + 'likely', + 'may', + 'me', + 'might', + 'most', + 'must', + 'my', + 'neither', + 'no', + 'nor', + 'not', + 'of', + 'off', + 'often', + 'on', + 'or', + 'other', + 'our', + 'own', + 'rather', + 'said', + 'say', + 'says', + 'she', + 'should', + 'since', + 'so', + 'some', + 'than', + 'that', + 'the', + 'their', + 'them', + 'then', + 'there', + 'these', + 'they', + 'this', + 'tis', + 'to', + 'too', + 'twas', + 'us', + 'wants', + 'was', + 'we', + 'were', + 'what', + 'when', + 'who', + 'whom', + 'why', + 'will', + 'would', + 'yet', + 'you', + 'your' + ]) + + // add . as a separator, because otherwise "title": "Documenter.Anchors.add!" + // would not find anything if searching for "add!", only for the entire qualification + lunr.tokenizer.separator = /[\s\-\.]+/ + + // custom trimmer that doesn't strip @ and !, which are used in julia macro and function names + lunr.trimmer = function (token) { + return token.update(function (s) { + return s.replace(/^[^a-zA-Z0-9@!]+/, '').replace(/[^a-zA-Z0-9@!]+$/, '') + }) + } + + lunr.Pipeline.registerFunction(lunr.stopWordFilter, 'juliaStopWordFilter') + lunr.Pipeline.registerFunction(lunr.trimmer, 'juliaTrimmer') + + var index = lunr(function () { + this.ref('location') + this.field('title',{boost: 100}) + this.field('text') + documenterSearchIndex['docs'].forEach(function(e) { + this.add(e) + }, this) + }) + var store = {} + + documenterSearchIndex['docs'].forEach(function(e) { + store[e.location] = {title: e.title, category: e.category, page: e.page} + }) + + $(function(){ + searchresults = $('#documenter-search-results'); + searchinfo = $('#documenter-search-info'); + searchbox = $('#documenter-search-query'); + searchform = $('.docs-search'); + sidebar = $('.docs-sidebar'); + function update_search(querystring) { + tokens = lunr.tokenizer(querystring) + results = index.query(function (q) { + tokens.forEach(function (t) { + q.term(t.toString(), { + fields: ["title"], + boost: 100, + usePipeline: true, + editDistance: 0, + wildcard: lunr.Query.wildcard.NONE + }) + q.term(t.toString(), { + fields: ["title"], + boost: 10, + usePipeline: true, + editDistance: 2, + wildcard: lunr.Query.wildcard.NONE + }) + q.term(t.toString(), { + fields: ["text"], + boost: 1, + usePipeline: true, + editDistance: 0, + wildcard: lunr.Query.wildcard.NONE + }) + }) + }) + searchinfo.text("Number of results: " + results.length) + searchresults.empty() + results.forEach(function(result) { + data = store[result.ref] + link = $(''+data.title+'') + link.attr('href', documenterBaseURL+'/'+result.ref) + if (data.category != "page"){ + cat = $('('+data.category+', '+data.page+')') + } else { + cat = $('('+data.category+')') + } + li = $('
  • ').append(link).append(" ").append(cat) + searchresults.append(li) + }) + } + + function update_search_box() { + querystring = searchbox.val() + update_search(querystring) + } + + searchbox.keyup(_.debounce(update_search_box, 250)) + searchbox.change(update_search_box) + + // Disable enter-key form submission for the searchbox on the search page + // and just re-run search rather than refresh the whole page. + searchform.keypress( + function(event){ + if (event.which == '13') { + if (sidebar.hasClass('visible')) { + sidebar.removeClass('visible'); + } + update_search_box(); + event.preventDefault(); + } + } + ); + + search_query_uri = parseUri(window.location).queryKey["q"] + if(search_query_uri !== undefined) { + search_query = decodeURIComponent(search_query_uri.replace(/\+/g, '%20')) + searchbox.val(search_query) + } + update_search_box(); + }) +}) + +}) diff --git a/previews/PR26/assets/themes/documenter-dark.css b/previews/PR26/assets/themes/documenter-dark.css new file mode 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kbd.button.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-dark.is-outlined.is-loading:focus::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-dark.is-outlined.is-loading.is-focused::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #f5f5f5 #f5f5f5 !important}html.theme--documenter-dark .button.is-dark.is-outlined[disabled],html.theme--documenter-dark .content kbd.button.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark.is-outlined,fieldset[disabled] html.theme--documenter-dark .content kbd.button.is-outlined{background-color:transparent;border-color:#363636;box-shadow:none;color:#363636}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:#f5f5f5;color:#f5f5f5}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined:hover,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined:focus,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-focused,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-focused{background-color:#f5f5f5;color:#363636}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading.is-focused::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #363636 #363636 !important}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined[disabled],html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined,fieldset[disabled] html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:#f5f5f5;box-shadow:none;color:#f5f5f5}html.theme--documenter-dark .button.is-primary,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink{background-color:#4eb5de;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-primary:hover,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink:hover,html.theme--documenter-dark .button.is-primary.is-hovered,html.theme--documenter-dark .docstring>section>a.button.is-hovered.docs-sourcelink{background-color:#43b1dc;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-primary:focus,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink:focus,html.theme--documenter-dark .button.is-primary.is-focused,html.theme--documenter-dark .docstring>section>a.button.is-focused.docs-sourcelink{border-color:transparent;color:#fff}html.theme--documenter-dark 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!important}html.theme--documenter-dark .button.is-success.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-success.is-outlined{background-color:transparent;border-color:#259a12;box-shadow:none;color:#259a12}html.theme--documenter-dark .button.is-success.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-success.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-success.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-success.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-success.is-inverted.is-outlined.is-focused{background-color:#fff;color:#259a12}html.theme--documenter-dark .button.is-success.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-success.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark 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.button.is-warning.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#f4c72f}html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #f4c72f #f4c72f !important}html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-danger{background-color:#cb3c33;border-color:transparent;color:#fff}html.theme--documenter-dark 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.button.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.button{border-radius:2px;font-size:.75rem}html.theme--documenter-dark .button.is-normal{font-size:1rem}html.theme--documenter-dark .button.is-medium{font-size:1.25rem}html.theme--documenter-dark .button.is-large{font-size:1.5rem}html.theme--documenter-dark .button[disabled],fieldset[disabled] html.theme--documenter-dark .button{background-color:#fff;border-color:#dbdbdb;box-shadow:none;opacity:.5}html.theme--documenter-dark .button.is-fullwidth{display:flex;width:100%}html.theme--documenter-dark .button.is-loading{color:transparent !important;pointer-events:none}html.theme--documenter-dark .button.is-loading::after{position:absolute;left:calc(50% - (1em / 2));top:calc(50% - (1em / 2));position:absolute !important}html.theme--documenter-dark .button.is-static{background-color:#f5f5f5;border-color:#dbdbdb;color:#7a7a7a;box-shadow:none;pointer-events:none}html.theme--documenter-dark 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.button:active:hover,html.theme--documenter-dark .buttons.has-addons .button.is-active:hover,html.theme--documenter-dark .buttons.has-addons .button.is-selected:hover{z-index:4}html.theme--documenter-dark .buttons.has-addons .button.is-expanded{flex-grow:1;flex-shrink:1}html.theme--documenter-dark .buttons.is-centered{justify-content:center}html.theme--documenter-dark .buttons.is-centered:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}html.theme--documenter-dark .buttons.is-right{justify-content:flex-end}html.theme--documenter-dark .buttons.is-right:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}html.theme--documenter-dark .container{flex-grow:1;margin:0 auto;position:relative;width:auto}@media screen and (min-width: 1056px){html.theme--documenter-dark .container{max-width:992px}html.theme--documenter-dark .container.is-fluid{margin-left:32px;margin-right:32px;max-width:none}}@media screen and (max-width: 1215px){html.theme--documenter-dark .container.is-widescreen{max-width:1152px}}@media screen and (max-width: 1407px){html.theme--documenter-dark .container.is-fullhd{max-width:1344px}}@media screen and (min-width: 1216px){html.theme--documenter-dark .container{max-width:1152px}}@media screen and (min-width: 1408px){html.theme--documenter-dark .container{max-width:1344px}}html.theme--documenter-dark .content li+li{margin-top:0.25em}html.theme--documenter-dark .content p:not(:last-child),html.theme--documenter-dark .content dl:not(:last-child),html.theme--documenter-dark .content ol:not(:last-child),html.theme--documenter-dark .content ul:not(:last-child),html.theme--documenter-dark .content blockquote:not(:last-child),html.theme--documenter-dark .content pre:not(:last-child),html.theme--documenter-dark .content table:not(:last-child){margin-bottom:1em}html.theme--documenter-dark .content h1,html.theme--documenter-dark .content h2,html.theme--documenter-dark .content h3,html.theme--documenter-dark .content h4,html.theme--documenter-dark .content h5,html.theme--documenter-dark .content h6{color:#fff;font-weight:600;line-height:1.125}html.theme--documenter-dark .content h1{font-size:2em;margin-bottom:0.5em}html.theme--documenter-dark .content h1:not(:first-child){margin-top:1em}html.theme--documenter-dark .content h2{font-size:1.75em;margin-bottom:0.5714em}html.theme--documenter-dark .content h2:not(:first-child){margin-top:1.1428em}html.theme--documenter-dark .content h3{font-size:1.5em;margin-bottom:0.6666em}html.theme--documenter-dark .content h3:not(:first-child){margin-top:1.3333em}html.theme--documenter-dark .content h4{font-size:1.25em;margin-bottom:0.8em}html.theme--documenter-dark .content h5{font-size:1.125em;margin-bottom:0.8888em}html.theme--documenter-dark .content h6{font-size:1em;margin-bottom:1em}html.theme--documenter-dark .content blockquote{background-color:#f5f5f5;border-left:5px solid #666;padding:1.25em 1.5em}html.theme--documenter-dark .content ol{list-style-position:outside;margin-left:2em;margin-top:1em}html.theme--documenter-dark .content ol:not([type]){list-style-type:decimal}html.theme--documenter-dark .content ol.is-lower-alpha:not([type]){list-style-type:lower-alpha}html.theme--documenter-dark .content ol.is-lower-roman:not([type]){list-style-type:lower-roman}html.theme--documenter-dark .content ol.is-upper-alpha:not([type]){list-style-type:upper-alpha}html.theme--documenter-dark .content ol.is-upper-roman:not([type]){list-style-type:upper-roman}html.theme--documenter-dark .content ul{list-style:disc outside;margin-left:2em;margin-top:1em}html.theme--documenter-dark .content ul ul{list-style-type:circle;margin-top:0.5em}html.theme--documenter-dark .content ul ul ul{list-style-type:square}html.theme--documenter-dark .content dd{margin-left:2em}html.theme--documenter-dark .content figure{margin-left:2em;margin-right:2em;text-align:center}html.theme--documenter-dark .content 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.pagination.is-rounded .pagination-previous,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.pagination .pagination-previous,html.theme--documenter-dark .pagination.is-rounded .pagination-next,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.pagination .pagination-next{padding-left:1em;padding-right:1em;border-radius:290486px}html.theme--documenter-dark .pagination.is-rounded .pagination-link,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.pagination .pagination-link{border-radius:290486px}html.theme--documenter-dark .pagination,html.theme--documenter-dark .pagination-list{align-items:center;display:flex;justify-content:center;text-align:center}html.theme--documenter-dark .pagination-previous,html.theme--documenter-dark .pagination-next,html.theme--documenter-dark .pagination-link,html.theme--documenter-dark .pagination-ellipsis{font-size:1em;justify-content:center;margin:.25rem;padding-left:.5em;padding-right:.5em;text-align:center}html.theme--documenter-dark .pagination-previous,html.theme--documenter-dark .pagination-next,html.theme--documenter-dark .pagination-link{border-color:#dbdbdb;color:#363636;min-width:2.25em}html.theme--documenter-dark .pagination-previous:hover,html.theme--documenter-dark .pagination-next:hover,html.theme--documenter-dark .pagination-link:hover{border-color:#b5b5b5;color:#8ad9f3}html.theme--documenter-dark .pagination-previous:focus,html.theme--documenter-dark .pagination-next:focus,html.theme--documenter-dark .pagination-link:focus{border-color:#3c5dcd}html.theme--documenter-dark .pagination-previous:active,html.theme--documenter-dark .pagination-next:active,html.theme--documenter-dark .pagination-link:active{box-shadow:inset 0 1px 2px rgba(10,10,10,0.2)}html.theme--documenter-dark .pagination-previous[disabled],html.theme--documenter-dark 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form.docs-search>input::placeholder{color:#fff}code.language-julia-repl>span.hljs-meta{color:#00205b;font-weight:bolder}span.hljs-built_in{color:#00205b}span.hljs-literal{color:#819b1c}span.hljs-number{color:#b52452} diff --git a/previews/PR26/assets/themeswap.js b/previews/PR26/assets/themeswap.js new file mode 100644 index 0000000..c58e993 --- /dev/null +++ b/previews/PR26/assets/themeswap.js @@ -0,0 +1,66 @@ +// Small function to quickly swap out themes. Gets put into the tag.. +function set_theme_from_local_storage() { + // Intialize the theme to null, which means default + var theme = null; + // If the browser supports the localstorage and is not disabled then try to get the + // documenter theme + if(window.localStorage != null) { + // Get the user-picked theme from localStorage. May be `null`, which means the default + // theme. + theme = window.localStorage.getItem("documenter-theme"); + } + // Check if the browser supports user color preference + var darkPreference = false; + // Check if the users preference is for dark color scheme + if(window.matchMedia('(prefers-color-scheme: dark)').matches === true) { + darkPreference = true; + } + // Initialize a few variables for the loop: + // + // - active: will contain the index of the theme that should be active. Note that there + // is no guarantee that localStorage contains sane values. If `active` stays `null` + // we either could not find the theme or it is the default (primary) theme anyway. + // Either way, we then need to stick to the primary theme. + // + // - disabled: style sheets that should be disabled (i.e. all the theme style sheets + // that are not the currently active theme) + var active = null; var disabled = []; var darkTheme = null; + for (var i = 0; i < document.styleSheets.length; i++) { + var ss = document.styleSheets[i]; + // The tag of each style sheet is expected to have a data-theme-name attribute + // which must contain the name of the theme. The names in localStorage much match this. + var themename = ss.ownerNode.getAttribute("data-theme-name"); + // attribute not set => non-theme stylesheet => ignore + if(themename === null) continue; + // To distinguish the default (primary) theme, it needs to have the data-theme-primary + // attribute set. + var isprimary = (ss.ownerNode.getAttribute("data-theme-primary") !== null); + // Check if the theme is primary dark theme + var isDarkTheme = (ss.ownerNode.getAttribute("data-theme-primary-dark") !== null); + // If ss is for dark theme then set the value of darkTheme to the name of the theme + if(isDarkTheme) darkTheme = themename; + // If we find a matching theme (and it's not the default), we'll set active to non-null + if(themename === theme) active = i; + // Store the style sheets of inactive themes so that we could disable them + if(themename !== theme) disabled.push(ss); + } + if(active !== null) { + // If we did find an active theme, we'll (1) add the theme--$(theme) class to + document.getElementsByTagName('html')[0].className = "theme--" + theme; + // and (2) disable all the other theme stylesheets + disabled.forEach(function(ss){ + ss.disabled = true; + }); + } + else if(darkTheme !== null && darkPreference === true) { + // If we did find an active theme, we'll (1) add the theme--$(theme) class to + document.getElementsByTagName('html')[0].className = "theme--" + darkTheme; + // and (2) disable all the other theme stylesheets + disabled.forEach(function(ss){ + if (ss.ownerNode.getAttribute("data-theme-name") !== darkTheme) { + ss.disabled = true; + } + }); + } +} +set_theme_from_local_storage(); diff --git a/previews/PR26/assets/warner.js b/previews/PR26/assets/warner.js new file mode 100644 index 0000000..5531c88 --- /dev/null +++ b/previews/PR26/assets/warner.js @@ -0,0 +1,49 @@ +function maybeAddWarning () { + // DOCUMENTER_NEWEST is defined in versions.js, DOCUMENTER_CURRENT_VERSION and DOCUMENTER_STABLE + // in siteinfo.js. + // If either of these are undefined something went horribly wrong, so we abort. + if ( + window.DOCUMENTER_NEWEST === undefined || + window.DOCUMENTER_CURRENT_VERSION === undefined || + window.DOCUMENTER_STABLE === undefined + ) { + return + }; + + // Current version is not a version number, so we can't tell if it's the newest version. Abort. + if (!/v(\d+\.)*\d+/.test(window.DOCUMENTER_CURRENT_VERSION)) { + return + }; + + // Current version is newest version, so no need to add a warning. + if (window.DOCUMENTER_NEWEST === window.DOCUMENTER_CURRENT_VERSION) { + return + }; + + // Add a noindex meta tag (unless one exists) so that search engines don't index this version of the docs. + if (document.body.querySelector('meta[name="robots"]') === null) { + const meta = document.createElement('meta'); + meta.name = 'robots'; + meta.content = 'noindex'; + + document.getElementsByTagName('head')[0].appendChild(meta); + }; + + const div = document.createElement('div'); + div.classList.add('outdated-warning-overlay'); + const closer = document.createElement('button'); + closer.classList.add('outdated-warning-closer', 'delete'); + closer.addEventListener('click', function () { + document.body.removeChild(div); + }); + const href = window.documenterBaseURL + '/../' + window.DOCUMENTER_STABLE; + div.innerHTML = 'This documentation is not for the latest stable release, but for either the development version or an older release.
    Click here to go to the documentation for the latest stable release.'; + div.appendChild(closer); + document.body.appendChild(div); +}; + +if (document.readyState === 'loading') { + document.addEventListener('DOMContentLoaded', maybeAddWarning); +} else { + maybeAddWarning(); +}; diff --git a/previews/PR26/devices/a100_sxm2/index.html b/previews/PR26/devices/a100_sxm2/index.html new file mode 100644 index 0000000..c202d8a --- /dev/null +++ b/previews/PR26/devices/a100_sxm2/index.html @@ -0,0 +1,189 @@ + +A100 SXM2 · GPUInspector.jl
    Edit on GitHub

    NVIDIA A100-SXM4-40GB (sm_80, 39.586 GiB)

    Benchmarks were run on a DGX-A100 at PC2.

    For comparison: Datasheet by NVIDIA.

    Peakflops

    CUDA cores

    julia> theoretical_peakflops_gpu(; dtype=Float32, tensorcores=false);
+Theoretical Peakflops (TFLOP/s):
+ ├ tensorcores: false
+ ├ dtype: Float32
+ └ max: 19.5
+
+julia> theoretical_peakflops_gpu(; dtype=Float64, tensorcores=false);
+Theoretical Peakflops (TFLOP/s):
+ ├ tensorcores: false
+ ├ dtype: Float64
+ └ max: 9.7
    julia> peakflops_gpu(; dtype=Float32, tensorcores=false);
+Peakflops (TFLOP/s):
+ ├ tensorcores: false
+ ├ dtype: Float32
+ └ max: 19.1
+
+julia> peakflops_gpu(; dtype=Float64, tensorcores=false);
+Peakflops (TFLOP/s):
+ ├ tensorcores: false
+ ├ dtype: Float64
+ └ max: 9.6
+
+julia> peakflops_gpu(; dtype=Float16, tensorcores=false);
+Peakflops (TFLOP/s):
+ ├ tensorcores: false
+ ├ dtype: Float16
+ └ max: 12.8

    Tensor cores

    julia> theoretical_peakflops_gpu(; dtype=Int8, tensorcores=true);
+Theoretical Peakflops (TOP/s):
+ ├ tensorcores: true
+ ├ dtype: Int8
+ └ max: 623.7
+
+julia> theoretical_peakflops_gpu(; dtype=Float16, tensorcores=true);
+Theoretical Peakflops (TFLOP/s):
+ ├ tensorcores: true
+ ├ dtype: Float16
+ └ max: 311.9
+
+julia> theoretical_peakflops_gpu(; dtype=Float32, tensorcores=true);
+Theoretical Peakflops (TFLOP/s):
+ ├ tensorcores: true
+ ├ dtype: Float32
+ └ max: 155.9
    julia> peakflops_gpu(; dtype=Int8, tensorcores=true); # as of writing, only works with CUDA.jl#master
+Peakflops (TOP/s):
+ ├ tensorcores: true
+ ├ dtype: Int8
+ └ max: 620.11
+
+julia> peakflops_gpu(; dtype=Float16, tensorcores=true);
+Peakflops (TFLOP/s):
+ ├ tensorcores: true
+ ├ dtype: Float16
+ └ max: 311.2
+
+julia> peakflops_gpu(; dtype=:TensorFloat32, tensorcores=true); # as of writing, only works with Julia >= 1.8.0 and CUDA.jl PR 1419
+Peakflops (TFLOP/s):
+ ├ tensorcores: true
+ ├ dtype: TensorFloat32
+ └ max: 155.55

    Memory bandwidth

    julia> theoretical_memory_bandwidth();
+Theoretical Maximal Memory Bandwidth (GiB/s):
+ └ max: 1448.4
+
+julia> memory_bandwidth();
+Memory Bandwidth (GiB/s):
+ └ max: 1220.7
+
+julia> GiB(1220.7) |> change_base
+~1310.72 GB
+
+julia> memory_bandwidth_saxpy();
+Memory Bandwidth (GiB/s):
+ └ max: 1192.09

    Host-to-device bandwidth

    julia> host2device_bandwidth()
+Host <-> Device Bandwidth (GiB/s):
+ └ max: 11.84
+
+Host (pinned) <-> Device Bandwidth (GiB/s):
+ └ max: 24.33

    Peer-to-peer bandwidth

    julia> p2p_bandwidth();
+Bandwidth (GiB/s):
+ ├ max: 247.32
+ ├ min: 173.5
+ ├ avg: 229.63
+ └ std_dev: 31.67
+
+julia> p2p_bandwidth_all()
+8×8 Matrix{Union{Nothing, Float64}}:
+    nothing  245.706     241.075     244.467     246.434     242.229     245.085     245.033
+ 239.046        nothing  241.776     243.853     241.626     245.136     244.467     240.379
+ 246.957     242.633        nothing  242.937     245.291     248.114     239.193     242.684
+ 244.724     241.375     244.211        nothing  245.861     238.117     245.085     242.28
+ 241.576     246.329     242.582     245.602        nothing  246.59      240.677     243.343
+ 247.114     240.18      245.965     244.006     236.616        nothing  242.28      244.673
+ 243.802     242.028     248.326     239.933     244.365     245.033        nothing  245.498
+ 245.136     246.904     239.488     243.343     244.057     240.627     243.445        nothing

    GPU information

    julia> CUDA.versioninfo()
+CUDA toolkit 11.5, local installation
+NVIDIA driver 495.29.5, for CUDA 11.5
+CUDA driver 11.5
+
+Libraries: 
+- CUBLAS: 11.7.3
+- CURAND: 10.2.6
+- CUFFT: 10.6.0
+- CUSOLVER: 11.2.1
+- CUSPARSE: 11.7.0
+- CUPTI: 16.0.0
+- NVML: 11.0.0+495.29.5
+- CUDNN: missing
+- CUTENSOR: missing
+
+Toolchain:
+- Julia: 1.7.1
+- LLVM: 12.0.1
+- PTX ISA support: 3.2, 4.0, 4.1, 4.2, 4.3, 5.0, 6.0, 6.1, 6.3, 6.4, 6.5, 7.0
+- Device capability support: sm_35, sm_37, sm_50, sm_52, sm_53, sm_60, sm_61, sm_62, sm_70, sm_72, sm_75, sm_80
+
+Environment:
+- JULIA_CUDA_USE_BINARYBUILDER: false
+
+8 devices:
+  0: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)
+  1: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)
+  2: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)
+  3: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)
+  4: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)
+  5: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)
+  6: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)
+  7: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)
+
+julia> gpuinfo()
+Device: NVIDIA A100-SXM4-40GB (CuDevice(0))
+Total amount of global memory: 42.5 GB
+Number of CUDA cores: 6912
+Number of multiprocessors: 108 (64 CUDA cores each)
+GPU max. clock rate: 1410 Mhz
+Memory clock rate: 1215 Mhz
+Memory bus width: 5120-bit
+L2 cache size: 41.9 MB
+Max. texture dimension sizes (1D): 131072
+Max. texture dimension sizes (2D): 131072, 65536
+Max. texture dimension sizes (3D): 16384, 16384, 16384
+Max. layered 1D texture size: 32768 (2048 layers)
+Max. layered 2D texture size: 32768, 32768 (2048 layers)
+Total amount of constant memory: 65.5 kB
+Total amount of shared memory per block: 49.2 kB
+Total number of registers available per block: 65536
+Warp size: 32
+Max. number of threads per multiprocessor: 2048
+Max. number of threads per block: 1024
+Max. dimension size of a thread block (x,y,z): 1024, 1024, 64
+Max. dimension size of a grid size (x,y,z): 2147483647, 65535, 65535
+Texture alignment: 512.0 B
+Maximum memory pitch: 2.1 GB
+Concurrent copy and kernel execution: Yes with 3 copy engine(s)
+Run time limit on kernels: No
+Integrated GPU sharing host memory: No
+Support host page-locked memory mapping: Yes
+Concurrent kernel execution: Yes
+Alignment requirement for surfaces: Yes
+Device has ECC support: Yes
+Device supports Unified Addressing (UVA): Yes
+Device supports managed memory: Yes
+Device supports compute preemption: Yes
+Supports cooperative kernel launch: Yes
+Supports multi-device co-op kernel launch: Yes
+Device PCI domain ID / bus ID / device ID: 0 / 7 / 0
+Compute mode: Default (multiple host threads can use ::cudaSetDevice() with device simultaneously)
+
+julia> gpuinfo_p2p_access()
+P2P Access Supported:
+8×8 Matrix{Bool}:
+ 0  1  1  1  1  1  1  1
+ 1  0  1  1  1  1  1  1
+ 1  1  0  1  1  1  1  1
+ 1  1  1  0  1  1  1  1
+ 1  1  1  1  0  1  1  1
+ 1  1  1  1  1  0  1  1
+ 1  1  1  1  1  1  0  1
+ 1  1  1  1  1  1  1  0
+
+P2P Atomic Supported:
+8×8 Matrix{Bool}:
+ 0  1  1  1  1  1  1  1
+ 1  0  1  1  1  1  1  1
+ 1  1  0  1  1  1  1  1
+ 1  1  1  0  1  1  1  1
+ 1  1  1  1  0  1  1  1
+ 1  1  1  1  1  0  1  1
+ 1  1  1  1  1  1  0  1
+ 1  1  1  1  1  1  1  0
    diff --git a/previews/PR26/devices/geforce_gtx_1650/index.html b/previews/PR26/devices/geforce_gtx_1650/index.html new file mode 100644 index 0000000..ee7f7b0 --- /dev/null +++ b/previews/PR26/devices/geforce_gtx_1650/index.html @@ -0,0 +1,92 @@ + +GeForce GTX 1650 · GPUInspector.jl
    Edit on GitHub

    NVIDIA GeForce GTX 1650 (sm_75, 3.787 GiB)

    Some information by NVIDIA is available here. Some external numbers may be found here.

    Peakflops

    Theoretical

    julia> theoretical_peakflops_gpu(; dtype=Float32);
+Theoretical Peakflops (TFLOP/s):
+ └ max: 2.9
+
+julia> theoretical_peakflops_gpu(; dtype=Float64);
+Theoretical Peakflops (TFLOP/s):
+ └ max: 1.5

    Empirical

    julia> peakflops_gpu_fmas(; dtype=Float32); # too large?
+Peakflops (TFLOP/s):
+ └ max: 3.41
+
+julia> peakflops_gpu_fmas(; dtype=Float64); # too low?
+Peakflops (TFLOP/s):
+ └ max: 0.11

    Memory bandwidth

    julia> memory_bandwidth();
+Memory Bandwidth (GiB/s):
+ └ max: 159.33
+
+julia> GiB(159.33) |> change_base
+~171.08 GB
    julia> host2device_bandwidth()
+Memsize: 512.000 MiB
+GPU: CuDevice(0) - NVIDIA GeForce GTX 1650
+
+Host <-> Device Bandwidth (GiB/s):
+ └ max: 12.06
+
+Host (pinned) <-> Device Bandwidth (GiB/s):
+ └ max: 12.05
+
+Device <-> Device (same device) Bandwidth (GiB/s):
+ └ max: 153.0

    GPU information

    julia> CUDA.versioninfo()
+CUDA toolkit 11.6, artifact installation
+NVIDIA driver 470.86.0, for CUDA 11.4
+CUDA driver 11.4
+
+Libraries: 
+- CUBLAS: 11.8.1
+- CURAND: 10.2.9
+- CUFFT: 10.7.0
+- CUSOLVER: 11.3.2
+- CUSPARSE: 11.7.1
+- CUPTI: 16.0.0
+- NVML: 11.0.0+470.86
+- CUDNN: 8.30.2 (for CUDA 11.5.0)
+- CUTENSOR: 1.4.0 (for CUDA 11.5.0)
+
+Toolchain:
+- Julia: 1.7.2
+- LLVM: 12.0.1
+- PTX ISA support: 3.2, 4.0, 4.1, 4.2, 4.3, 5.0, 6.0, 6.1, 6.3, 6.4, 6.5, 7.0
+- Device capability support: sm_35, sm_37, sm_50, sm_52, sm_53, sm_60, sm_61, sm_62, sm_70, sm_72, sm_75, sm_80
+
+1 device:
+  0: NVIDIA GeForce GTX 1650 (sm_75, 2.240 GiB / 3.787 GiB available)
+
+julia> gpuinfo()
+Device: NVIDIA GeForce GTX 1650 (CuDevice(0))
+Total amount of global memory: 3.787 GiB
+Number of CUDA cores: 896
+Number of multiprocessors: 14 (64 CUDA cores each)
+GPU max. clock rate: 1620 MHz
+Memory clock rate: 6001 MHz
+Memory bus width: 128-bit
+L2 cache size: 1024.000 KiB
+Max. texture dimension sizes (1D): 131072
+Max. texture dimension sizes (2D): 131072, 65536
+Max. texture dimension sizes (3D): 16384, 16384, 16384
+Max. layered 1D texture size: 32768 (2048 layers)
+Max. layered 2D texture size: 32768, 32768 (2048 layers)
+Total amount of constant memory: 64.000 KiB
+Total amount of shared memory per block: 48.000 KiB
+Total number of registers available per block: 65536
+Warp size: 32
+Max. number of threads per multiprocessor: 1024
+Max. number of threads per block: 1024 
+Max. dimension size of a thread block (x,y,z): 1024, 1024, 64
+Max. dimension size of a grid size (x,y,z): 2147483647, 65535, 65535
+Texture alignment: 512 bytes
+Maximum memory pitch: 2.000 GiB
+Concurrent copy and kernel execution: Yes with 3 copy engine(s)
+Run time limit on kernels: Yes
+Integrated GPU sharing host memory: No 
+Support host page-locked memory mapping: Yes
+Concurrent kernel execution: Yes
+Alignment requirement for surfaces: Yes
+Device has ECC support: No
+Device supports Unified Addressing (UVA): Yes
+Device supports managed memory: Yes
+Device supports compute preemption: Yes
+Supports cooperative kernel launch: Yes
+Supports multi-device co-op kernel launch: Yes
+Device PCI domain ID / bus ID / device ID: 0 / 1 / 0
+Compute mode: Default (multiple host threads can use ::cudaSetDevice() with device simultaneously)
    diff --git a/previews/PR26/devices/v100_sxm2/index.html b/previews/PR26/devices/v100_sxm2/index.html new file mode 100644 index 0000000..b2e55b5 --- /dev/null +++ b/previews/PR26/devices/v100_sxm2/index.html @@ -0,0 +1,169 @@ + +V100 SXM2 · GPUInspector.jl
    Edit on GitHub

    Tesla V100-SXM2-32GB (sm_70, 32.00 GiB)

    Benchmarks were run on the GPU cluster of the physics faculty of University of Bielefeld.

    For comparison: Datasheet by NVIDIA.

    Peakflops

    Theoretical

    julia> theoretical_peakflops_gpu(; dtype=Float32, tensorcores=false);
+Theoretical Peakflops (TFLOP/s):
+ ├ tensorcores: false
+ ├ dtype: Float32
+ └ max: 15.7
+
+julia> theoretical_peakflops_gpu(; dtype=Float64, tensorcores=false);
+Theoretical Peakflops (TFLOP/s):
+ ├ tensorcores: false
+ ├ dtype: Float64
+ └ max: 7.8
+
+julia> theoretical_peakflops_gpu(; dtype=Float16, tensorcores=true);
+Theoretical Peakflops (TFLOP/s):
+ ├ tensorcores: true
+ ├ dtype: Float16
+ └ max: 125.3

    Empirical

    julia> peakflops_gpu(; dtype=Float32, tensorcores=false);
+Peakflops (TFLOP/s):
+ ├ tensorcores: false
+ ├ dtype: Float32
+ └ max: 15.5
+
+julia> peakflops_gpu(; dtype=Float64, tensorcores=false);
+Peakflops (TFLOP/s):
+ ├ tensorcores: false
+ ├ dtype: Float64
+ └ max: 7.7
+
+julia> peakflops_gpu(; dtype=Float16, tensorcores=true);
+Peakflops (TFLOP/s):
+ ├ tensorcores: true
+ ├ dtype: Float16
+ └ max: 116.4

    Memory bandwidth

    julia> theoretical_memory_bandwidth();
+Theoretical Maximal Memory Bandwidth (GiB/s):
+ └ max: 836.4
+
+julia> memory_bandwidth();
+Memory Bandwidth (GiB/s):
+ └ max: 722.31
+
+julia> GiB(722.31) |> change_base
+~775.57 GB
+
+julia> memory_bandwidth(GiB(1.4)) |> GiB |> change_base
+Memory Bandwidth (GiB/s):
+ └ max: 740.02
+~794.59 GB
+
+julia> memory_bandwidth_saxpy(; size=2^20*200) |> GiB |> change_base
+Memory Bandwidth (GiB/s):
+ └ max: 754.39
+~810.02 GB

    Host-to-device bandwidth

    julia> host2device_bandwidth()
+Host <-> Device Bandwidth (GiB/s):
+ └ max: 4.46
+
+Host (pinned) <-> Device Bandwidth (GiB/s):
+ └ max: 12.14

    Peer-to-peer bandwidth

    julia> p2p_bandwidth();
+Bandwidth (GiB/s):
+ ├ max: 22.46
+ ├ min: 0.21
+ ├ avg: 17.99
+ └ std_dev: 9.94
+
+julia> p2p_bandwidth_all()
+8_8 Matrix{Union{Nothing, Float64}}:
+   nothing  22.5406    45.0071    22.5367    44.9879     8.11161    8.0912     8.09942
+ 22.4844      nothing  22.5393    44.9706     8.11274   45.0036     8.08732    8.10026
+ 44.7958    22.5406      nothing  45.0158     8.10381    8.11014   22.5406     7.39972
+ 22.5301    45.0088    45.0001      nothing   7.428      8.10646    8.08204   22.5401
+ 44.9897     7.14454    7.14674    7.14906     nothing  22.5371    44.9723    22.538
+  6.63411   44.9966     7.14542    7.14467   22.5371      nothing  22.5362    44.9949
+  7.75585    7.75844   22.5375     8.10314   44.9966    22.5353      nothing  44.9984
+  7.75575    7.75911    8.10116   22.5371    22.5367    44.9966    44.9914      nothing

    GPU information

    julia> CUDA.versioninfo()
+CUDA toolkit 11.6, local installation
+NVIDIA driver 510.47.3, for CUDA 11.6
+CUDA driver 11.6
+
+Libraries: 
+- CUBLAS: 11.8.1
+- CURAND: 10.2.9
+- CUFFT: 10.7.1
+- CUSOLVER: 11.3.3
+- CUSPARSE: 11.7.2
+- CUPTI: 16.0.0
+- NVML: 11.0.0+510.47.3
+- CUDNN: missing
+- CUTENSOR: missing
+
+Toolchain:
+- Julia: 1.7.1
+- LLVM: 12.0.1
+- PTX ISA support: 3.2, 4.0, 4.1, 4.2, 4.3, 5.0, 6.0, 6.1, 6.3, 6.4, 6.5, 7.0
+- Device capability support: sm_35, sm_37, sm_50, sm_52, sm_53, sm_60, sm_61, sm_62, sm_70, sm_72, sm_75, sm_80
+
+Environment:
+- JULIA_CUDA_USE_BINARYBUILDER: false
+
+8 devices:
+  0: Tesla V100-SXM2-32GB (sm_70, 31.449 GiB / 32.000 GiB available)
+  1: Tesla V100-SXM2-32GB (sm_70, 31.745 GiB / 32.000 GiB available)
+  2: Tesla V100-SXM2-32GB (sm_70, 31.745 GiB / 32.000 GiB available)
+  3: Tesla V100-SXM2-32GB (sm_70, 31.745 GiB / 32.000 GiB available)
+  4: Tesla V100-SXM2-32GB (sm_70, 31.745 GiB / 32.000 GiB available)
+  5: Tesla V100-SXM2-32GB (sm_70, 31.745 GiB / 32.000 GiB available)
+  6: Tesla V100-SXM2-32GB (sm_70, 31.745 GiB / 32.000 GiB available)
+  7: Tesla V100-SXM2-32GB (sm_70, 31.745 GiB / 32.000 GiB available)
+
+julia> gpuinfo()
+Device: Tesla V100-SXM2-32GB (CuDevice(0))
+Total amount of global memory: 31.749 GiB
+Number of CUDA cores: 5120
+Number of multiprocessors: 80 (64 CUDA cores each)
+GPU max. clock rate: 1530 MHz
+Memory clock rate: 877 MHz
+Memory bus width: 4096-bit
+L2 cache size: 6.000 MiB
+Max. texture dimension sizes (1D): 131072
+Max. texture dimension sizes (2D): 131072, 65536
+Max. texture dimension sizes (3D): 16384, 16384, 16384
+Max. layered 1D texture size: 32768 (2048 layers)
+Max. layered 2D texture size: 32768, 32768 (2048 layers)
+Total amount of constant memory: 64.000 KiB
+Total amount of shared memory per block: 48.000 KiB
+Total number of registers available per block: 65536
+Warp size: 32
+Max. number of threads per multiprocessor: 2048
+Max. number of threads per block: 1024
+Max. dimension size of a thread block (x,y,z): 1024, 1024, 64
+Max. dimension size of a grid size (x,y,z): 2147483647, 65535, 65535
+Texture alignment: 512 bytes
+Maximum memory pitch: 2.000 GiB
+Concurrent copy and kernel execution: Yes with 6 copy engine(s)
+Run time limit on kernels: No
+Integrated GPU sharing host memory: No
+Support host page-locked memory mapping: Yes
+Concurrent kernel execution: Yes
+Alignment requirement for surfaces: Yes
+Device has ECC support: Yes
+Device supports Unified Addressing (UVA): Yes
+Device supports managed memory: Yes
+Device supports compute preemption: Yes
+Supports cooperative kernel launch: Yes
+Supports multi-device co-op kernel launch: Yes
+Device PCI domain ID / bus ID / device ID: 0 / 31 / 0
+Compute mode: Default (multiple host threads can use ::cudaSetDevice() with device simultaneously)
+
+julia> gpuinfo_p2p_access()
+P2P Access Supported:
+8_8 Matrix{Bool}:
+ 0  1  1  1  1  1  1  1
+ 1  0  1  1  1  1  1  1
+ 1  1  0  1  1  1  1  1
+ 1  1  1  0  1  1  1  1
+ 1  1  1  1  0  1  1  1
+ 1  1  1  1  1  0  1  1
+ 1  1  1  1  1  1  0  1
+ 1  1  1  1  1  1  1  0
+
+P2P Atomic Supported:
+8_8 Matrix{Bool}:
+ 0  1  1  1  1  0  0  0
+ 1  0  1  1  0  1  0  0
+ 1  1  0  1  0  0  1  0
+ 1  1  1  0  0  0  0  1
+ 1  0  0  0  0  1  1  1
+ 0  1  0  0  1  0  1  1
+ 0  0  1  0  1  1  0  1
+ 0  0  0  1  1  1  1  0
    diff --git a/previews/PR26/examples/data_bandwidth/index.html b/previews/PR26/examples/data_bandwidth/index.html new file mode 100644 index 0000000..478ea6b --- /dev/null +++ b/previews/PR26/examples/data_bandwidth/index.html @@ -0,0 +1,93 @@ + +Data Bandwidth · GPUInspector.jl
    Edit on GitHub

    Example: Data Bandwidth

    Note

    Test system: DGX with 8x A100 GPUs (+ NVSwitch)

    Memory Bandwidth

    You can benchmark the memory bandwidth of a GPU, i.e. how much data can be loaded within a second, with the function memory_bandwidth. Under the hood, this function transfers a certain amount of data (memcpy), times the operation, and computes the resulting memory bandwidth.

    julia> memory_bandwidth();
+Memory Bandwidth (GiB/s):
+ └ max: 1219.18

    Note that this is reasonably close to the expected theoretical maximal memory bandwidth of our A100 test device.

    julia> theoretical_memory_bandwidth();
+Theoretical Maximal Memory Bandwidth (GiB/s):
+ └ max: 1448.4

    The scaling (as a function of data size) can be assessed with memory_bandwidth_scaling.

    julia> memory_bandwidth_scaling()
+
+              ⠀⠀⠀⠀Peak: 1225.3 GiB/s (size = 1.0 GiB)⠀⠀⠀ 
+              ┌────────────────────────────────────────┐ 
+         2000 │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ 
+              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ 
+              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ 
+              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ 
+              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ 
+              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀│ 
+              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⠤⠔⠒⠉⠉⢸│ 
+   GiB/s      │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⠒⠉⠀⠀⠀⠀⠀⠀⢸│ 
+              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡎⠀⠀⠀⠀⠀⠀⠀⠀⢸│ 
+              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡜⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ 
+              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢰⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ 
+              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ 
+              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡎⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ 
+              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡠⠜⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ 
+            0 │⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⡠⠔⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ 
+              └────────────────────────────────────────┘ 
+              ⠀2⁰⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀2³⁰⠀ 
+              ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀data size⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀

    SAXPY

    Note that we also provide similar functions that are based on a SAXPY streaming kernel.

    julia> memory_bandwidth_saxpy();
+Memory Bandwidth (GiB/s):
+ └ max: 1192.09
+
+julia> memory_bandwidth_saxpy_scaling()
+
+              ⠀⠀Peak: 1275.34 GiB/s (size = 300.0 MiB)⠀⠀ 
+              ┌────────────────────────────────────────┐ 
+         1280 │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡀⠀⠀⠀⢀│ 
+              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⣀⠤⠤⠔⠒⠙⠊⠊⠉⢹│ 
+              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⠤⠤⠔⠒⠊⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ 
+              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⠔⠒⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ 
+              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡠⠔⠊⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ 
+              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡠⠔⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ 
+              │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡠⠊⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ 
+   GiB/s      │⠀⠀⠀⠀⠀⠀⠀⠀⠀⡠⠊⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ 
+              │⠀⠀⠀⠀⠀⠀⠀⡰⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ 
+              │⠀⠀⠀⠀⠀⠀⡜⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ 
+              │⠀⠀⠀⠀⢀⠎⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ 
+              │⠀⠀⠀⡠⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ 
+              │⠀⠀⡰⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ 
+              │⢀⠜⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ 
+         1190 │⠎⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ 
+              └────────────────────────────────────────┘ 
+              ⠀2²³⸱³²¹⁹⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀2²⁸⸱²²⁸⁸⠀ 
+              ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀vector length⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀

    Host-to-Device Bandwidth

    How much data can be transferred per second from the host memory to the GPU (and the other way around)?

    julia> host2device_bandwidth()
+Host <-> Device Bandwidth (GiB/s):
+ └ max: 11.79
+
+Host (pinned) <-> Device Bandwidth (GiB/s):
+ └ max: 24.33

    Peer-to-Peer Bandwidth

    Unidirectional

    julia> p2p_bandwidth();
+Memsize: 38.147 MiB
+
+Bandwidth (GiB/s):
+ ├ max: 247.32
+ ├ min: 173.5
+ ├ avg: 229.63
+ └ std_dev: 31.67
+
+julia> p2p_bandwidth_all()
+8×8 Matrix{Union{Nothing, Float64}}:
+    nothing  245.706     241.075     244.467     246.434     242.229     245.085     245.033
+ 239.046        nothing  241.776     243.853     241.626     245.136     244.467     240.379
+ 246.957     242.633        nothing  242.937     245.291     248.114     239.193     242.684
+ 244.724     241.375     244.211        nothing  245.861     238.117     245.085     242.28
+ 241.576     246.329     242.582     245.602        nothing  246.59      240.677     243.343
+ 247.114     240.18      245.965     244.006     236.616        nothing  242.28      244.673
+ 243.802     242.028     248.326     239.933     244.365     245.033        nothing  245.498
+ 245.136     246.904     239.488     243.343     244.057     240.627     243.445        nothing

    According to NVIDIA, the theoretical maximal peer-to-peer bandwidth for our A100 with NVSwitch is 300GB/s ≈ 279GiB/s, which is in reasonable agreement with our findings.

    Bidirectional

    julia> p2p_bandwidth_bidirectional();
+Memsize: 38.147 MiB
+
+Bandwidth (GiB/s):
+ ├ max: 450.36
+ ├ min: 448.66
+ ├ avg: 449.69
+ └ std_dev: 0.49
+
+julia> p2p_bandwidth_bidirectional_all()
+8×8 Matrix{Union{Nothing, Float64}}:
+    nothing  456.631     453.133     454.946     453.67      453.953     455.06      454.662
+ 453.67         nothing  454.01      450.329     455.345     453.02      454.691     455.203
+ 453.981     451.53         nothing  452.344     453.868     454.747     452.232     454.208
+ 453.557     451.979     449.883        nothing  454.01      455.288     450.189     454.691
+ 452.429     454.293     445.094     454.151        nothing  453.472     451.474     453.981
+ 454.89      454.066     453.84      453.84      451.194        nothing  453.274     451.53
+ 453.925     453.02      454.293     456.459     451.839     451.951        nothing  455.032
+ 454.208     416.936     454.265     435.947     452.035     437.836     451.895        nothing
    diff --git a/previews/PR26/examples/gpuinfo/index.html b/previews/PR26/examples/gpuinfo/index.html new file mode 100644 index 0000000..5ad7d45 --- /dev/null +++ b/previews/PR26/examples/gpuinfo/index.html @@ -0,0 +1,69 @@ + +GPU Information · GPUInspector.jl
    Edit on GitHub

    Example: CUDA/GPU Information

    Note

    Test system: DGX with 8x A100 GPUs

    julia> gpus()
+8 devices:
+  0: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)
+  1: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)
+  2: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)
+  3: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)
+  4: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)
+  5: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)
+  6: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)
+  7: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)

    More specific information for each gpu device can be obtained with gpuinfo(deviceid::Integer).

    julia> gpuinfo()
+Device: NVIDIA A100-SXM4-40GB (CuDevice(0))
+Total amount of global memory: 42.5 GB
+Number of CUDA cores: 6912
+Number of multiprocessors: 108 (64 CUDA cores each)
+GPU max. clock rate: 1410 Mhz
+Memory clock rate: 1215 Mhz
+Memory bus width: 5120-bit
+L2 cache size: 41.9 MB
+Max. texture dimension sizes (1D): 131072
+Max. texture dimension sizes (2D): 131072, 65536
+Max. texture dimension sizes (3D): 16384, 16384, 16384
+Max. layered 1D texture size: 32768 (2048 layers)
+Max. layered 2D texture size: 32768, 32768 (2048 layers)
+Total amount of constant memory: 65.5 kB
+Total amount of shared memory per block: 49.2 kB
+Total number of registers available per block: 65536
+Warp size: 32
+Max. number of threads per multiprocessor: 2048
+Max. number of threads per block: 1024
+Max. dimension size of a thread block (x,y,z): 1024, 1024, 64
+Max. dimension size of a grid size (x,y,z): 2147483647, 65535, 65535
+Texture alignment: 512.0 B
+Maximum memory pitch: 2.1 GB
+Concurrent copy and kernel execution: Yes with 3 copy engine(s)
+Run time limit on kernels: No
+Integrated GPU sharing host memory: No
+Support host page-locked memory mapping: Yes
+Concurrent kernel execution: Yes
+Alignment requirement for surfaces: Yes
+Device has ECC support: Yes
+Device supports Unified Addressing (UVA): Yes
+Device supports managed memory: Yes
+Device supports compute preemption: Yes
+Supports cooperative kernel launch: Yes
+Supports multi-device co-op kernel launch: Yes
+Device PCI domain ID / bus ID / device ID: 0 / 7 / 0
+Compute mode: Default (multiple host threads can use ::cudaSetDevice() with device simultaneously)

    Peer-to-peer access information can be retrived via gpuinfo_p2p_access.

    julia> gpuinfo_p2p_access()
+P2P Access Supported:
+8×8 Matrix{Bool}:
+ 0  1  1  1  1  1  1  1
+ 1  0  1  1  1  1  1  1
+ 1  1  0  1  1  1  1  1
+ 1  1  1  0  1  1  1  1
+ 1  1  1  1  0  1  1  1
+ 1  1  1  1  1  0  1  1
+ 1  1  1  1  1  1  0  1
+ 1  1  1  1  1  1  1  0
+
+P2P Atomic Supported:
+8×8 Matrix{Bool}:
+ 0  1  1  1  1  1  1  1
+ 1  0  1  1  1  1  1  1
+ 1  1  0  1  1  1  1  1
+ 1  1  1  0  1  1  1  1
+ 1  1  1  1  0  1  1  1
+ 1  1  1  1  1  0  1  1
+ 1  1  1  1  1  1  0  1
+ 1  1  1  1  1  1  1  0

    Turns out that using CU_DEVICE_P2P_ATTRIBUTE_ACCESS_SUPPORTED or cuDeviceCanAccessPeer to query p2p access support may lead to different results (see this stackoverflow thread). In gpuinfo_p2p_access() we use both methods and, if the results were to be different, we print both matrices (not shown above).

    diff --git a/previews/PR26/examples/gpustresstest/index.html b/previews/PR26/examples/gpustresstest/index.html new file mode 100644 index 0000000..3c11f32 --- /dev/null +++ b/previews/PR26/examples/gpustresstest/index.html @@ -0,0 +1,120 @@ + +GPU Stress Test · GPUInspector.jl
    Edit on GitHub

    Example: GPU Stress Test

    Note

    Test system: DGX with 8x A100 GPUs

    Single GPU

    julia> using GPUInspector, CUDA
+
+julia> stresstest(; devices=CUDA.device(), duration=10)
+[ Info: Will try to run for approximately 10 seconds on each GPU.
+[ Info: Running StressTest{Float32} on Julia thread 2 and CuDevice(0).
+[ Info: Ran 11241 iterations on CuDevice(0).
+[ Info: Clearing GPU memory.
+[ Info: Took 10.0 seconds to run the tests.

    Multiple GPUs

    First, make sure to run Julia with enough threads (more or equal to the number of GPU devices). Then, to run a GPU Stress test, use stresstest.

    In the following example, I started Julia with julia -t 20 (why not? 😄).

    julia> using GPUInspector, CUDA
+
+julia> stresstest(; devices=CUDA.devices(), duration=10)
+[ Info: Will try to run for approximately 10 seconds on each GPU.
+[ Info: Running StressTest{Float32} on Julia thread 2 and CuDevice(0).
+[ Info: Running StressTest{Float32} on Julia thread 8 and CuDevice(6).
+[ Info: Running StressTest{Float32} on Julia thread 4 and CuDevice(2).
+[ Info: Running StressTest{Float32} on Julia thread 7 and CuDevice(5).
+[ Info: Running StressTest{Float32} on Julia thread 5 and CuDevice(3).
+[ Info: Running StressTest{Float32} on Julia thread 9 and CuDevice(7).
+[ Info: Running StressTest{Float32} on Julia thread 6 and CuDevice(4).
+[ Info: Running StressTest{Float32} on Julia thread 3 and CuDevice(1).
+[ Info: Ran 11222 iterations on CuDevice(3).
+[ Info: Ran 11203 iterations on CuDevice(6).
+[ Info: Ran 11227 iterations on CuDevice(2).
+[ Info: Ran 11230 iterations on CuDevice(4).
+[ Info: Ran 11231 iterations on CuDevice(1).
+[ Info: Ran 11199 iterations on CuDevice(0).
+[ Info: Ran 11215 iterations on CuDevice(5).
+[ Info: Ran 11246 iterations on CuDevice(7).
+[ Info: Clearing GPU memory.
+[ Info: Took 10.0 seconds to run the tests.

    Monitoring

    To verify that the GPUs are actually busy you could use external tools like, e.g., nvidia-smi. However, we also provide built-in monitoring tools.

    julia> using GPUInspector, CUDA
+
+julia> monitoring_start()                                                           
+[ Info: Spawning monitoring on Julia thread 20.
+
+julia> stresstest(; devices=CUDA.devices(), duration=10) # all devices, 10 seconds
+[ Info: Will try to run for approximately 10 seconds on each GPU.
+[ Info: Running StressTest{Float32} on Julia thread 4 and CuDevice(2).
+[ Info: Running StressTest{Float32} on Julia thread 2 and CuDevice(0).
+[ Info: Running StressTest{Float32} on Julia thread 6 and CuDevice(4).
+[ Info: Running StressTest{Float32} on Julia thread 3 and CuDevice(1).
+[ Info: Running StressTest{Float32} on Julia thread 9 and CuDevice(7).
+[ Info: Running StressTest{Float32} on Julia thread 7 and CuDevice(5).
+[ Info: Running StressTest{Float32} on Julia thread 5 and CuDevice(3).
+[ Info: Running StressTest{Float32} on Julia thread 8 and CuDevice(6).
+[ Info: Ran 11215 iterations on CuDevice(2).
+[ Info: Ran 11241 iterations on CuDevice(6).
+[ Info: Ran 11261 iterations on CuDevice(1).
+[ Info: Ran 11236 iterations on CuDevice(5).
+[ Info: Ran 11263 iterations on CuDevice(4).
+[ Info: Ran 11261 iterations on CuDevice(3).
+[ Info: Ran 11270 iterations on CuDevice(7).
+[ Info: Ran 11241 iterations on CuDevice(0).
+[ Info: Clearing GPU memory.
+[ Info: Took 10.0 seconds to run the tests.
+
+julia> results = monitoring_stop();
+[ Info: Stopping monitoring and fetching results...

    Here, results is a MonitoringResults object with time series information about GPU utilization, temperature, and more. See monitoring_stop for details.

    You can use save_monitoring_results to save the results on disk. We also provide plot_monitoring_results to visualize the results, which should give you an output like this.

    julia> plot_monitoring_results(results)
+
+             ⠀⠀⠀⠀⠀⠀⠀⠀⠀GPU Utilization (Compute)
+             ┌────────────────────────────────────────┐        
+         105 │⠀⠀⠀⠀⡤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⢤⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 0: NVIDIA A100-SXM4-40GB
+             │⠀⠀⠀⢰⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⡆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 1: NVIDIA A100-SXM4-40GB
+             │⠀⠀⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 2: NVIDIA A100-SXM4-40GB
+             │⠀⠀⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 3: NVIDIA A100-SXM4-40GB
+             │⠀⠀⠀⡸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 4: NVIDIA A100-SXM4-40GB
+             │⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 5: NVIDIA A100-SXM4-40GB
+             │⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 6: NVIDIA A100-SXM4-40GB
+   U [%]     │⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 7: NVIDIA A100-SXM4-40GB
+             │⠀⠀⢰⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⡆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│                             
+             │⠀⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│                             
+             │⠀⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│                             
+             │⠀⠀⡸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│                             
+             │⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│                             
+             │⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│                             
+           0 │⣀⣀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⣀⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│                             
+             └────────────────────────────────────────┘                             
+             ⠀0⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀20⠀                             
+             ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀Time [s]⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀                                                          
+                                                                                                                 
+            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀GPU Temperature⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀                             
+            ┌────────────────────────────────────────┐                             
+         63 │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 0: NVIDIA A100-SXM4-40GB
+            │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⡠⠤⠤⠤⠒⢒⣲⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 1: NVIDIA A100-SXM4-40GB
+            │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⡠⠤⠒⠊⣁⠤⣤⠤⠶⠮⠛⠛⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 2: NVIDIA A100-SXM4-40GB
+            │⠀⠀⠀⠀⠀⠀⠀⡠⣒⣉⣉⣭⣓⠭⠛⠋⠉⠉⠉⠀⠀⠀⠀⠀⠀⢻⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 3: NVIDIA A100-SXM4-40GB 
+            │⠀⠀⠀⠀⠀⢠⣮⠮⠊⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⡠⢤⣤⠸⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 4: NVIDIA A100-SXM4-40GB 
+            │⠀⠀⠀⠀⢠⡿⠁⠀⠀⠀⠀⠀⢀⡠⠤⣤⠤⠶⠮⠛⠋⣉⠭⠥⠤⡇⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 5: NVIDIA A100-SXM4-40GB 
+            │⠀⠀⠀⢠⣷⠁⠀⣠⣒⠶⠒⢉⣉⢭⣛⣒⣒⡪⠝⠛⠛⠊⠉⠉⠉⣿⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 6: NVIDIA A100-SXM4-40GB 
+   T [C]    │⠀⠀⠀⣼⠃⢠⠊⣁⠤⠔⠚⠓⠊⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢻⡞⠲⡤⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 7: NVIDIA A100-SXM4-40GB 
+            │⠀⠀⢠⡏⢠⣿⠋⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠸⣧⠀⠈⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│                              
+            │⠀⠀⣼⢡⣷⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│                              
+            │⠤⠤⡟⣸⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠸⡳⡢⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│                              
+            │⠉⠛⢇⡟⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠪⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│                              
+            │⠀⠀⣸⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│                              
+            │⠒⠒⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│                              
+         28 │⠉⠉⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│                              
+            └────────────────────────────────────────┘                              
+            ⠀0⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀20⠀                              
+            ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀Time [s]⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀                              
+                                                                                                                 
+             ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀GPU Power Usage⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀                              
+             ┌────────────────────────────────────────┐                             
+         340 │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣀⣀⣀⣀⣀⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 0: NVIDIA A100-SXM4-40GB
+             │⠀⠀⠀⠀⣤⣠⣤⣤⣶⡶⠶⠶⠶⠾⠿⠿⣶⣿⣷⣶⣶⣶⣒⣒⣺⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 1: NVIDIA A100-SXM4-40GB
+             │⠀⠀⠀⢰⡯⡭⡿⠟⠛⠛⠛⠛⠛⠛⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠹⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 2: NVIDIA A100-SXM4-40GB
+             │⠀⠀⠀⢸⠋⡜⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 3: NVIDIA A100-SXM4-40GB
+             │⠀⠀⠀⢸⡰⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣧⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 4: NVIDIA A100-SXM4-40GB
+             │⠀⠀⠀⣾⠇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢻⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 5: NVIDIA A100-SXM4-40GB
+             │⠀⠀⠀⣿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 6: NVIDIA A100-SXM4-40GB
+   P [W]     │⠀⠀⠀⣿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 7: NVIDIA A100-SXM4-40GB
+             │⠀⠀⢠⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│                              
+             │⠀⠀⢸⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│                              
+             │⠀⠀⢸⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣧⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│                              
+             │⠀⠀⣼⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢹⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│                              
+             │⠀⠀⡟⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│                              
+             │⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⣶⣶⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│                              
+          56 │⣶⣶⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠙⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│                              
+             └────────────────────────────────────────┘                              
+             ⠀0⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀20⠀                              
+             ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀Time [s]⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀

    Note that for convenience stresstest also takes a monitoring keyword argument which can be used to automatically trigger the starting and stopping of the monitoring.

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    Edit on GitHub

    Example: Peakflops

    Note

    Test system: NVIDIA A100 GPU

    CUDA cores

    We assess the peak performance of the CUDA cores by executing many pure FMAs (fused multiply-adds) on the CUDA cores in parallel.

    Theoretically, we expect the performances below, which we compute from the clock rate, number of CUDA cores etc. The same numbers are given by NVIDIA, e.g. here or here, or Wikipedia, e.g. here.

    julia> theoretical_peakflops_gpu(; dtype=Float32, tensorcores=false);
+Theoretical Peakflops (TFLOP/s):
+ ├ tensorcores: false
+ ├ dtype: Float32
+ └ max: 19.5
+
+julia> theoretical_peakflops_gpu(; dtype=Float64, tensorcores=false);
+Theoretical Peakflops (TFLOP/s):
+ ├ tensorcores: false
+ ├ dtype: Float64
+ └ max: 9.7

    In good agreement, we find the following empirical numbers.

    julia> peakflops_gpu(; dtype=Float32, tensorcores=false);
+Peakflops (TFLOP/s):
+ ├ tensorcores: false
+ ├ dtype: Float32
+ └ max: 19.1
+
+julia> peakflops_gpu(; dtype=Float64, tensorcores=false);
+Peakflops (TFLOP/s):
+ ├ tensorcores: false
+ ├ dtype: Float64
+ └ max: 9.6
+
+julia> peakflops_gpu(; dtype=Float16, tensorcores=false);
+Peakflops (TFLOP/s):
+ ├ tensorcores: false
+ ├ dtype: Float16
+ └ max: 12.8

    Tensor Cores

    We assess the peak performance of the Tensor cores by executing many pure WMMAs (warp-level matrix-multiply-and-accumulate), see, e.g., here and here), on the Tensor cores in parallel.

    Theoretically, we expect the performances below, which we compute from the clock rate, number of Tensor cores etc. The same numbers are given by NVIDIA, e.g. here or here, or on Wikipedia, e.g. here.

    julia> theoretical_peakflops_gpu(; dtype=Int8, tensorcores=true);
+Theoretical Peakflops (TOP/s):
+ ├ tensorcores: true
+ ├ dtype: Int8
+ └ max: 623.7
+
+julia> theoretical_peakflops_gpu(; dtype=Float16, tensorcores=true);
+Theoretical Peakflops (TFLOP/s):
+ ├ tensorcores: true
+ ├ dtype: Float16
+ └ max: 311.9
+
+julia> theoretical_peakflops_gpu(; dtype=Float32, tensorcores=true);
+Theoretical Peakflops (TFLOP/s):
+ ├ tensorcores: true
+ ├ dtype: Float32
+ └ max: 155.9

    Empirically, we find the following numbers in good agreement.

    julia> peakflops_gpu(; dtype=Int8, tensorcores=true); # as of writing, only works with CUDA.jl#master
+Peakflops (TOP/s):
+ ├ tensorcores: true
+ ├ dtype: Int8
+ └ max: 620.1
+
+julia> peakflops_gpu(; dtype=Float16, tensorcores=true);
+Peakflops (TFLOP/s):
+ ├ tensorcores: true
+ ├ dtype: Float16
+ └ max: 311.2
+
+julia> peakflops_gpu(; dtype=:TensorFloat32, tensorcores=true); # as of writing, requires Julia 1.8 and https://github.com/JuliaGPU/CUDA.jl/pull/1419
+Peakflops (TFLOP/s):
+ ├ tensorcores: true
+ ├ dtype: TensorFloat32
+ └ max: 155.5
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a/previews/PR26/explanations/dgx/index.html b/previews/PR26/explanations/dgx/index.html new file mode 100644 index 0000000..b270356 --- /dev/null +++ b/previews/PR26/explanations/dgx/index.html @@ -0,0 +1,2 @@ + +DGX Details · GPUInspector.jl

     diff --git a/previews/PR26/index.html b/previews/PR26/index.html new file mode 100644 index 0000000..f3d06ec --- /dev/null +++ b/previews/PR26/index.html @@ -0,0 +1,3 @@ + +GPUInspector · GPUInspector.jl
    Edit on GitHub

    GPUInspector.jl

    Installation

    The package is registered in the General registry and can readily be added by using the Pkg REPL mode.

    ] add GPUInspector

    Note: The minimal required Julia version is 1.6 but we strongly recommend to use Julia >= 1.7. Some features might not be available in Julia 1.6!

    Getting Started

    Backends

    GPUInspector itself only provides limited functionality. Most of its features come to live - through package extensions - when you load a GPU backend, like CUDA.jl. Hence, most of the time, you want to add one of these backends next to GPUInspector to your Julia environment and then run

    using GPUInspector
+using CUDA # loading a GPU backend triggers the pkg extension to load

    Note that you can check the current backend with the backend() function (and set it manually via backend!).

    Examples

    diff --git a/previews/PR26/refs/UnitPrefixedBytes/index.html b/previews/PR26/refs/UnitPrefixedBytes/index.html new file mode 100644 index 0000000..9811c38 --- /dev/null +++ b/previews/PR26/refs/UnitPrefixedBytes/index.html @@ -0,0 +1,10 @@ + +Unit-Prefixed Bytes · GPUInspector.jl
    Edit on GitHub

    Unit-Prefixed Bytes

    Index

    References

    GPUInspector.UnitPrefixedBytes — Type

    Abstract type representing an amount of data, i.e. a certain number of bytes, with a unit prefix (also "metric prefix"). Examples include the SI prefixes, like KB, MB, and GB, but also the binary prefixes (ISO/IEC 80000), like KiB, MiB, and GiB.

    See https://en.wikipedia.org/wiki/Binary_prefix for more information.

    GPUInspector.bytes — Method
    bytes(x::Number)

    Returns an appropriate UnitPrefixedBytes object, representing the number of bytes.

    Note: This function is type unstable by construction!

    See simplify for what "appropriate" means here.

    GPUInspector.bytes — Method
    bytes(x::UnitPrefixedBytes)

    Return the number of bytes (without prefix) as Float64.

    GPUInspector.change_base — Method

    Toggle between

    • Base 10, SI prefixes, i.e. factors of 1000
    • Base 2, ISO/IEC prefixes, i.e. factors of 1024

    Example:

    julia> change_base(KB(13))
+~12.7 KiB
+
+julia> change_base(KiB(13))
+~13.31 KB
    GPUInspector.simplify — Method
    simplify(x::UnitPrefixedBytes[; base])

    Given a UnitPrefixedBytes number x, finds a more appropriate UnitPrefixedBytes that represents the same number of bytes but with a smaller value.

    The optional keyword argument base can be used to switch between base 2, i.e. ISO/IEC prefixes (default), and base 10. Allowed values are 2, 10, :SI, :ISO, and :IEC.

    Note: This function is type unstable by construction!

    Example:

    julia> simplify(B(40_000_000))
+~38.15 MiB
+
+julia> simplify(B(40_000_000); base=10)
+40.0 MB
    diff --git a/previews/PR26/refs/backends/index.html b/previews/PR26/refs/backends/index.html new file mode 100644 index 0000000..00537da --- /dev/null +++ b/previews/PR26/refs/backends/index.html @@ -0,0 +1,3 @@ + +Backends · GPUInspector.jl
    Edit on GitHub

    Backends

    Index

    References

    GPUInspector.backend! — Method
    backend!(b::Backend)
+

    Set the GPU backend (manually). Note that the corresponding backend package (e.g. CUDA.jl) must already be loaded in the active Julia session (otherwise an exception is thrown).

    GPUInspector.backendinfo — Method

    Query information about a specific backend, e.g., what functionality the backend currently supports.

    GPUInspector.functional — Method

    Check if GPUInspector, and its GPU backend (e.g. CUDA.jl), is available and functional. If not, print some hopefully useful debug information (or turn it off with verbose=false).

    diff --git a/previews/PR26/refs/cuda_extension/index.html b/previews/PR26/refs/cuda_extension/index.html new file mode 100644 index 0000000..cb7452b --- /dev/null +++ b/previews/PR26/refs/cuda_extension/index.html @@ -0,0 +1,3 @@ + +CUDA Extension · GPUInspector.jl
    Edit on GitHub

    CUDA Extension

    Index

    References

    CUDAExt.get_gpu_utilization — Function
    get_gpu_utilization(device=CUDA.device())

    Get the current utilization of the given CUDA device in percent.

    CUDAExt.get_gpu_utilizations — Function
    get_gpu_utilizations(devices=CUDA.devices())

    Get the current utilization of the given CUDA devices in percent.

    CUDAExt.get_power_usage — Method
    get_power_usage(device=CUDA.device())

    Get current power usage of the given CUDA device in Watts.

    CUDAExt.get_power_usages — Function
    get_power_usages(devices=CUDA.devices())

    Get current power usage of the given CUDA devices in Watts.

    CUDAExt.get_temperature — Function
    get_temperature(device=CUDA.device())

    Get current temperature of the given CUDA device in degrees Celsius.

    CUDAExt.get_temperatures — Function
    get_temperatures(devices=CUDA.devices())

    Get current temperature of the given CUDA devices in degrees Celsius.

    CUDAExt.gpuid — Function

    Get GPU index of the given device.

    Note: GPU indices start with zero.

    CUDAExt._kernel_fma — Method

    Dummy kernel doing _kernel_fma_nfmas() many FMAs (default: 100_000).

    CUDAExt._peakflops_gpu_fmas — Method
    _peakflops_gpu_fmas(; size::Integer=5_000_000, dtype=Float32, nbench=5, nkernel=5, device=CUDA.device(), verbose=true)

    Tries to estimate the peak performance of a GPU in TFLOP/s by measuring the time it takes to perform _kernel_fma_nfmas() * size many FMAs on CUDA cores.

    Keyword arguments:

    • device (default: CUDA.device()): CUDA device to be used.
    • dtype (default: Float32): element type of the matrices.
    • size (default: 5_000_000): length of vectors.
    • nkernel (default: 5): number of kernel calls that make up one benchmarking sample.
    • nbench (default: 5): number of measurements to be performed the best of which is used for the TFLOP/s computation.
    • verbose (default: true): toggle printing.
    • io (default: stdout): set the stream where the results should be printed.
    CUDAExt._peakflops_gpu_wmmas — Method
    _peakflops_gpu_wmmas()

    Tries to estimate the peak performance of a GPU in TFLOP/s by measuring the time it takes to perform _kernel_wmma_nwmmas() many WMMAs on Tensor Cores.

    Keyword arguments:

    • device (default: CUDA.device()): CUDA device to be used.
    • dtype (default: Float16): element type of the matrices. We currently only support Float16 (Int8, :TensorFloat32, :BFloat16, and Float64 might or might not work).
    • nkernel (default: 10): number of kernel calls that make up one benchmarking sample.
    • nbench (default: 5): number of measurements to be performed the best of which is used for the TFLOP/s computation.
    • threads (default: max. threads per block): how many threads to use per block (part of the kernel launch configuration).
    • blocks (default: 2048): how many blocks to use (part of the kernel launch configuration).
    • verbose (default: true): toggle printing.
    • io (default: stdout): set the stream where the results should be printed.
    CUDAExt.peakflops_gpu_matmul — Method
    peakflops_gpu_matmul(; device, dtype=Float32, size=2^14, nmatmuls=5, nbench=5, verbose=true)

    Tries to estimate the peak performance of a GPU in TFLOP/s by measuring the time it takes to perform nmatmuls many (in-place) matrix-matrix multiplications.

    Keyword arguments:

    • device (default: CUDA.device()): CUDA device to be used.
    • dtype (default: Float32): element type of the matrices.
    • size (default: 2^14): matrices will have dimensions (size, size).
    • nmatmuls (default: 5): number of matmuls that will make up the kernel to be timed.
    • nbench (default: 5): number of measurements to be performed the best of which is used for the TFLOP/s computation.
    • verbose (default: true): toggle printing.
    • io (default: stdout): set the stream where the results should be printed.

    See also: peakflops_gpu_matmul_scaling, peakflops_gpu_matmul_graphs.

    CUDAExt.peakflops_gpu_matmul_scaling — Method
    peakflops_gpu_matmul_scaling(peakflops_func = peakflops_gpu_matmul; verbose=true) -> sizes, flops

    Asserts the scaling of the given peakflops_function (defaults to peakflops_gpu_matmul) with increasing matrix size. If verbose=true (default), displays a unicode plot. Returns the considered sizes and TFLOP/s. For further options, see peakflops_gpu_matmul.

    CUDAExt.StressTestBatched — Type

    GPU stress test (matrix multiplications) in which we try to run for a given time period. We try to keep the CUDA stream continously busy with matmuls at any point in time. Concretely, we submit batches of matmuls and, after half of them, we record a CUDA event. On the host, after submitting a batch, we (non-blockingly) synchronize on, i.e. wait for, the CUDA event and, if we haven't exceeded the desired duration already, submit another batch.

    CUDAExt.StressTestEnforced — Type

    GPU stress test (matrix multiplications) in which we run almost precisely for a given time period (duration is enforced).

    CUDAExt.StressTestFixedIter — Type

    GPU stress test (matrix multiplications) in which we run for a given number of iteration, or try to run for a given time period (with potentially high uncertainty!). In the latter case, we estimate how long a synced matmul takes and set niter accordingly.

    CUDAExt.StressTestStoreResults — Type

    GPU stress test (matrix multiplications) in which we store all matmul results and try to run as many iterations as possible for a certain memory limit (default: 90% of free memory).

    This stress test is somewhat inspired by gpu-burn by Ville Timonen.

    CUDAExt.alloc_mem — Method
    alloc_mem(memsize::UnitPrefixedBytes; devs=(CUDA.device(),), dtype=Float32)

    Allocates memory on the devices whose IDs are provided via devs. Returns a vector of memory handles (i.e. CuArrays).

    Examples:

    alloc_mem(MiB(1024)) # allocate on the currently active device
+alloc_mem(B(40_000_000); devs=(0,1)) # allocate on GPU0 and GPU1
    CUDAExt.toggle_tensorcoremath — Function
    toggle_tensorcoremath([enable::Bool; verbose=true])

    Switches the CUDA.math_mode between CUDA.FAST_MATH (enable=true) and CUDA.DEFAULT_MATH (enable=false). For matmuls of CuArray{Float32}s, this should have the effect of using/enabling and not using/disabling tensor cores. Of course, this only works on supported devices and CUDA versions.

    If no arguments are provided, this functions toggles between the two math modes.

    GPUInspector.memory_bandwidth_saxpy — Method

    Extra keyword arguments:

    • cublas (default: true): toggle between CUDA.axpy! and a custom _saxpy_gpu_kernel!.

    (This method is from the NVIDIA Backend.)

    GPUInspector.gpuinfo — Method
    gpuinfo(deviceid::Integer)

    Print out detailed information about the NVIDIA GPU with the given deviceid.

    Heavily inspired by the CUDA sample "deviceQueryDrv.cpp".

    (This method is from the NVIDIA Backend.)

    GPUInspector.monitoring_stop — Method
    monitoring_stop(; verbose=true) -> results

    Specifically, results is a named tuple with the following keys:

    • time: the (relative) times at which we measured
    • temperature, power, compute, mem

    (This method is from the NVIDIA Backend.)

    diff --git a/previews/PR26/refs/data_bandwidth/index.html b/previews/PR26/refs/data_bandwidth/index.html new file mode 100644 index 0000000..979ec17 --- /dev/null +++ b/previews/PR26/refs/data_bandwidth/index.html @@ -0,0 +1,6 @@ + +Data Bandwidth · GPUInspector.jl
    Edit on GitHub

    Data Bandwidth

    Index

    References

    GPUInspector.memory_bandwidth — Method
    memory_bandwidth(; kwargs...)

    Tries to estimate the peak memory bandwidth of a GPU in GiB/s by measuring the time it takes to perform a memcpy of a certain amount of data (as specified by memsize).

    Keyword arguments:

    • memsize (default: GiB(0.5)): memory size to be used
    • device (default: e.g. CUDA.device()): GPU device to be used.
    • dtype (default: Cchar): element type of the vectors.
    • verbose (default: true): toggle printing.
    • io (default: stdout): set the stream where the results should be printed.

    See also: memory_bandwidth_scaling.

    GPUInspector.memory_bandwidth_saxpy — Method

    Tries to estimate the peak memory bandwidth of a GPU in GiB/s by measuring the time it takes to perform a SAXPY, i.e. a * x[i] + y[i].

    Keyword arguments:

    • device (default: e.g. CUDA.device()): GPU device to be used.
    • dtype (default: Float32): element type of the vectors.
    • size (default: 2^20 * 10): length of the vectors.
    • nbench (default: 5): number of measurements to be performed the best of which is used for the GiB/s computation.
    • verbose (default: true): toggle printing.
    • io (default: stdout): set the stream where the results should be printed.

    See also: memory_bandwidth_saxpy_scaling.

    GPUInspector.host2device_bandwidth — Method
    host2device_bandwidth(; kwargs...)

    Performs a host-to-device memory copy benchmark (time measurement) and returns the host-to-device bandwidth estimate (in GiB/s) derived from it.

    Keyword arguments:

    • memsize (default: GiB(0.5)): memory size to be used
    • nbench (default: 10): number of time measurements (i.e. p2p memcopies)
    • verbose (default: true): set to false to turn off any printing.
    • stats (default: false): when true shows statistical information about the benchmark.
    • times (default: false): toggle printing of measured times.
    • dtype (default: Cchar): used data type.
    • io (default: stdout): set the stream where the results should be printed.

    Examples:

    host2device_bandwidth()
+host2device_bandwidth(MiB(1024))
+host2device_bandwidth(KiB(20_000); dtype=Int32)
    GPUInspector.p2p_bandwidth — Method

    p2p_bandwidth(; kwargs...) Performs a peer-to-peer memory copy benchmark (time measurement) and returns an inter-gpu memory bandwidth estimate (in GiB/s) derived from it.

    Keyword arguments:

    • memsize (default: B(40_000_000)): memory size to be used
    • src (default: 0): source device
    • dst (default: 1): destination device
    • nbench (default: 5): number of time measurements (i.e. p2p memcopies)
    • verbose (default: true): set to false to turn off any printing.
    • hist (default: false): when true, a UnicodePlots-based histogram is printed.
    • times (default: false): toggle printing of measured times.
    • alternate (default: false): alternate src and dst, i.e. copy data back and forth.
    • dtype (default: Float32): see alloc_mem.
    • io (default: stdout): set the stream where the results should be printed.

    Examples:

    p2p_bandwidth()
+p2p_bandwidth(MiB(1024))
+p2p_bandwidth(KiB(20_000); dtype=Int32)
    diff --git a/previews/PR26/refs/gpuinfo/index.html b/previews/PR26/refs/gpuinfo/index.html new file mode 100644 index 0000000..58cdb83 --- /dev/null +++ b/previews/PR26/refs/gpuinfo/index.html @@ -0,0 +1,2 @@ + +GPU Information · GPUInspector.jl
    Edit on GitHub

    GPU Information

    Heavily inspired by the cuda sample deviceQueryDrv.

    Index

    References

    GPUInspector.gpuinfo — Method
    gpuinfo([device]; kwargs...)

    Print out detailed information about the GPU with the given device id.

    Note: Device ids start at zero!

    GPUInspector.ngpus — Method

    Returns the number of available GPUs for the given/current backend

    diff --git a/previews/PR26/refs/gpustresstest/index.html b/previews/PR26/refs/gpustresstest/index.html new file mode 100644 index 0000000..312a01d --- /dev/null +++ b/previews/PR26/refs/gpustresstest/index.html @@ -0,0 +1,2 @@ + +GPU Stress Test · GPUInspector.jl
    Edit on GitHub

    GPU Stresstest

    Index

    References

    GPUInspector.stresstest — Method
    stresstest(; kwargs...)

    Run a GPU stress test (matrix multiplication) on one or multiple GPU devices, as specified by the positional argument. If no argument is provided (only) the currently active GPU will be used.

    Keyword arguments:

    Choose one of the following (or none):

    • duration: stress test will take about the given time in seconds. (StressTestBatched)
    • enforced_duration: stress test will take almost precisely the given time in seconds. (StressTestEnforced)
    • approx_duration: stress test will hopefully take approximately the given time in seconds. No promises made! (StressTestFixedIter)
    • niter: stress test will run the given number of matrix-multiplications, however long that will take. (StressTestFixedIter)
    • mem: number (<:Real) between 0 and 1, indicating the fraction of the available GPU memory that should be used, or a <:UnitPrefixedBytes indicating an absolute memory limit. (StressTestStoreResults)

    General settings:

    • devices (default: e.g. [CUDA.device()]): GPU devices to be included in the stress test
    • dtype (default: Float32): element type of the matrices
    • monitoring (default: false): enable automatic monitoring, in which case a MonitoringResults object is returned.
    • size (default: 2048): matrices of size (size, size) will be used
    • verbose (default: true): toggle printing of information
    • parallel (default: true): If true, will (try to) run each GPU test on a different Julia thread. Make sure to have enough Julia threads.
    • threads (default: nothing): If parallel == true, this argument may be used to specify the Julia threads to use.
    • clearmem (default: false): If true, we call clear_all_gpus_memory after the stress test.
    • io (default: stdout): set the stream where the results should be printed.

    When duration is specifiec (i.e. StressTestEnforced) there is also:

    • batch_duration (default: ceil(Int, duration/10)): desired duration of one batch of matmuls.
    diff --git a/previews/PR26/refs/monitoring/index.html b/previews/PR26/refs/monitoring/index.html new file mode 100644 index 0000000..7018a34 --- /dev/null +++ b/previews/PR26/refs/monitoring/index.html @@ -0,0 +1,2 @@ + +GPU Monitoring · GPUInspector.jl
    Edit on GitHub

    GPU Monitoring

    Index

    References

    GPUInspector.livemonitor_powerusage — Method
    livemonitor_powerusage(duration) -> times, powerusage

    Monitor the power usage of GPU(s) (in Watts) over a given time period, as specified by duration (in seconds). Returns the (relative) times as a Vector{Float64} and the power usage as a Vector{Vector{Float64}}.

    For general keyword arguments, see livemonitor_something.

    GPUInspector.livemonitor_something — Method
    livemonitor_something(f, duration) -> times, values

    Monitor some property of GPU(s), as specified through the function f, over a given time period, as specified by duration (in seconds). Returns the (relative) times as a Vector{Float64} and the temperatures as a Vector{Vector{Float64}}.

    The function f will be called on a vector of devices and should return a vector of Float64 values.

    Keyword arguments:

    • freq (default: 1): polling rate in Hz.
    • devices (default: e.g. NVML.devices()): Devices to monitor.
    • plot (default: false): Create a unicode plot after the monitoring.
    • liveplot (default: false): Create and update a unicode plot during the monitoring. Use optional ylims to specify fixed y limits.
    • title (default: ""): Title used in unicode plots.
    • ylabel (default: "Values"): y label used in unicode plots.

    See: livemonitor_temperature, livemonitor_powerusage

    GPUInspector.livemonitor_temperature — Method
    livemonitor_temperature(duration) -> times, temperatures

    Monitor the temperature of GPU(s) over a given time period, as specified by duration (in seconds). Returns the (relative) times as a Vector{Float64} and the temperatures as a Vector{Vector{Float64}}.

    For general keyword arguments, see livemonitor_something.

    GPUInspector.monitoring_start — Method
    monitoring_start(; devices, kwargs...)

    Start monitoring of GPU temperature, utilization, power usage, etc.

    Keyword arguments:

    • freq (default: 1): polling rate in Hz.
    • devices (default: e.g. CUDA.devices()): GPU devices to monitor.
    • thread (default: Threads.nthreads()): id of the Julia thread that should run the monitoring.
    • verbose (default: true): toggle verbose output.

    See also monitoring_stop.

    GPUInspector.savefig_monitoring_results — Function
    savefig_monitoring_results(r::MonitoringResults, symbols=keys(r.results); ext=:pdf)

    Save plots of the quantities specified through symbols of a MonitoringResults object to disk. Note: Only available if CairoMakie.jl is loaded next to GPUInspector.jl.

    GPUInspector.MonitoringResults — Type

    Struct to hold the results of monitoring. This includes the time points (times), the monitored devices (devices), as well as a dictionary holding the (vector-)values of different quantities (identified by symbols) at each of the time points.

    GPUInspector.plot_monitoring_results — Function
    plot_monitoring_results(r::MonitoringResults, symbols=keys(r.results))

    Plot the quantities specified through symbols of a MonitoringResults object. Will generate a textual in-terminal / in-logfile plot using UnicodePlots.jl.

    diff --git a/previews/PR26/refs/peakflops_gpu/index.html b/previews/PR26/refs/peakflops_gpu/index.html new file mode 100644 index 0000000..9c3a784 --- /dev/null +++ b/previews/PR26/refs/peakflops_gpu/index.html @@ -0,0 +1,2 @@ + +Peakflops · GPUInspector.jl
    Edit on GitHub

    Peakflops

    Index

    References

    GPUInspector.theoretical_peakflops_gpu — Method

    Estimates the theoretical peak performance of a GPU device in TFLOP/s.

    Keyword arguments:

    • verbose (default: true): toggle printing of information
    • device (default: e.g. CUDA.device()): GPU device to be analyzed
    • dtype (default: Float32): element type of the matrices
    • io (default: stdout): set the stream where the results should be printed.
    diff --git a/previews/PR26/refs/stresstest_cpu/index.html b/previews/PR26/refs/stresstest_cpu/index.html new file mode 100644 index 0000000..4c1d865 --- /dev/null +++ b/previews/PR26/refs/stresstest_cpu/index.html @@ -0,0 +1,2 @@ + +CPU Stress Test · GPUInspector.jl
    Edit on GitHub

    CPU Stresstest

    Index

    References

    GPUInspector.stresstest_cpu — Method
    stresstest_cpu(core_or_cores)

    Run a CPU stress test (matrix multiplication) on one or multiple CPU cores, as specified by the positional argument. If no argument is provided (only) the currently active CPU core will be used.

    Keyword arguments:

    • duration: stress test will take about the given time in seconds.
    • dtype (default: Float64): element type of the matrices
    • size (default: floor(Int, sqrt(L2_cachesize() / sizeof(dtype)))): matrices of size (size, size) will be used
    • verbose (default: true): toggle printing of information
    • parallel (default: true): If true, will (try to) run each CPU core test on a different Julia thread. Make sure to have enough Julia threads.
    • threads (default: nothing): If parallel == true, this argument may be used to specify the Julia threads to use.
    diff --git a/previews/PR26/refs/utility/index.html b/previews/PR26/refs/utility/index.html new file mode 100644 index 0000000..4e13e4f --- /dev/null +++ b/previews/PR26/refs/utility/index.html @@ -0,0 +1,2 @@ + +Utility · GPUInspector.jl
    Edit on GitHub
    diff --git a/previews/PR26/search/index.html b/previews/PR26/search/index.html new file mode 100644 index 0000000..de8f604 --- /dev/null +++ b/previews/PR26/search/index.html @@ -0,0 +1,2 @@ + +Search · GPUInspector.jl

    Loading search...

      diff --git a/previews/PR26/search_index.js b/previews/PR26/search_index.js new file mode 100644 index 0000000..3d17605 --- /dev/null +++ b/previews/PR26/search_index.js @@ -0,0 +1,3 @@ +var documenterSearchIndex = {"docs": +[{"location":"refs/gpustresstest/#GPU-Stresstest","page":"GPU Stress Test","title":"GPU Stresstest","text":"","category":"section"},{"location":"refs/gpustresstest/#Index","page":"GPU Stress Test","title":"Index","text":"","category":"section"},{"location":"refs/gpustresstest/","page":"GPU Stress Test","title":"GPU Stress Test","text":"Pages = [\"gpustresstest.md\"]\nOrder = [:function, :type]","category":"page"},{"location":"refs/gpustresstest/#References","page":"GPU Stress Test","title":"References","text":"","category":"section"},{"location":"refs/gpustresstest/","page":"GPU Stress Test","title":"GPU Stress Test","text":"Modules = [GPUInspector]\nPages = [\"stubs_stresstest.jl\"]","category":"page"},{"location":"refs/gpustresstest/#GPUInspector.stresstest-Tuple{}","page":"GPU Stress Test","title":"GPUInspector.stresstest","text":"stresstest(; kwargs...)\n\nRun a GPU stress test (matrix multiplication) on one or multiple GPU devices, as specified by the positional argument. If no argument is provided (only) the currently active GPU will be used.\n\nKeyword arguments:\n\nChoose one of the following (or none):\n\nduration: stress test will take about the given time in seconds. (StressTestBatched)\nenforced_duration: stress test will take almost precisely the given time in seconds. (StressTestEnforced)\napprox_duration: stress test will hopefully take approximately the given time in seconds. No promises made! (StressTestFixedIter)\nniter: stress test will run the given number of matrix-multiplications, however long that will take. (StressTestFixedIter)\nmem: number (<:Real) between 0 and 1, indicating the fraction of the available GPU memory that should be used, or a <:UnitPrefixedBytes indicating an absolute memory limit. (StressTestStoreResults)\n\nGeneral settings:\n\ndevices (default: e.g. [CUDA.device()]): GPU devices to be included in the stress test\ndtype (default: Float32): element type of the matrices\nmonitoring (default: false): enable automatic monitoring, in which case a MonitoringResults object is returned.\nsize (default: 2048): matrices of size (size, size) will be used\nverbose (default: true): toggle printing of information\nparallel (default: true): If true, will (try to) run each GPU test on a different Julia thread. Make sure to have enough Julia threads.\nthreads (default: nothing): If parallel == true, this argument may be used to specify the Julia threads to use.\nclearmem (default: false): If true, we call clear_all_gpus_memory after the stress test.\nio (default: stdout): set the stream where the results should be printed.\n\nWhen duration is specifiec (i.e. StressTestEnforced) there is also:\n\nbatch_duration (default: ceil(Int, duration/10)): desired duration of one batch of matmuls.\n\n\n\n\n\n","category":"method"},{"location":"examples/gpuinfo/#Example:-CUDA/GPU-Information","page":"GPU Information","title":"Example: CUDA/GPU Information","text":"","category":"section"},{"location":"examples/gpuinfo/","page":"GPU Information","title":"GPU Information","text":"note: Note\nTest system: DGX with 8x A100 GPUs","category":"page"},{"location":"examples/gpuinfo/","page":"GPU Information","title":"GPU Information","text":"julia> gpus()\n8 devices:\n 0: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)\n 1: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)\n 2: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)\n 3: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)\n 4: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)\n 5: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)\n 6: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)\n 7: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)","category":"page"},{"location":"examples/gpuinfo/","page":"GPU Information","title":"GPU Information","text":"More specific information for each gpu device can be obtained with gpuinfo(deviceid::Integer).","category":"page"},{"location":"examples/gpuinfo/","page":"GPU Information","title":"GPU Information","text":"julia> gpuinfo()\nDevice: NVIDIA A100-SXM4-40GB (CuDevice(0))\nTotal amount of global memory: 42.5 GB\nNumber of CUDA cores: 6912\nNumber of multiprocessors: 108 (64 CUDA cores each)\nGPU max. clock rate: 1410 Mhz\nMemory clock rate: 1215 Mhz\nMemory bus width: 5120-bit\nL2 cache size: 41.9 MB\nMax. texture dimension sizes (1D): 131072\nMax. texture dimension sizes (2D): 131072, 65536\nMax. texture dimension sizes (3D): 16384, 16384, 16384\nMax. layered 1D texture size: 32768 (2048 layers)\nMax. layered 2D texture size: 32768, 32768 (2048 layers)\nTotal amount of constant memory: 65.5 kB\nTotal amount of shared memory per block: 49.2 kB\nTotal number of registers available per block: 65536\nWarp size: 32\nMax. number of threads per multiprocessor: 2048\nMax. number of threads per block: 1024\nMax. dimension size of a thread block (x,y,z): 1024, 1024, 64\nMax. dimension size of a grid size (x,y,z): 2147483647, 65535, 65535\nTexture alignment: 512.0 B\nMaximum memory pitch: 2.1 GB\nConcurrent copy and kernel execution: Yes with 3 copy engine(s)\nRun time limit on kernels: No\nIntegrated GPU sharing host memory: No\nSupport host page-locked memory mapping: Yes\nConcurrent kernel execution: Yes\nAlignment requirement for surfaces: Yes\nDevice has ECC support: Yes\nDevice supports Unified Addressing (UVA): Yes\nDevice supports managed memory: Yes\nDevice supports compute preemption: Yes\nSupports cooperative kernel launch: Yes\nSupports multi-device co-op kernel launch: Yes\nDevice PCI domain ID / bus ID / device ID: 0 / 7 / 0\nCompute mode: Default (multiple host threads can use ::cudaSetDevice() with device simultaneously)","category":"page"},{"location":"examples/gpuinfo/","page":"GPU Information","title":"GPU Information","text":"Peer-to-peer access information can be retrived via gpuinfo_p2p_access.","category":"page"},{"location":"examples/gpuinfo/","page":"GPU Information","title":"GPU Information","text":"julia> gpuinfo_p2p_access()\nP2P Access Supported:\n8×8 Matrix{Bool}:\n 0 1 1 1 1 1 1 1\n 1 0 1 1 1 1 1 1\n 1 1 0 1 1 1 1 1\n 1 1 1 0 1 1 1 1\n 1 1 1 1 0 1 1 1\n 1 1 1 1 1 0 1 1\n 1 1 1 1 1 1 0 1\n 1 1 1 1 1 1 1 0\n\nP2P Atomic Supported:\n8×8 Matrix{Bool}:\n 0 1 1 1 1 1 1 1\n 1 0 1 1 1 1 1 1\n 1 1 0 1 1 1 1 1\n 1 1 1 0 1 1 1 1\n 1 1 1 1 0 1 1 1\n 1 1 1 1 1 0 1 1\n 1 1 1 1 1 1 0 1\n 1 1 1 1 1 1 1 0","category":"page"},{"location":"examples/gpuinfo/","page":"GPU Information","title":"GPU Information","text":"Turns out that using CU_DEVICE_P2P_ATTRIBUTE_ACCESS_SUPPORTED or cuDeviceCanAccessPeer to query p2p access support may lead to different results (see this stackoverflow thread). In gpuinfo_p2p_access() we use both methods and, if the results were to be different, we print both matrices (not shown above).","category":"page"},{"location":"devices/v100_sxm2/#Tesla-V100-SXM2-32GB-(sm_70,-32.00-GiB)","page":"V100 SXM2","title":"Tesla V100-SXM2-32GB (sm_70, 32.00 GiB)","text":"","category":"section"},{"location":"devices/v100_sxm2/","page":"V100 SXM2","title":"V100 SXM2","text":"Benchmarks were run on the GPU cluster of the physics faculty of University of Bielefeld. ","category":"page"},{"location":"devices/v100_sxm2/","page":"V100 SXM2","title":"V100 SXM2","text":"For comparison: Datasheet by NVIDIA.","category":"page"},{"location":"devices/v100_sxm2/#Peakflops","page":"V100 SXM2","title":"Peakflops","text":"","category":"section"},{"location":"devices/v100_sxm2/#Theoretical","page":"V100 SXM2","title":"Theoretical","text":"","category":"section"},{"location":"devices/v100_sxm2/","page":"V100 SXM2","title":"V100 SXM2","text":"julia> theoretical_peakflops_gpu(; dtype=Float32, tensorcores=false);\nTheoretical Peakflops (TFLOP/s):\n ├ tensorcores: false\n ├ dtype: Float32\n └ max: 15.7\n\njulia> theoretical_peakflops_gpu(; dtype=Float64, tensorcores=false);\nTheoretical Peakflops (TFLOP/s):\n ├ tensorcores: false\n ├ dtype: Float64\n └ max: 7.8\n\njulia> theoretical_peakflops_gpu(; dtype=Float16, tensorcores=true);\nTheoretical Peakflops (TFLOP/s):\n ├ tensorcores: true\n ├ dtype: Float16\n └ max: 125.3","category":"page"},{"location":"devices/v100_sxm2/#Empirical","page":"V100 SXM2","title":"Empirical","text":"","category":"section"},{"location":"devices/v100_sxm2/","page":"V100 SXM2","title":"V100 SXM2","text":"julia> peakflops_gpu(; dtype=Float32, tensorcores=false);\nPeakflops (TFLOP/s):\n ├ tensorcores: false\n ├ dtype: Float32\n └ max: 15.5\n\njulia> peakflops_gpu(; dtype=Float64, tensorcores=false);\nPeakflops (TFLOP/s):\n ├ tensorcores: false\n ├ dtype: Float64\n └ max: 7.7\n\njulia> peakflops_gpu(; dtype=Float16, tensorcores=true);\nPeakflops (TFLOP/s):\n ├ tensorcores: true\n ├ dtype: Float16\n └ max: 116.4","category":"page"},{"location":"devices/v100_sxm2/#Memory-bandwidth","page":"V100 SXM2","title":"Memory bandwidth","text":"","category":"section"},{"location":"devices/v100_sxm2/","page":"V100 SXM2","title":"V100 SXM2","text":"julia> theoretical_memory_bandwidth();\nTheoretical Maximal Memory Bandwidth (GiB/s):\n └ max: 836.4\n\njulia> memory_bandwidth();\nMemory Bandwidth (GiB/s):\n └ max: 722.31\n\njulia> GiB(722.31) |> change_base\n~775.57 GB\n\njulia> memory_bandwidth(GiB(1.4)) |> GiB |> change_base\nMemory Bandwidth (GiB/s):\n └ max: 740.02\n~794.59 GB\n\njulia> memory_bandwidth_saxpy(; size=2^20*200) |> GiB |> change_base\nMemory Bandwidth (GiB/s):\n └ max: 754.39\n~810.02 GB","category":"page"},{"location":"devices/v100_sxm2/#Host-to-device-bandwidth","page":"V100 SXM2","title":"Host-to-device bandwidth","text":"","category":"section"},{"location":"devices/v100_sxm2/","page":"V100 SXM2","title":"V100 SXM2","text":"julia> host2device_bandwidth()\nHost <-> Device Bandwidth (GiB/s):\n └ max: 4.46\n\nHost (pinned) <-> Device Bandwidth (GiB/s):\n └ max: 12.14","category":"page"},{"location":"devices/v100_sxm2/#Peer-to-peer-bandwidth","page":"V100 SXM2","title":"Peer-to-peer bandwidth","text":"","category":"section"},{"location":"devices/v100_sxm2/","page":"V100 SXM2","title":"V100 SXM2","text":"julia> p2p_bandwidth();\nBandwidth (GiB/s):\n ├ max: 22.46\n ├ min: 0.21\n ├ avg: 17.99\n └ std_dev: 9.94\n\njulia> p2p_bandwidth_all()\n8_8 Matrix{Union{Nothing, Float64}}:\n nothing 22.5406 45.0071 22.5367 44.9879 8.11161 8.0912 8.09942\n 22.4844 nothing 22.5393 44.9706 8.11274 45.0036 8.08732 8.10026\n 44.7958 22.5406 nothing 45.0158 8.10381 8.11014 22.5406 7.39972\n 22.5301 45.0088 45.0001 nothing 7.428 8.10646 8.08204 22.5401\n 44.9897 7.14454 7.14674 7.14906 nothing 22.5371 44.9723 22.538\n 6.63411 44.9966 7.14542 7.14467 22.5371 nothing 22.5362 44.9949\n 7.75585 7.75844 22.5375 8.10314 44.9966 22.5353 nothing 44.9984\n 7.75575 7.75911 8.10116 22.5371 22.5367 44.9966 44.9914 nothing","category":"page"},{"location":"devices/v100_sxm2/#GPU-information","page":"V100 SXM2","title":"GPU information","text":"","category":"section"},{"location":"devices/v100_sxm2/","page":"V100 SXM2","title":"V100 SXM2","text":"julia> CUDA.versioninfo()\nCUDA toolkit 11.6, local installation\nNVIDIA driver 510.47.3, for CUDA 11.6\nCUDA driver 11.6\n\nLibraries: \n- CUBLAS: 11.8.1\n- CURAND: 10.2.9\n- CUFFT: 10.7.1\n- CUSOLVER: 11.3.3\n- CUSPARSE: 11.7.2\n- CUPTI: 16.0.0\n- NVML: 11.0.0+510.47.3\n- CUDNN: missing\n- CUTENSOR: missing\n\nToolchain:\n- Julia: 1.7.1\n- LLVM: 12.0.1\n- PTX ISA support: 3.2, 4.0, 4.1, 4.2, 4.3, 5.0, 6.0, 6.1, 6.3, 6.4, 6.5, 7.0\n- Device capability support: sm_35, sm_37, sm_50, sm_52, sm_53, sm_60, sm_61, sm_62, sm_70, sm_72, sm_75, sm_80\n\nEnvironment:\n- JULIA_CUDA_USE_BINARYBUILDER: false\n\n8 devices:\n 0: Tesla V100-SXM2-32GB (sm_70, 31.449 GiB / 32.000 GiB available)\n 1: Tesla V100-SXM2-32GB (sm_70, 31.745 GiB / 32.000 GiB available)\n 2: Tesla V100-SXM2-32GB (sm_70, 31.745 GiB / 32.000 GiB available)\n 3: Tesla V100-SXM2-32GB (sm_70, 31.745 GiB / 32.000 GiB available)\n 4: Tesla V100-SXM2-32GB (sm_70, 31.745 GiB / 32.000 GiB available)\n 5: Tesla V100-SXM2-32GB (sm_70, 31.745 GiB / 32.000 GiB available)\n 6: Tesla V100-SXM2-32GB (sm_70, 31.745 GiB / 32.000 GiB available)\n 7: Tesla V100-SXM2-32GB (sm_70, 31.745 GiB / 32.000 GiB available)\n\njulia> gpuinfo()\nDevice: Tesla V100-SXM2-32GB (CuDevice(0))\nTotal amount of global memory: 31.749 GiB\nNumber of CUDA cores: 5120\nNumber of multiprocessors: 80 (64 CUDA cores each)\nGPU max. clock rate: 1530 MHz\nMemory clock rate: 877 MHz\nMemory bus width: 4096-bit\nL2 cache size: 6.000 MiB\nMax. texture dimension sizes (1D): 131072\nMax. texture dimension sizes (2D): 131072, 65536\nMax. texture dimension sizes (3D): 16384, 16384, 16384\nMax. layered 1D texture size: 32768 (2048 layers)\nMax. layered 2D texture size: 32768, 32768 (2048 layers)\nTotal amount of constant memory: 64.000 KiB\nTotal amount of shared memory per block: 48.000 KiB\nTotal number of registers available per block: 65536\nWarp size: 32\nMax. number of threads per multiprocessor: 2048\nMax. number of threads per block: 1024\nMax. dimension size of a thread block (x,y,z): 1024, 1024, 64\nMax. dimension size of a grid size (x,y,z): 2147483647, 65535, 65535\nTexture alignment: 512 bytes\nMaximum memory pitch: 2.000 GiB\nConcurrent copy and kernel execution: Yes with 6 copy engine(s)\nRun time limit on kernels: No\nIntegrated GPU sharing host memory: No\nSupport host page-locked memory mapping: Yes\nConcurrent kernel execution: Yes\nAlignment requirement for surfaces: Yes\nDevice has ECC support: Yes\nDevice supports Unified Addressing (UVA): Yes\nDevice supports managed memory: Yes\nDevice supports compute preemption: Yes\nSupports cooperative kernel launch: Yes\nSupports multi-device co-op kernel launch: Yes\nDevice PCI domain ID / bus ID / device ID: 0 / 31 / 0\nCompute mode: Default (multiple host threads can use ::cudaSetDevice() with device simultaneously)\n\njulia> gpuinfo_p2p_access()\nP2P Access Supported:\n8_8 Matrix{Bool}:\n 0 1 1 1 1 1 1 1\n 1 0 1 1 1 1 1 1\n 1 1 0 1 1 1 1 1\n 1 1 1 0 1 1 1 1\n 1 1 1 1 0 1 1 1\n 1 1 1 1 1 0 1 1\n 1 1 1 1 1 1 0 1\n 1 1 1 1 1 1 1 0\n\nP2P Atomic Supported:\n8_8 Matrix{Bool}:\n 0 1 1 1 1 0 0 0\n 1 0 1 1 0 1 0 0\n 1 1 0 1 0 0 1 0\n 1 1 1 0 0 0 0 1\n 1 0 0 0 0 1 1 1\n 0 1 0 0 1 0 1 1\n 0 0 1 0 1 1 0 1\n 0 0 0 1 1 1 1 0","category":"page"},{"location":"devices/a100_sxm2/#NVIDIA-A100-SXM4-40GB-(sm_80,-39.586-GiB)","page":"A100 SXM2","title":"NVIDIA A100-SXM4-40GB (sm_80, 39.586 GiB)","text":"","category":"section"},{"location":"devices/a100_sxm2/","page":"A100 SXM2","title":"A100 SXM2","text":"Benchmarks were run on a DGX-A100 at PC2. ","category":"page"},{"location":"devices/a100_sxm2/","page":"A100 SXM2","title":"A100 SXM2","text":"For comparison: Datasheet by NVIDIA.","category":"page"},{"location":"devices/a100_sxm2/#Peakflops","page":"A100 SXM2","title":"Peakflops","text":"","category":"section"},{"location":"devices/a100_sxm2/#CUDA-cores","page":"A100 SXM2","title":"CUDA cores","text":"","category":"section"},{"location":"devices/a100_sxm2/","page":"A100 SXM2","title":"A100 SXM2","text":"julia> theoretical_peakflops_gpu(; dtype=Float32, tensorcores=false);\nTheoretical Peakflops (TFLOP/s):\n ├ tensorcores: false\n ├ dtype: Float32\n └ max: 19.5\n\njulia> theoretical_peakflops_gpu(; dtype=Float64, tensorcores=false);\nTheoretical Peakflops (TFLOP/s):\n ├ tensorcores: false\n ├ dtype: Float64\n └ max: 9.7","category":"page"},{"location":"devices/a100_sxm2/","page":"A100 SXM2","title":"A100 SXM2","text":"julia> peakflops_gpu(; dtype=Float32, tensorcores=false);\nPeakflops (TFLOP/s):\n ├ tensorcores: false\n ├ dtype: Float32\n └ max: 19.1\n\njulia> peakflops_gpu(; dtype=Float64, tensorcores=false);\nPeakflops (TFLOP/s):\n ├ tensorcores: false\n ├ dtype: Float64\n └ max: 9.6\n\njulia> peakflops_gpu(; dtype=Float16, tensorcores=false);\nPeakflops (TFLOP/s):\n ├ tensorcores: false\n ├ dtype: Float16\n └ max: 12.8","category":"page"},{"location":"devices/a100_sxm2/#Tensor-cores","page":"A100 SXM2","title":"Tensor cores","text":"","category":"section"},{"location":"devices/a100_sxm2/","page":"A100 SXM2","title":"A100 SXM2","text":"julia> theoretical_peakflops_gpu(; dtype=Int8, tensorcores=true);\nTheoretical Peakflops (TOP/s):\n ├ tensorcores: true\n ├ dtype: Int8\n └ max: 623.7\n\njulia> theoretical_peakflops_gpu(; dtype=Float16, tensorcores=true);\nTheoretical Peakflops (TFLOP/s):\n ├ tensorcores: true\n ├ dtype: Float16\n └ max: 311.9\n\njulia> theoretical_peakflops_gpu(; dtype=Float32, tensorcores=true);\nTheoretical Peakflops (TFLOP/s):\n ├ tensorcores: true\n ├ dtype: Float32\n └ max: 155.9","category":"page"},{"location":"devices/a100_sxm2/","page":"A100 SXM2","title":"A100 SXM2","text":"julia> peakflops_gpu(; dtype=Int8, tensorcores=true); # as of writing, only works with CUDA.jl#master\nPeakflops (TOP/s):\n ├ tensorcores: true\n ├ dtype: Int8\n └ max: 620.11\n\njulia> peakflops_gpu(; dtype=Float16, tensorcores=true);\nPeakflops (TFLOP/s):\n ├ tensorcores: true\n ├ dtype: Float16\n └ max: 311.2\n\njulia> peakflops_gpu(; dtype=:TensorFloat32, tensorcores=true); # as of writing, only works with Julia >= 1.8.0 and CUDA.jl PR 1419\nPeakflops (TFLOP/s):\n ├ tensorcores: true\n ├ dtype: TensorFloat32\n └ max: 155.55","category":"page"},{"location":"devices/a100_sxm2/#Memory-bandwidth","page":"A100 SXM2","title":"Memory bandwidth","text":"","category":"section"},{"location":"devices/a100_sxm2/","page":"A100 SXM2","title":"A100 SXM2","text":"julia> theoretical_memory_bandwidth();\nTheoretical Maximal Memory Bandwidth (GiB/s):\n └ max: 1448.4\n\njulia> memory_bandwidth();\nMemory Bandwidth (GiB/s):\n └ max: 1220.7\n\njulia> GiB(1220.7) |> change_base\n~1310.72 GB\n\njulia> memory_bandwidth_saxpy();\nMemory Bandwidth (GiB/s):\n └ max: 1192.09","category":"page"},{"location":"devices/a100_sxm2/#Host-to-device-bandwidth","page":"A100 SXM2","title":"Host-to-device bandwidth","text":"","category":"section"},{"location":"devices/a100_sxm2/","page":"A100 SXM2","title":"A100 SXM2","text":"julia> host2device_bandwidth()\nHost <-> Device Bandwidth (GiB/s):\n └ max: 11.84\n\nHost (pinned) <-> Device Bandwidth (GiB/s):\n └ max: 24.33","category":"page"},{"location":"devices/a100_sxm2/#Peer-to-peer-bandwidth","page":"A100 SXM2","title":"Peer-to-peer bandwidth","text":"","category":"section"},{"location":"devices/a100_sxm2/","page":"A100 SXM2","title":"A100 SXM2","text":"julia> p2p_bandwidth();\nBandwidth (GiB/s):\n ├ max: 247.32\n ├ min: 173.5\n ├ avg: 229.63\n └ std_dev: 31.67\n\njulia> p2p_bandwidth_all()\n8×8 Matrix{Union{Nothing, Float64}}:\n nothing 245.706 241.075 244.467 246.434 242.229 245.085 245.033\n 239.046 nothing 241.776 243.853 241.626 245.136 244.467 240.379\n 246.957 242.633 nothing 242.937 245.291 248.114 239.193 242.684\n 244.724 241.375 244.211 nothing 245.861 238.117 245.085 242.28\n 241.576 246.329 242.582 245.602 nothing 246.59 240.677 243.343\n 247.114 240.18 245.965 244.006 236.616 nothing 242.28 244.673\n 243.802 242.028 248.326 239.933 244.365 245.033 nothing 245.498\n 245.136 246.904 239.488 243.343 244.057 240.627 243.445 nothing","category":"page"},{"location":"devices/a100_sxm2/#GPU-information","page":"A100 SXM2","title":"GPU information","text":"","category":"section"},{"location":"devices/a100_sxm2/","page":"A100 SXM2","title":"A100 SXM2","text":"julia> CUDA.versioninfo()\nCUDA toolkit 11.5, local installation\nNVIDIA driver 495.29.5, for CUDA 11.5\nCUDA driver 11.5\n\nLibraries: \n- CUBLAS: 11.7.3\n- CURAND: 10.2.6\n- CUFFT: 10.6.0\n- CUSOLVER: 11.2.1\n- CUSPARSE: 11.7.0\n- CUPTI: 16.0.0\n- NVML: 11.0.0+495.29.5\n- CUDNN: missing\n- CUTENSOR: missing\n\nToolchain:\n- Julia: 1.7.1\n- LLVM: 12.0.1\n- PTX ISA support: 3.2, 4.0, 4.1, 4.2, 4.3, 5.0, 6.0, 6.1, 6.3, 6.4, 6.5, 7.0\n- Device capability support: sm_35, sm_37, sm_50, sm_52, sm_53, sm_60, sm_61, sm_62, sm_70, sm_72, sm_75, sm_80\n\nEnvironment:\n- JULIA_CUDA_USE_BINARYBUILDER: false\n\n8 devices:\n 0: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)\n 1: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)\n 2: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)\n 3: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)\n 4: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)\n 5: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)\n 6: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)\n 7: NVIDIA A100-SXM4-40GB (sm_80, 39.583 GiB / 39.586 GiB available)\n\njulia> gpuinfo()\nDevice: NVIDIA A100-SXM4-40GB (CuDevice(0))\nTotal amount of global memory: 42.5 GB\nNumber of CUDA cores: 6912\nNumber of multiprocessors: 108 (64 CUDA cores each)\nGPU max. clock rate: 1410 Mhz\nMemory clock rate: 1215 Mhz\nMemory bus width: 5120-bit\nL2 cache size: 41.9 MB\nMax. texture dimension sizes (1D): 131072\nMax. texture dimension sizes (2D): 131072, 65536\nMax. texture dimension sizes (3D): 16384, 16384, 16384\nMax. layered 1D texture size: 32768 (2048 layers)\nMax. layered 2D texture size: 32768, 32768 (2048 layers)\nTotal amount of constant memory: 65.5 kB\nTotal amount of shared memory per block: 49.2 kB\nTotal number of registers available per block: 65536\nWarp size: 32\nMax. number of threads per multiprocessor: 2048\nMax. number of threads per block: 1024\nMax. dimension size of a thread block (x,y,z): 1024, 1024, 64\nMax. dimension size of a grid size (x,y,z): 2147483647, 65535, 65535\nTexture alignment: 512.0 B\nMaximum memory pitch: 2.1 GB\nConcurrent copy and kernel execution: Yes with 3 copy engine(s)\nRun time limit on kernels: No\nIntegrated GPU sharing host memory: No\nSupport host page-locked memory mapping: Yes\nConcurrent kernel execution: Yes\nAlignment requirement for surfaces: Yes\nDevice has ECC support: Yes\nDevice supports Unified Addressing (UVA): Yes\nDevice supports managed memory: Yes\nDevice supports compute preemption: Yes\nSupports cooperative kernel launch: Yes\nSupports multi-device co-op kernel launch: Yes\nDevice PCI domain ID / bus ID / device ID: 0 / 7 / 0\nCompute mode: Default (multiple host threads can use ::cudaSetDevice() with device simultaneously)\n\njulia> gpuinfo_p2p_access()\nP2P Access Supported:\n8×8 Matrix{Bool}:\n 0 1 1 1 1 1 1 1\n 1 0 1 1 1 1 1 1\n 1 1 0 1 1 1 1 1\n 1 1 1 0 1 1 1 1\n 1 1 1 1 0 1 1 1\n 1 1 1 1 1 0 1 1\n 1 1 1 1 1 1 0 1\n 1 1 1 1 1 1 1 0\n\nP2P Atomic Supported:\n8×8 Matrix{Bool}:\n 0 1 1 1 1 1 1 1\n 1 0 1 1 1 1 1 1\n 1 1 0 1 1 1 1 1\n 1 1 1 0 1 1 1 1\n 1 1 1 1 0 1 1 1\n 1 1 1 1 1 0 1 1\n 1 1 1 1 1 1 0 1\n 1 1 1 1 1 1 1 0","category":"page"},{"location":"refs/stresstest_cpu/#CPU-Stresstest","page":"CPU Stress Test","title":"CPU Stresstest","text":"","category":"section"},{"location":"refs/stresstest_cpu/#Index","page":"CPU Stress Test","title":"Index","text":"","category":"section"},{"location":"refs/stresstest_cpu/","page":"CPU Stress Test","title":"CPU Stress Test","text":"Pages = [\"stresstest_cpu.md\"]\nOrder = [:function, :type]","category":"page"},{"location":"refs/stresstest_cpu/#References","page":"CPU Stress Test","title":"References","text":"","category":"section"},{"location":"refs/stresstest_cpu/","page":"CPU Stress Test","title":"CPU Stress Test","text":"Modules = [GPUInspector]\nPages = [\"stresstest_cpu.jl\"]","category":"page"},{"location":"refs/stresstest_cpu/#GPUInspector.stresstest_cpu-Tuple{Any}","page":"CPU Stress Test","title":"GPUInspector.stresstest_cpu","text":"stresstest_cpu(core_or_cores)\n\nRun a CPU stress test (matrix multiplication) on one or multiple CPU cores, as specified by the positional argument. If no argument is provided (only) the currently active CPU core will be used.\n\nKeyword arguments:\n\nduration: stress test will take about the given time in seconds.\ndtype (default: Float64): element type of the matrices\nsize (default: floor(Int, sqrt(L2_cachesize() / sizeof(dtype)))): matrices of size (size, size) will be used\nverbose (default: true): toggle printing of information\nparallel (default: true): If true, will (try to) run each CPU core test on a different Julia thread. Make sure to have enough Julia threads.\nthreads (default: nothing): If parallel == true, this argument may be used to specify the Julia threads to use.\n\n\n\n\n\n","category":"method"},{"location":"refs/data_bandwidth/#Data-Bandwidth","page":"Data Bandwidth","title":"Data Bandwidth","text":"","category":"section"},{"location":"refs/data_bandwidth/#Index","page":"Data Bandwidth","title":"Index","text":"","category":"section"},{"location":"refs/data_bandwidth/","page":"Data Bandwidth","title":"Data Bandwidth","text":"Pages = [\"data_bandwidth.md\"]\nOrder = [:function, :type]","category":"page"},{"location":"refs/data_bandwidth/#References","page":"Data Bandwidth","title":"References","text":"","category":"section"},{"location":"refs/data_bandwidth/","page":"Data Bandwidth","title":"Data Bandwidth","text":"Modules = [GPUInspector]\nPages = [\"stubs_membw.jl\", \"stubs_host2device_bandwidth.jl\", \"stubs_p2p_bandwidth.jl\"]","category":"page"},{"location":"refs/data_bandwidth/#GPUInspector.memory_bandwidth-Tuple{}","page":"Data Bandwidth","title":"GPUInspector.memory_bandwidth","text":"memory_bandwidth(; kwargs...)\n\nTries to estimate the peak memory bandwidth of a GPU in GiB/s by measuring the time it takes to perform a memcpy of a certain amount of data (as specified by memsize).\n\nKeyword arguments:\n\nmemsize (default: GiB(0.5)): memory size to be used\ndevice (default: e.g. CUDA.device()): GPU device to be used.\ndtype (default: Cchar): element type of the vectors.\nverbose (default: true): toggle printing.\nio (default: stdout): set the stream where the results should be printed.\n\nSee also: memory_bandwidth_scaling.\n\n\n\n\n\n","category":"method"},{"location":"refs/data_bandwidth/#GPUInspector.memory_bandwidth_saxpy-Tuple{}","page":"Data Bandwidth","title":"GPUInspector.memory_bandwidth_saxpy","text":"Tries to estimate the peak memory bandwidth of a GPU in GiB/s by measuring the time it takes to perform a SAXPY, i.e. a * x[i] + y[i].\n\nKeyword arguments:\n\ndevice (default: e.g. CUDA.device()): GPU device to be used.\ndtype (default: Float32): element type of the vectors.\nsize (default: 2^20 * 10): length of the vectors.\nnbench (default: 5): number of measurements to be performed the best of which is used for the GiB/s computation.\nverbose (default: true): toggle printing.\nio (default: stdout): set the stream where the results should be printed.\n\nSee also: memory_bandwidth_saxpy_scaling.\n\n\n\n\n\n","category":"method"},{"location":"refs/data_bandwidth/#GPUInspector.memory_bandwidth_saxpy_scaling-Tuple{}","page":"Data Bandwidth","title":"GPUInspector.memory_bandwidth_saxpy_scaling","text":"memory_bandwidth_saxpy_scaling() -> sizes, bandwidths\n\nMeasures the memory bandwidth (via memory_bandwidth_saxpy) as a function of vector length. If verbose=true (default), displays a unicode plot. Returns the considered lengths and GiB/s. For further options, see memory_bandwidth_saxpy.\n\n\n\n\n\n","category":"method"},{"location":"refs/data_bandwidth/#GPUInspector.memory_bandwidth_scaling-Tuple{}","page":"Data Bandwidth","title":"GPUInspector.memory_bandwidth_scaling","text":"memory_bandwidth_scaling() -> datasizes, bandwidths\n\nMeasures the memory bandwidth (via memory_bandwidth) as a function of data size. If verbose=true (default), displays a unicode plot. Returns the considered data sizes and GiB/s. For further options, see memory_bandwidth.\n\n\n\n\n\n","category":"method"},{"location":"refs/data_bandwidth/#GPUInspector.theoretical_memory_bandwidth-Tuple{}","page":"Data Bandwidth","title":"GPUInspector.theoretical_memory_bandwidth","text":"theoretical_memory_bandwidth(; device, verbose)\n\nEstimates the theoretical maximal GPU memory bandwidth in GiB/s.\n\n\n\n\n\n","category":"method"},{"location":"refs/data_bandwidth/#GPUInspector.host2device_bandwidth-Tuple{}","page":"Data Bandwidth","title":"GPUInspector.host2device_bandwidth","text":"host2device_bandwidth(; kwargs...)\n\nPerforms a host-to-device memory copy benchmark (time measurement) and returns the host-to-device bandwidth estimate (in GiB/s) derived from it.\n\nKeyword arguments:\n\nmemsize (default: GiB(0.5)): memory size to be used\nnbench (default: 10): number of time measurements (i.e. p2p memcopies)\nverbose (default: true): set to false to turn off any printing.\nstats (default: false): when true shows statistical information about the benchmark.\ntimes (default: false): toggle printing of measured times.\ndtype (default: Cchar): used data type.\nio (default: stdout): set the stream where the results should be printed.\n\nExamples:\n\nhost2device_bandwidth()\nhost2device_bandwidth(MiB(1024))\nhost2device_bandwidth(KiB(20_000); dtype=Int32)\n\n\n\n\n\n","category":"method"},{"location":"refs/data_bandwidth/#GPUInspector.p2p_bandwidth-Tuple{}","page":"Data Bandwidth","title":"GPUInspector.p2p_bandwidth","text":"p2p_bandwidth(; kwargs...) Performs a peer-to-peer memory copy benchmark (time measurement) and returns an inter-gpu memory bandwidth estimate (in GiB/s) derived from it.\n\nKeyword arguments:\n\nmemsize (default: B(40_000_000)): memory size to be used\nsrc (default: 0): source device\ndst (default: 1): destination device\nnbench (default: 5): number of time measurements (i.e. p2p memcopies)\nverbose (default: true): set to false to turn off any printing.\nhist (default: false): when true, a UnicodePlots-based histogram is printed.\ntimes (default: false): toggle printing of measured times.\nalternate (default: false): alternate src and dst, i.e. copy data back and forth.\ndtype (default: Float32): see alloc_mem.\nio (default: stdout): set the stream where the results should be printed.\n\nExamples:\n\np2p_bandwidth()\np2p_bandwidth(MiB(1024))\np2p_bandwidth(KiB(20_000); dtype=Int32)\n\n\n\n\n\n","category":"method"},{"location":"refs/data_bandwidth/#GPUInspector.p2p_bandwidth_all-Tuple{}","page":"Data Bandwidth","title":"GPUInspector.p2p_bandwidth_all","text":"p2p_bandwidth_all(; kwargs...)\n\nRun p2p_bandwidth for all combinations of available devices. Returns a matrix with the p2p memory bandwidth estimates.\n\n\n\n\n\n","category":"method"},{"location":"refs/data_bandwidth/#GPUInspector.p2p_bandwidth_bidirectional-Tuple{}","page":"Data Bandwidth","title":"GPUInspector.p2p_bandwidth_bidirectional","text":"Same as p2p_bandwidth but measures the bidirectional bandwidth (copying data back and forth).\n\n\n\n\n\n","category":"method"},{"location":"refs/data_bandwidth/#GPUInspector.p2p_bandwidth_bidirectional_all-Tuple{}","page":"Data Bandwidth","title":"GPUInspector.p2p_bandwidth_bidirectional_all","text":"Same as p2p_bandwidth_all but measures the bidirectional bandwidth (copying data back and forth).\n\n\n\n\n\n","category":"method"},{"location":"examples/data_bandwidth/#Example:-Data-Bandwidth","page":"Data Bandwidth","title":"Example: Data Bandwidth","text":"","category":"section"},{"location":"examples/data_bandwidth/","page":"Data Bandwidth","title":"Data Bandwidth","text":"note: Note\nTest system: DGX with 8x A100 GPUs (+ NVSwitch)","category":"page"},{"location":"examples/data_bandwidth/#Memory-Bandwidth","page":"Data Bandwidth","title":"Memory Bandwidth","text":"","category":"section"},{"location":"examples/data_bandwidth/","page":"Data Bandwidth","title":"Data Bandwidth","text":"You can benchmark the memory bandwidth of a GPU, i.e. how much data can be loaded within a second, with the function memory_bandwidth. Under the hood, this function transfers a certain amount of data (memcpy), times the operation, and computes the resulting memory bandwidth.","category":"page"},{"location":"examples/data_bandwidth/","page":"Data Bandwidth","title":"Data Bandwidth","text":"julia> memory_bandwidth();\nMemory Bandwidth (GiB/s):\n └ max: 1219.18","category":"page"},{"location":"examples/data_bandwidth/","page":"Data Bandwidth","title":"Data Bandwidth","text":"Note that this is reasonably close to the expected theoretical maximal memory bandwidth of our A100 test device.","category":"page"},{"location":"examples/data_bandwidth/","page":"Data Bandwidth","title":"Data Bandwidth","text":"julia> theoretical_memory_bandwidth();\nTheoretical Maximal Memory Bandwidth (GiB/s):\n └ max: 1448.4","category":"page"},{"location":"examples/data_bandwidth/","page":"Data Bandwidth","title":"Data Bandwidth","text":"The scaling (as a function of data size) can be assessed with memory_bandwidth_scaling.","category":"page"},{"location":"examples/data_bandwidth/","page":"Data Bandwidth","title":"Data Bandwidth","text":"julia> memory_bandwidth_scaling()\n\n ⠀⠀⠀⠀Peak: 1225.3 GiB/s (size = 1.0 GiB)⠀⠀⠀ \n ┌────────────────────────────────────────┐ \n 2000 │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀│ \n │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⠤⠔⠒⠉⠉⢸│ \n GiB/s │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⠒⠉⠀⠀⠀⠀⠀⠀⢸│ \n │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡎⠀⠀⠀⠀⠀⠀⠀⠀⢸│ \n │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡜⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ \n │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢰⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ \n │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ \n │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡎⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ \n │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡠⠜⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ \n 0 │⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⣀⡠⠔⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ \n └────────────────────────────────────────┘ \n ⠀2⁰⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀2³⁰⠀ \n ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀data size⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ","category":"page"},{"location":"examples/data_bandwidth/#SAXPY","page":"Data Bandwidth","title":"SAXPY","text":"","category":"section"},{"location":"examples/data_bandwidth/","page":"Data Bandwidth","title":"Data Bandwidth","text":"Note that we also provide similar functions that are based on a SAXPY streaming kernel.","category":"page"},{"location":"examples/data_bandwidth/","page":"Data Bandwidth","title":"Data Bandwidth","text":"julia> memory_bandwidth_saxpy();\nMemory Bandwidth (GiB/s):\n └ max: 1192.09\n\njulia> memory_bandwidth_saxpy_scaling()\n\n ⠀⠀Peak: 1275.34 GiB/s (size = 300.0 MiB)⠀⠀ \n ┌────────────────────────────────────────┐ \n 1280 │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡀⠀⠀⠀⢀│ \n │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⣀⠤⠤⠔⠒⠙⠊⠊⠉⢹│ \n │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⠤⠤⠔⠒⠊⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ \n │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⠔⠒⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ \n │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡠⠔⠊⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ \n │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡠⠔⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ \n │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡠⠊⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ \n GiB/s │⠀⠀⠀⠀⠀⠀⠀⠀⠀⡠⠊⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ \n │⠀⠀⠀⠀⠀⠀⠀⡰⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ \n │⠀⠀⠀⠀⠀⠀⡜⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ \n │⠀⠀⠀⠀⢀⠎⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ \n │⠀⠀⠀⡠⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ \n │⠀⠀⡰⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ \n │⢀⠜⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ \n 1190 │⠎⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸│ \n └────────────────────────────────────────┘ \n ⠀2²³⸱³²¹⁹⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀2²⁸⸱²²⁸⁸⠀ \n ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀vector length⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ","category":"page"},{"location":"examples/data_bandwidth/#Host-to-Device-Bandwidth","page":"Data Bandwidth","title":"Host-to-Device Bandwidth","text":"","category":"section"},{"location":"examples/data_bandwidth/","page":"Data Bandwidth","title":"Data Bandwidth","text":"How much data can be transferred per second from the host memory to the GPU (and the other way around)?","category":"page"},{"location":"examples/data_bandwidth/","page":"Data Bandwidth","title":"Data Bandwidth","text":"julia> host2device_bandwidth()\nHost <-> Device Bandwidth (GiB/s):\n └ max: 11.79\n\nHost (pinned) <-> Device Bandwidth (GiB/s):\n └ max: 24.33","category":"page"},{"location":"examples/data_bandwidth/#Peer-to-Peer-Bandwidth","page":"Data Bandwidth","title":"Peer-to-Peer Bandwidth","text":"","category":"section"},{"location":"examples/data_bandwidth/#Unidirectional","page":"Data Bandwidth","title":"Unidirectional","text":"","category":"section"},{"location":"examples/data_bandwidth/","page":"Data Bandwidth","title":"Data Bandwidth","text":"julia> p2p_bandwidth();\nMemsize: 38.147 MiB\n\nBandwidth (GiB/s):\n ├ max: 247.32\n ├ min: 173.5\n ├ avg: 229.63\n └ std_dev: 31.67\n\njulia> p2p_bandwidth_all()\n8×8 Matrix{Union{Nothing, Float64}}:\n nothing 245.706 241.075 244.467 246.434 242.229 245.085 245.033\n 239.046 nothing 241.776 243.853 241.626 245.136 244.467 240.379\n 246.957 242.633 nothing 242.937 245.291 248.114 239.193 242.684\n 244.724 241.375 244.211 nothing 245.861 238.117 245.085 242.28\n 241.576 246.329 242.582 245.602 nothing 246.59 240.677 243.343\n 247.114 240.18 245.965 244.006 236.616 nothing 242.28 244.673\n 243.802 242.028 248.326 239.933 244.365 245.033 nothing 245.498\n 245.136 246.904 239.488 243.343 244.057 240.627 243.445 nothing","category":"page"},{"location":"examples/data_bandwidth/","page":"Data Bandwidth","title":"Data Bandwidth","text":"According to NVIDIA, the theoretical maximal peer-to-peer bandwidth for our A100 with NVSwitch is 300GB/s ≈ 279GiB/s, which is in reasonable agreement with our findings.","category":"page"},{"location":"examples/data_bandwidth/#Bidirectional","page":"Data Bandwidth","title":"Bidirectional","text":"","category":"section"},{"location":"examples/data_bandwidth/","page":"Data Bandwidth","title":"Data Bandwidth","text":"julia> p2p_bandwidth_bidirectional();\nMemsize: 38.147 MiB\n\nBandwidth (GiB/s):\n ├ max: 450.36\n ├ min: 448.66\n ├ avg: 449.69\n └ std_dev: 0.49\n\njulia> p2p_bandwidth_bidirectional_all()\n8×8 Matrix{Union{Nothing, Float64}}:\n nothing 456.631 453.133 454.946 453.67 453.953 455.06 454.662\n 453.67 nothing 454.01 450.329 455.345 453.02 454.691 455.203\n 453.981 451.53 nothing 452.344 453.868 454.747 452.232 454.208\n 453.557 451.979 449.883 nothing 454.01 455.288 450.189 454.691\n 452.429 454.293 445.094 454.151 nothing 453.472 451.474 453.981\n 454.89 454.066 453.84 453.84 451.194 nothing 453.274 451.53\n 453.925 453.02 454.293 456.459 451.839 451.951 nothing 455.032\n 454.208 416.936 454.265 435.947 452.035 437.836 451.895 nothing","category":"page"},{"location":"refs/monitoring/#GPU-Monitoring","page":"GPU Monitoring","title":"GPU Monitoring","text":"","category":"section"},{"location":"refs/monitoring/#Index","page":"GPU Monitoring","title":"Index","text":"","category":"section"},{"location":"refs/monitoring/","page":"GPU Monitoring","title":"GPU Monitoring","text":"Pages = [\"monitoring.md\"]\nOrder = [:function, :type]","category":"page"},{"location":"refs/monitoring/#References","page":"GPU Monitoring","title":"References","text":"","category":"section"},{"location":"refs/monitoring/","page":"GPU Monitoring","title":"GPU Monitoring","text":"Modules = [GPUInspector]\nPages = [\"stubs_monitoring.jl\", \"monitoring.jl\", \"monitoring_io.jl\"]","category":"page"},{"location":"refs/monitoring/#GPUInspector.livemonitor_powerusage-Tuple","page":"GPU Monitoring","title":"GPUInspector.livemonitor_powerusage","text":"livemonitor_powerusage(duration) -> times, powerusage\n\nMonitor the power usage of GPU(s) (in Watts) over a given time period, as specified by duration (in seconds). Returns the (relative) times as a Vector{Float64} and the power usage as a Vector{Vector{Float64}}.\n\nFor general keyword arguments, see livemonitor_something.\n\n\n\n\n\n","category":"method"},{"location":"refs/monitoring/#GPUInspector.livemonitor_something-Tuple{}","page":"GPU Monitoring","title":"GPUInspector.livemonitor_something","text":"livemonitor_something(f, duration) -> times, values\n\nMonitor some property of GPU(s), as specified through the function f, over a given time period, as specified by duration (in seconds). Returns the (relative) times as a Vector{Float64} and the temperatures as a Vector{Vector{Float64}}.\n\nThe function f will be called on a vector of devices and should return a vector of Float64 values.\n\nKeyword arguments:\n\nfreq (default: 1): polling rate in Hz.\ndevices (default: e.g. NVML.devices()): Devices to monitor.\nplot (default: false): Create a unicode plot after the monitoring.\nliveplot (default: false): Create and update a unicode plot during the monitoring. Use optional ylims to specify fixed y limits.\ntitle (default: \"\"): Title used in unicode plots.\nylabel (default: \"Values\"): y label used in unicode plots.\n\nSee: livemonitor_temperature, livemonitor_powerusage\n\n\n\n\n\n","category":"method"},{"location":"refs/monitoring/#GPUInspector.livemonitor_temperature-Tuple","page":"GPU Monitoring","title":"GPUInspector.livemonitor_temperature","text":"livemonitor_temperature(duration) -> times, temperatures\n\nMonitor the temperature of GPU(s) over a given time period, as specified by duration (in seconds). Returns the (relative) times as a Vector{Float64} and the temperatures as a Vector{Vector{Float64}}.\n\nFor general keyword arguments, see livemonitor_something.\n\n\n\n\n\n","category":"method"},{"location":"refs/monitoring/#GPUInspector.monitoring_start-Tuple{}","page":"GPU Monitoring","title":"GPUInspector.monitoring_start","text":"monitoring_start(; devices, kwargs...)\n\nStart monitoring of GPU temperature, utilization, power usage, etc.\n\nKeyword arguments:\n\nfreq (default: 1): polling rate in Hz.\ndevices (default: e.g. CUDA.devices()): GPU devices to monitor.\nthread (default: Threads.nthreads()): id of the Julia thread that should run the monitoring.\nverbose (default: true): toggle verbose output.\n\nSee also monitoring_stop.\n\n\n\n\n\n","category":"method"},{"location":"refs/monitoring/#GPUInspector.monitoring_stop-Tuple{}","page":"GPU Monitoring","title":"GPUInspector.monitoring_stop","text":"monitoring_stop(; verbose=true) -> results\n\nStops the GPU monitoring and returns the measured values.\n\nSee also monitoring_start and plot_monitoring_results.\n\n\n\n\n\n","category":"method"},{"location":"refs/monitoring/#GPUInspector.savefig_monitoring_results","page":"GPU Monitoring","title":"GPUInspector.savefig_monitoring_results","text":"savefig_monitoring_results(r::MonitoringResults, symbols=keys(r.results); ext=:pdf)\n\nSave plots of the quantities specified through symbols of a MonitoringResults object to disk. Note: Only available if CairoMakie.jl is loaded next to GPUInspector.jl.\n\n\n\n\n\n","category":"function"},{"location":"refs/monitoring/#GPUInspector.MonitoringResults","page":"GPU Monitoring","title":"GPUInspector.MonitoringResults","text":"Struct to hold the results of monitoring. This includes the time points (times), the monitored devices (devices), as well as a dictionary holding the (vector-)values of different quantities (identified by symbols) at each of the time points.\n\n\n\n\n\n","category":"type"},{"location":"refs/monitoring/#GPUInspector.ismonitoring-Tuple{}","page":"GPU Monitoring","title":"GPUInspector.ismonitoring","text":"Checks if we are currently monitoring.\n\n\n\n\n\n","category":"method"},{"location":"refs/monitoring/#GPUInspector.plot_monitoring_results","page":"GPU Monitoring","title":"GPUInspector.plot_monitoring_results","text":"plot_monitoring_results(r::MonitoringResults, symbols=keys(r.results))\n\nPlot the quantities specified through symbols of a MonitoringResults object. Will generate a textual in-terminal / in-logfile plot using UnicodePlots.jl.\n\n\n\n\n\n","category":"function"},{"location":"refs/monitoring/#GPUInspector.load_monitoring_results-Tuple{String}","page":"GPU Monitoring","title":"GPUInspector.load_monitoring_results","text":"Given an HDF5 file created with save_monitoring_results, restore the saved monitoring results (i.e. output of monitoring_stop).\n\n\n\n\n\n","category":"method"},{"location":"refs/monitoring/#GPUInspector.save_monitoring_results-Tuple{String, MonitoringResults}","page":"GPU Monitoring","title":"GPUInspector.save_monitoring_results","text":"save_monitoring_results(filename::String, r::MonitoringResults; overwrite=false)\n\nStore the given MonitoringResults (output of monitoring_stop) to disk as an HDF5 file with name filename.\n\n\n\n\n\n","category":"method"},{"location":"refs/peakflops_gpu/#Peakflops","page":"Peakflops","title":"Peakflops","text":"","category":"section"},{"location":"refs/peakflops_gpu/#Index","page":"Peakflops","title":"Index","text":"","category":"section"},{"location":"refs/peakflops_gpu/","page":"Peakflops","title":"Peakflops","text":"Pages = [\"peakflops_gpu.md\"]\nOrder = [:function, :type]","category":"page"},{"location":"refs/peakflops_gpu/#References","page":"Peakflops","title":"References","text":"","category":"section"},{"location":"refs/peakflops_gpu/","page":"Peakflops","title":"Peakflops","text":"Modules = [GPUInspector]\nPages = [\"stubs_peakflops_gpu.jl\"]","category":"page"},{"location":"refs/peakflops_gpu/#GPUInspector.peakflops_gpu-Tuple{}","page":"Peakflops","title":"GPUInspector.peakflops_gpu","text":"peakflops_gpu(; kwargs...)\n\nTries to estimate the peak performance of a GPU in TFLOP/s\n\n\n\n\n\n","category":"method"},{"location":"refs/peakflops_gpu/#GPUInspector.theoretical_peakflops_gpu-Tuple{}","page":"Peakflops","title":"GPUInspector.theoretical_peakflops_gpu","text":"Estimates the theoretical peak performance of a GPU device in TFLOP/s.\n\nKeyword arguments:\n\nverbose (default: true): toggle printing of information\ndevice (default: e.g. CUDA.device()): GPU device to be analyzed\ndtype (default: Float32): element type of the matrices\nio (default: stdout): set the stream where the results should be printed.\n\n\n\n\n\n","category":"method"},{"location":"refs/utility/#Utility","page":"Utility","title":"Utility","text":"","category":"section"},{"location":"refs/utility/#Index","page":"Utility","title":"Index","text":"","category":"section"},{"location":"refs/utility/","page":"Utility","title":"Utility","text":"Pages = [\"utility.md\"]\nOrder = [:function, :type]","category":"page"},{"location":"refs/utility/#References","page":"Utility","title":"References","text":"","category":"section"},{"location":"refs/utility/","page":"Utility","title":"Utility","text":"Modules = [GPUInspector]\nPages = [\"utility.jl\", \"utility_unroll.jl\"]","category":"page"},{"location":"refs/utility/#GPUInspector.clear_all_gpus_memory-Tuple{}","page":"Utility","title":"GPUInspector.clear_all_gpus_memory","text":"Reclaim the unused memory of all available GPUs.\n\n\n\n\n\n","category":"method"},{"location":"refs/utility/#GPUInspector.@unroll-Tuple{Any, Any}","page":"Utility","title":"GPUInspector.@unroll","text":"@unroll N expr\n\nTakes a for loop as expr and informs the LLVM unroller to unroll it N times, if it is safe to do so.\n\n\n\n\n\n","category":"macro"},{"location":"refs/utility/#GPUInspector.@unroll-Tuple{Any}","page":"Utility","title":"GPUInspector.@unroll","text":"@unroll expr Takes a for loop as expr and informs the LLVM unroller to fully unroll it, if it is safe to do so and the loop count is known.\n\n\n\n\n\n","category":"macro"},{"location":"devices/geforce_gtx_1650/#NVIDIA-GeForce-GTX-1650-(sm_75,-3.787-GiB)","page":"GeForce GTX 1650","title":"NVIDIA GeForce GTX 1650 (sm_75, 3.787 GiB)","text":"","category":"section"},{"location":"devices/geforce_gtx_1650/","page":"GeForce GTX 1650","title":"GeForce GTX 1650","text":"Some information by NVIDIA is available here. Some external numbers may be found here.","category":"page"},{"location":"devices/geforce_gtx_1650/#Peakflops","page":"GeForce GTX 1650","title":"Peakflops","text":"","category":"section"},{"location":"devices/geforce_gtx_1650/#Theoretical","page":"GeForce GTX 1650","title":"Theoretical","text":"","category":"section"},{"location":"devices/geforce_gtx_1650/","page":"GeForce GTX 1650","title":"GeForce GTX 1650","text":"julia> theoretical_peakflops_gpu(; dtype=Float32);\nTheoretical Peakflops (TFLOP/s):\n └ max: 2.9\n\njulia> theoretical_peakflops_gpu(; dtype=Float64);\nTheoretical Peakflops (TFLOP/s):\n └ max: 1.5","category":"page"},{"location":"devices/geforce_gtx_1650/#Empirical","page":"GeForce GTX 1650","title":"Empirical","text":"","category":"section"},{"location":"devices/geforce_gtx_1650/","page":"GeForce GTX 1650","title":"GeForce GTX 1650","text":"julia> peakflops_gpu_fmas(; dtype=Float32); # too large?\nPeakflops (TFLOP/s):\n └ max: 3.41\n\njulia> peakflops_gpu_fmas(; dtype=Float64); # too low?\nPeakflops (TFLOP/s):\n └ max: 0.11","category":"page"},{"location":"devices/geforce_gtx_1650/#Memory-bandwidth","page":"GeForce GTX 1650","title":"Memory bandwidth","text":"","category":"section"},{"location":"devices/geforce_gtx_1650/","page":"GeForce GTX 1650","title":"GeForce GTX 1650","text":"julia> memory_bandwidth();\nMemory Bandwidth (GiB/s):\n └ max: 159.33\n\njulia> GiB(159.33) |> change_base\n~171.08 GB","category":"page"},{"location":"devices/geforce_gtx_1650/","page":"GeForce GTX 1650","title":"GeForce GTX 1650","text":"julia> host2device_bandwidth()\nMemsize: 512.000 MiB\nGPU: CuDevice(0) - NVIDIA GeForce GTX 1650\n\nHost <-> Device Bandwidth (GiB/s):\n └ max: 12.06\n\nHost (pinned) <-> Device Bandwidth (GiB/s):\n └ max: 12.05\n\nDevice <-> Device (same device) Bandwidth (GiB/s):\n └ max: 153.0","category":"page"},{"location":"devices/geforce_gtx_1650/#GPU-information","page":"GeForce GTX 1650","title":"GPU information","text":"","category":"section"},{"location":"devices/geforce_gtx_1650/","page":"GeForce GTX 1650","title":"GeForce GTX 1650","text":"julia> CUDA.versioninfo()\nCUDA toolkit 11.6, artifact installation\nNVIDIA driver 470.86.0, for CUDA 11.4\nCUDA driver 11.4\n\nLibraries: \n- CUBLAS: 11.8.1\n- CURAND: 10.2.9\n- CUFFT: 10.7.0\n- CUSOLVER: 11.3.2\n- CUSPARSE: 11.7.1\n- CUPTI: 16.0.0\n- NVML: 11.0.0+470.86\n- CUDNN: 8.30.2 (for CUDA 11.5.0)\n- CUTENSOR: 1.4.0 (for CUDA 11.5.0)\n\nToolchain:\n- Julia: 1.7.2\n- LLVM: 12.0.1\n- PTX ISA support: 3.2, 4.0, 4.1, 4.2, 4.3, 5.0, 6.0, 6.1, 6.3, 6.4, 6.5, 7.0\n- Device capability support: sm_35, sm_37, sm_50, sm_52, sm_53, sm_60, sm_61, sm_62, sm_70, sm_72, sm_75, sm_80\n\n1 device:\n 0: NVIDIA GeForce GTX 1650 (sm_75, 2.240 GiB / 3.787 GiB available)\n\njulia> gpuinfo()\nDevice: NVIDIA GeForce GTX 1650 (CuDevice(0))\nTotal amount of global memory: 3.787 GiB\nNumber of CUDA cores: 896\nNumber of multiprocessors: 14 (64 CUDA cores each)\nGPU max. clock rate: 1620 MHz\nMemory clock rate: 6001 MHz\nMemory bus width: 128-bit\nL2 cache size: 1024.000 KiB\nMax. texture dimension sizes (1D): 131072\nMax. texture dimension sizes (2D): 131072, 65536\nMax. texture dimension sizes (3D): 16384, 16384, 16384\nMax. layered 1D texture size: 32768 (2048 layers)\nMax. layered 2D texture size: 32768, 32768 (2048 layers)\nTotal amount of constant memory: 64.000 KiB\nTotal amount of shared memory per block: 48.000 KiB\nTotal number of registers available per block: 65536\nWarp size: 32\nMax. number of threads per multiprocessor: 1024\nMax. number of threads per block: 1024 \nMax. dimension size of a thread block (x,y,z): 1024, 1024, 64\nMax. dimension size of a grid size (x,y,z): 2147483647, 65535, 65535\nTexture alignment: 512 bytes\nMaximum memory pitch: 2.000 GiB\nConcurrent copy and kernel execution: Yes with 3 copy engine(s)\nRun time limit on kernels: Yes\nIntegrated GPU sharing host memory: No \nSupport host page-locked memory mapping: Yes\nConcurrent kernel execution: Yes\nAlignment requirement for surfaces: Yes\nDevice has ECC support: No\nDevice supports Unified Addressing (UVA): Yes\nDevice supports managed memory: Yes\nDevice supports compute preemption: Yes\nSupports cooperative kernel launch: Yes\nSupports multi-device co-op kernel launch: Yes\nDevice PCI domain ID / bus ID / device ID: 0 / 1 / 0\nCompute mode: Default (multiple host threads can use ::cudaSetDevice() with device simultaneously)","category":"page"},{"location":"refs/UnitPrefixedBytes/#Unit-Prefixed-Bytes","page":"Unit-Prefixed Bytes","title":"Unit-Prefixed Bytes","text":"","category":"section"},{"location":"refs/UnitPrefixedBytes/#Index","page":"Unit-Prefixed Bytes","title":"Index","text":"","category":"section"},{"location":"refs/UnitPrefixedBytes/","page":"Unit-Prefixed Bytes","title":"Unit-Prefixed Bytes","text":"Pages = [\"UnitPrefixedBytes.md\"]\nOrder = [:function, :type]","category":"page"},{"location":"refs/UnitPrefixedBytes/#References","page":"Unit-Prefixed Bytes","title":"References","text":"","category":"section"},{"location":"refs/UnitPrefixedBytes/","page":"Unit-Prefixed Bytes","title":"Unit-Prefixed Bytes","text":"Modules = [GPUInspector]\nPages = [\"UnitPrefixedBytes.jl\"]","category":"page"},{"location":"refs/UnitPrefixedBytes/#GPUInspector.B","page":"Unit-Prefixed Bytes","title":"GPUInspector.B","text":"Bytes\n\n\n\n\n\n","category":"type"},{"location":"refs/UnitPrefixedBytes/#GPUInspector.GB","page":"Unit-Prefixed Bytes","title":"GPUInspector.GB","text":"Gigabytes, i.e. 10^9 = 1000^3 bytes\n\n\n\n\n\n","category":"type"},{"location":"refs/UnitPrefixedBytes/#GPUInspector.GiB","page":"Unit-Prefixed Bytes","title":"GPUInspector.GiB","text":"Gibibytes, i.e. 2^30 = 1024^3 bytes\n\n\n\n\n\n","category":"type"},{"location":"refs/UnitPrefixedBytes/#GPUInspector.KB","page":"Unit-Prefixed Bytes","title":"GPUInspector.KB","text":"Kilobytes, i.e. 10^3 = 1000 bytes\n\n\n\n\n\n","category":"type"},{"location":"refs/UnitPrefixedBytes/#GPUInspector.KiB","page":"Unit-Prefixed Bytes","title":"GPUInspector.KiB","text":"Kibibytes, i.e. 2^10 = 1024 bytes\n\n\n\n\n\n","category":"type"},{"location":"refs/UnitPrefixedBytes/#GPUInspector.MB","page":"Unit-Prefixed Bytes","title":"GPUInspector.MB","text":"Megabytes, i.e. 10^6 = 1000^2 bytes\n\n\n\n\n\n","category":"type"},{"location":"refs/UnitPrefixedBytes/#GPUInspector.MiB","page":"Unit-Prefixed Bytes","title":"GPUInspector.MiB","text":"Mebibytes, i.e. 2^20 = 1024^2 bytes\n\n\n\n\n\n","category":"type"},{"location":"refs/UnitPrefixedBytes/#GPUInspector.TB","page":"Unit-Prefixed Bytes","title":"GPUInspector.TB","text":"Terabytes, i.e. 10^12 = 1000^4 bytes\n\n\n\n\n\n","category":"type"},{"location":"refs/UnitPrefixedBytes/#GPUInspector.TiB","page":"Unit-Prefixed Bytes","title":"GPUInspector.TiB","text":"Tebibytes, i.e. 2^40 = 1024^4 bytes\n\n\n\n\n\n","category":"type"},{"location":"refs/UnitPrefixedBytes/#GPUInspector.UnitPrefixedBytes","page":"Unit-Prefixed Bytes","title":"GPUInspector.UnitPrefixedBytes","text":"Abstract type representing an amount of data, i.e. a certain number of bytes, with a unit prefix (also \"metric prefix\"). Examples include the SI prefixes, like KB, MB, and GB, but also the binary prefixes (ISO/IEC 80000), like KiB, MiB, and GiB.\n\nSee https://en.wikipedia.org/wiki/Binary_prefix for more information.\n\n\n\n\n\n","category":"type"},{"location":"refs/UnitPrefixedBytes/#GPUInspector.bytes-Tuple{Number}","page":"Unit-Prefixed Bytes","title":"GPUInspector.bytes","text":"bytes(x::Number)\n\nReturns an appropriate UnitPrefixedBytes object, representing the number of bytes.\n\nNote: This function is type unstable by construction!\n\nSee simplify for what \"appropriate\" means here.\n\n\n\n\n\n","category":"method"},{"location":"refs/UnitPrefixedBytes/#GPUInspector.bytes-Tuple{UnitPrefixedBytes}","page":"Unit-Prefixed Bytes","title":"GPUInspector.bytes","text":"bytes(x::UnitPrefixedBytes)\n\nReturn the number of bytes (without prefix) as Float64.\n\n\n\n\n\n","category":"method"},{"location":"refs/UnitPrefixedBytes/#GPUInspector.change_base-Tuple{T} where T<:UnitPrefixedBytes","page":"Unit-Prefixed Bytes","title":"GPUInspector.change_base","text":"Toggle between\n\nBase 10, SI prefixes, i.e. factors of 1000\nBase 2, ISO/IEC prefixes, i.e. factors of 1024\n\nExample:\n\njulia> change_base(KB(13))\n~12.7 KiB\n\njulia> change_base(KiB(13))\n~13.31 KB\n\n\n\n\n\n","category":"method"},{"location":"refs/UnitPrefixedBytes/#GPUInspector.simplify-Tuple{B}","page":"Unit-Prefixed Bytes","title":"GPUInspector.simplify","text":"simplify(x::UnitPrefixedBytes[; base])\n\nGiven a UnitPrefixedBytes number x, finds a more appropriate UnitPrefixedBytes that represents the same number of bytes but with a smaller value.\n\nThe optional keyword argument base can be used to switch between base 2, i.e. ISO/IEC prefixes (default), and base 10. Allowed values are 2, 10, :SI, :ISO, and :IEC.\n\nNote: This function is type unstable by construction!\n\nExample:\n\njulia> simplify(B(40_000_000))\n~38.15 MiB\n\njulia> simplify(B(40_000_000); base=10)\n40.0 MB\n\n\n\n\n\n","category":"method"},{"location":"refs/gpuinfo/#GPU-Information","page":"GPU Information","title":"GPU Information","text":"","category":"section"},{"location":"refs/gpuinfo/","page":"GPU Information","title":"GPU Information","text":"Heavily inspired by the cuda sample deviceQueryDrv.","category":"page"},{"location":"refs/gpuinfo/#Index","page":"GPU Information","title":"Index","text":"","category":"section"},{"location":"refs/gpuinfo/","page":"GPU Information","title":"GPU Information","text":"Pages = [\"gpuinfo.md\"]\nOrder = [:function, :type]","category":"page"},{"location":"refs/gpuinfo/#References","page":"GPU Information","title":"References","text":"","category":"section"},{"location":"refs/gpuinfo/","page":"GPU Information","title":"GPU Information","text":"Modules = [GPUInspector]\nPages = [\"stubs_gpuinfo.jl\"]","category":"page"},{"location":"refs/gpuinfo/#GPUInspector.gpuinfo-Tuple{}","page":"GPU Information","title":"GPUInspector.gpuinfo","text":"gpuinfo([device]; kwargs...)\n\nPrint out detailed information about the GPU with the given device id.\n\nNote: Device ids start at zero!\n\n\n\n\n\n","category":"method"},{"location":"refs/gpuinfo/#GPUInspector.gpuinfo_p2p_access-Tuple{}","page":"GPU Information","title":"GPUInspector.gpuinfo_p2p_access","text":"Query peer-to-peer (i.e. inter-GPU) access support.\n\n\n\n\n\n","category":"method"},{"location":"refs/gpuinfo/#GPUInspector.gpus-Tuple{}","page":"GPU Information","title":"GPUInspector.gpus","text":"List the available GPUs.\n\n\n\n\n\n","category":"method"},{"location":"refs/gpuinfo/#GPUInspector.ngpus-Tuple{}","page":"GPU Information","title":"GPUInspector.ngpus","text":"Returns the number of available GPUs for the given/current backend\n\n\n\n\n\n","category":"method"},{"location":"examples/peakflops_gpu/#Example:-Peakflops","page":"Peakflops","title":"Example: Peakflops","text":"","category":"section"},{"location":"examples/peakflops_gpu/","page":"Peakflops","title":"Peakflops","text":"note: Note\nTest system: NVIDIA A100 GPU","category":"page"},{"location":"examples/peakflops_gpu/#CUDA-cores","page":"Peakflops","title":"CUDA cores","text":"","category":"section"},{"location":"examples/peakflops_gpu/","page":"Peakflops","title":"Peakflops","text":"We assess the peak performance of the CUDA cores by executing many pure FMAs (fused multiply-adds) on the CUDA cores in parallel.","category":"page"},{"location":"examples/peakflops_gpu/","page":"Peakflops","title":"Peakflops","text":"Theoretically, we expect the performances below, which we compute from the clock rate, number of CUDA cores etc. The same numbers are given by NVIDIA, e.g. here or here, or Wikipedia, e.g. here.","category":"page"},{"location":"examples/peakflops_gpu/","page":"Peakflops","title":"Peakflops","text":"julia> theoretical_peakflops_gpu(; dtype=Float32, tensorcores=false);\nTheoretical Peakflops (TFLOP/s):\n ├ tensorcores: false\n ├ dtype: Float32\n └ max: 19.5\n\njulia> theoretical_peakflops_gpu(; dtype=Float64, tensorcores=false);\nTheoretical Peakflops (TFLOP/s):\n ├ tensorcores: false\n ├ dtype: Float64\n └ max: 9.7","category":"page"},{"location":"examples/peakflops_gpu/","page":"Peakflops","title":"Peakflops","text":"In good agreement, we find the following empirical numbers.","category":"page"},{"location":"examples/peakflops_gpu/","page":"Peakflops","title":"Peakflops","text":"julia> peakflops_gpu(; dtype=Float32, tensorcores=false);\nPeakflops (TFLOP/s):\n ├ tensorcores: false\n ├ dtype: Float32\n └ max: 19.1\n\njulia> peakflops_gpu(; dtype=Float64, tensorcores=false);\nPeakflops (TFLOP/s):\n ├ tensorcores: false\n ├ dtype: Float64\n └ max: 9.6\n\njulia> peakflops_gpu(; dtype=Float16, tensorcores=false);\nPeakflops (TFLOP/s):\n ├ tensorcores: false\n ├ dtype: Float16\n └ max: 12.8","category":"page"},{"location":"examples/peakflops_gpu/#Tensor-Cores","page":"Peakflops","title":"Tensor Cores","text":"","category":"section"},{"location":"examples/peakflops_gpu/","page":"Peakflops","title":"Peakflops","text":"We assess the peak performance of the Tensor cores by executing many pure WMMAs (warp-level matrix-multiply-and-accumulate), see, e.g., here and here), on the Tensor cores in parallel.","category":"page"},{"location":"examples/peakflops_gpu/","page":"Peakflops","title":"Peakflops","text":"Theoretically, we expect the performances below, which we compute from the clock rate, number of Tensor cores etc. The same numbers are given by NVIDIA, e.g. here or here, or on Wikipedia, e.g. here.","category":"page"},{"location":"examples/peakflops_gpu/","page":"Peakflops","title":"Peakflops","text":"julia> theoretical_peakflops_gpu(; dtype=Int8, tensorcores=true);\nTheoretical Peakflops (TOP/s):\n ├ tensorcores: true\n ├ dtype: Int8\n └ max: 623.7\n\njulia> theoretical_peakflops_gpu(; dtype=Float16, tensorcores=true);\nTheoretical Peakflops (TFLOP/s):\n ├ tensorcores: true\n ├ dtype: Float16\n └ max: 311.9\n\njulia> theoretical_peakflops_gpu(; dtype=Float32, tensorcores=true);\nTheoretical Peakflops (TFLOP/s):\n ├ tensorcores: true\n ├ dtype: Float32\n └ max: 155.9","category":"page"},{"location":"examples/peakflops_gpu/","page":"Peakflops","title":"Peakflops","text":"Empirically, we find the following numbers in good agreement.","category":"page"},{"location":"examples/peakflops_gpu/","page":"Peakflops","title":"Peakflops","text":"julia> peakflops_gpu(; dtype=Int8, tensorcores=true); # as of writing, only works with CUDA.jl#master\nPeakflops (TOP/s):\n ├ tensorcores: true\n ├ dtype: Int8\n └ max: 620.1\n\njulia> peakflops_gpu(; dtype=Float16, tensorcores=true);\nPeakflops (TFLOP/s):\n ├ tensorcores: true\n ├ dtype: Float16\n └ max: 311.2\n\njulia> peakflops_gpu(; dtype=:TensorFloat32, tensorcores=true); # as of writing, requires Julia 1.8 and https://github.com/JuliaGPU/CUDA.jl/pull/1419\nPeakflops (TFLOP/s):\n ├ tensorcores: true\n ├ dtype: TensorFloat32\n └ max: 155.5","category":"page"},{"location":"examples/gpustresstest/#Example:-GPU-Stress-Test","page":"GPU Stress Test","title":"Example: GPU Stress Test","text":"","category":"section"},{"location":"examples/gpustresstest/","page":"GPU Stress Test","title":"GPU Stress Test","text":"note: Note\nTest system: DGX with 8x A100 GPUs","category":"page"},{"location":"examples/gpustresstest/#Single-GPU","page":"GPU Stress Test","title":"Single GPU","text":"","category":"section"},{"location":"examples/gpustresstest/","page":"GPU Stress Test","title":"GPU Stress Test","text":"julia> using GPUInspector, CUDA\n\njulia> stresstest(; devices=CUDA.device(), duration=10)\n[ Info: Will try to run for approximately 10 seconds on each GPU.\n[ Info: Running StressTest{Float32} on Julia thread 2 and CuDevice(0).\n[ Info: Ran 11241 iterations on CuDevice(0).\n[ Info: Clearing GPU memory.\n[ Info: Took 10.0 seconds to run the tests.","category":"page"},{"location":"examples/gpustresstest/#Multiple-GPUs","page":"GPU Stress Test","title":"Multiple GPUs","text":"","category":"section"},{"location":"examples/gpustresstest/","page":"GPU Stress Test","title":"GPU Stress Test","text":"First, make sure to run Julia with enough threads (more or equal to the number of GPU devices). Then, to run a GPU Stress test, use stresstest.","category":"page"},{"location":"examples/gpustresstest/","page":"GPU Stress Test","title":"GPU Stress Test","text":"In the following example, I started Julia with julia -t 20 (why not? 😄).","category":"page"},{"location":"examples/gpustresstest/","page":"GPU Stress Test","title":"GPU Stress Test","text":"julia> using GPUInspector, CUDA\n\njulia> stresstest(; devices=CUDA.devices(), duration=10)\n[ Info: Will try to run for approximately 10 seconds on each GPU.\n[ Info: Running StressTest{Float32} on Julia thread 2 and CuDevice(0).\n[ Info: Running StressTest{Float32} on Julia thread 8 and CuDevice(6).\n[ Info: Running StressTest{Float32} on Julia thread 4 and CuDevice(2).\n[ Info: Running StressTest{Float32} on Julia thread 7 and CuDevice(5).\n[ Info: Running StressTest{Float32} on Julia thread 5 and CuDevice(3).\n[ Info: Running StressTest{Float32} on Julia thread 9 and CuDevice(7).\n[ Info: Running StressTest{Float32} on Julia thread 6 and CuDevice(4).\n[ Info: Running StressTest{Float32} on Julia thread 3 and CuDevice(1).\n[ Info: Ran 11222 iterations on CuDevice(3).\n[ Info: Ran 11203 iterations on CuDevice(6).\n[ Info: Ran 11227 iterations on CuDevice(2).\n[ Info: Ran 11230 iterations on CuDevice(4).\n[ Info: Ran 11231 iterations on CuDevice(1).\n[ Info: Ran 11199 iterations on CuDevice(0).\n[ Info: Ran 11215 iterations on CuDevice(5).\n[ Info: Ran 11246 iterations on CuDevice(7).\n[ Info: Clearing GPU memory.\n[ Info: Took 10.0 seconds to run the tests.","category":"page"},{"location":"examples/gpustresstest/#Monitoring","page":"GPU Stress Test","title":"Monitoring","text":"","category":"section"},{"location":"examples/gpustresstest/","page":"GPU Stress Test","title":"GPU Stress Test","text":"To verify that the GPUs are actually busy you could use external tools like, e.g., nvidia-smi. However, we also provide built-in monitoring tools.","category":"page"},{"location":"examples/gpustresstest/","page":"GPU Stress Test","title":"GPU Stress Test","text":"julia> using GPUInspector, CUDA\n\njulia> monitoring_start() \n[ Info: Spawning monitoring on Julia thread 20.\n\njulia> stresstest(; devices=CUDA.devices(), duration=10) # all devices, 10 seconds\n[ Info: Will try to run for approximately 10 seconds on each GPU.\n[ Info: Running StressTest{Float32} on Julia thread 4 and CuDevice(2).\n[ Info: Running StressTest{Float32} on Julia thread 2 and CuDevice(0).\n[ Info: Running StressTest{Float32} on Julia thread 6 and CuDevice(4).\n[ Info: Running StressTest{Float32} on Julia thread 3 and CuDevice(1).\n[ Info: Running StressTest{Float32} on Julia thread 9 and CuDevice(7).\n[ Info: Running StressTest{Float32} on Julia thread 7 and CuDevice(5).\n[ Info: Running StressTest{Float32} on Julia thread 5 and CuDevice(3).\n[ Info: Running StressTest{Float32} on Julia thread 8 and CuDevice(6).\n[ Info: Ran 11215 iterations on CuDevice(2).\n[ Info: Ran 11241 iterations on CuDevice(6).\n[ Info: Ran 11261 iterations on CuDevice(1).\n[ Info: Ran 11236 iterations on CuDevice(5).\n[ Info: Ran 11263 iterations on CuDevice(4).\n[ Info: Ran 11261 iterations on CuDevice(3).\n[ Info: Ran 11270 iterations on CuDevice(7).\n[ Info: Ran 11241 iterations on CuDevice(0).\n[ Info: Clearing GPU memory.\n[ Info: Took 10.0 seconds to run the tests.\n\njulia> results = monitoring_stop();\n[ Info: Stopping monitoring and fetching results...","category":"page"},{"location":"examples/gpustresstest/","page":"GPU Stress Test","title":"GPU Stress Test","text":"Here, results is a MonitoringResults object with time series information about GPU utilization, temperature, and more. See monitoring_stop for details.","category":"page"},{"location":"examples/gpustresstest/","page":"GPU Stress Test","title":"GPU Stress Test","text":"You can use save_monitoring_results to save the results on disk. We also provide plot_monitoring_results to visualize the results, which should give you an output like this.","category":"page"},{"location":"examples/gpustresstest/","page":"GPU Stress Test","title":"GPU Stress Test","text":"julia> plot_monitoring_results(results)\n\n ⠀⠀⠀⠀⠀⠀⠀⠀⠀GPU Utilization (Compute)\n ┌────────────────────────────────────────┐ \n 105 │⠀⠀⠀⠀⡤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⠤⢤⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 0: NVIDIA A100-SXM4-40GB\n │⠀⠀⠀⢰⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⡆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 1: NVIDIA A100-SXM4-40GB\n │⠀⠀⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 2: NVIDIA A100-SXM4-40GB\n │⠀⠀⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 3: NVIDIA A100-SXM4-40GB\n │⠀⠀⠀⡸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 4: NVIDIA A100-SXM4-40GB\n │⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 5: NVIDIA A100-SXM4-40GB\n │⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 6: NVIDIA A100-SXM4-40GB\n U [%] │⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 7: NVIDIA A100-SXM4-40GB\n │⠀⠀⢰⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⡆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n │⠀⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n │⠀⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n │⠀⠀⡸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n │⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n │⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n 0 │⣀⣀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⣀⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n └────────────────────────────────────────┘ \n ⠀0⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀20⠀ \n ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀Time [s]⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ \n \n ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀GPU Temperature⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ \n ┌────────────────────────────────────────┐ \n 63 │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 0: NVIDIA A100-SXM4-40GB\n │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⡠⠤⠤⠤⠒⢒⣲⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 1: NVIDIA A100-SXM4-40GB\n │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⡠⠤⠒⠊⣁⠤⣤⠤⠶⠮⠛⠛⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 2: NVIDIA A100-SXM4-40GB\n │⠀⠀⠀⠀⠀⠀⠀⡠⣒⣉⣉⣭⣓⠭⠛⠋⠉⠉⠉⠀⠀⠀⠀⠀⠀⢻⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 3: NVIDIA A100-SXM4-40GB \n │⠀⠀⠀⠀⠀⢠⣮⠮⠊⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⡠⢤⣤⠸⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 4: NVIDIA A100-SXM4-40GB \n │⠀⠀⠀⠀⢠⡿⠁⠀⠀⠀⠀⠀⢀⡠⠤⣤⠤⠶⠮⠛⠋⣉⠭⠥⠤⡇⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 5: NVIDIA A100-SXM4-40GB \n │⠀⠀⠀⢠⣷⠁⠀⣠⣒⠶⠒⢉⣉⢭⣛⣒⣒⡪⠝⠛⠛⠊⠉⠉⠉⣿⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 6: NVIDIA A100-SXM4-40GB \n T [C] │⠀⠀⠀⣼⠃⢠⠊⣁⠤⠔⠚⠓⠊⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢻⡞⠲⡤⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 7: NVIDIA A100-SXM4-40GB \n │⠀⠀⢠⡏⢠⣿⠋⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠸⣧⠀⠈⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n │⠀⠀⣼⢡⣷⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n │⠤⠤⡟⣸⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠸⡳⡢⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n │⠉⠛⢇⡟⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠪⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n │⠀⠀⣸⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n │⠒⠒⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n 28 │⠉⠉⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n └────────────────────────────────────────┘ \n ⠀0⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀20⠀ \n ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀Time [s]⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ \n \n ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀GPU Power Usage⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ \n ┌────────────────────────────────────────┐ \n 340 │⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣀⣀⣀⣀⣀⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 0: NVIDIA A100-SXM4-40GB\n │⠀⠀⠀⠀⣤⣠⣤⣤⣶⡶⠶⠶⠶⠾⠿⠿⣶⣿⣷⣶⣶⣶⣒⣒⣺⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 1: NVIDIA A100-SXM4-40GB\n │⠀⠀⠀⢰⡯⡭⡿⠟⠛⠛⠛⠛⠛⠛⠉⠉⠉⠉⠉⠉⠉⠉⠉⠉⠹⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 2: NVIDIA A100-SXM4-40GB\n │⠀⠀⠀⢸⠋⡜⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 3: NVIDIA A100-SXM4-40GB\n │⠀⠀⠀⢸⡰⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣧⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 4: NVIDIA A100-SXM4-40GB\n │⠀⠀⠀⣾⠇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢻⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 5: NVIDIA A100-SXM4-40GB\n │⠀⠀⠀⣿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 6: NVIDIA A100-SXM4-40GB\n P [W] │⠀⠀⠀⣿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ GPU 7: NVIDIA A100-SXM4-40GB\n │⠀⠀⢠⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n │⠀⠀⢸⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n │⠀⠀⢸⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣧⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n │⠀⠀⣼⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢹⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n │⠀⠀⡟⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n │⠀⠀⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⣶⣶⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n 56 │⣶⣶⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠙⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀│ \n └────────────────────────────────────────┘ \n ⠀0⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀20⠀ \n ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀Time [s]⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ","category":"page"},{"location":"examples/gpustresstest/","page":"GPU Stress Test","title":"GPU Stress Test","text":"Note that for convenience stresstest also takes a monitoring keyword argument which can be used to automatically trigger the starting and stopping of the monitoring.","category":"page"},{"location":"refs/backends/#Backends","page":"Backends","title":"Backends","text":"","category":"section"},{"location":"refs/backends/#Index","page":"Backends","title":"Index","text":"","category":"section"},{"location":"refs/backends/","page":"Backends","title":"Backends","text":"Pages = [\"backends.md\"]\nOrder = [:function, :type]","category":"page"},{"location":"refs/backends/#References","page":"Backends","title":"References","text":"","category":"section"},{"location":"refs/backends/","page":"Backends","title":"Backends","text":"Modules = [GPUInspector]\nPages = [\"backends.jl\", \"stubs_general.jl\"]","category":"page"},{"location":"refs/backends/#GPUInspector.AMDBackend","page":"Backends","title":"GPUInspector.AMDBackend","text":"Represents the AMD backend (AMDGPU.jl)\n\n\n\n\n\n","category":"type"},{"location":"refs/backends/#GPUInspector.Backend","page":"Backends","title":"GPUInspector.Backend","text":"Abstract Backend for GPUInspector\n\n\n\n\n\n","category":"type"},{"location":"refs/backends/#GPUInspector.NVIDIABackend","page":"Backends","title":"GPUInspector.NVIDIABackend","text":"Represents the NVIDIA backend (CUDA.jl)\n\n\n\n\n\n","category":"type"},{"location":"refs/backends/#GPUInspector.NoBackend","page":"Backends","title":"GPUInspector.NoBackend","text":"Indicates the absence of any GPU backends\n\n\n\n\n\n","category":"type"},{"location":"refs/backends/#GPUInspector.backend!-Tuple{Backend}","page":"Backends","title":"GPUInspector.backend!","text":"backend!(b::Backend)\n\n\nSet the GPU backend (manually). Note that the corresponding backend package (e.g. CUDA.jl) must already be loaded in the active Julia session (otherwise an exception is thrown).\n\n\n\n\n\n","category":"method"},{"location":"refs/backends/#GPUInspector.backend-Tuple{}","page":"Backends","title":"GPUInspector.backend","text":"Returns the currently active GPU backend\n\n\n\n\n\n","category":"method"},{"location":"refs/backends/#GPUInspector.backendinfo-Tuple{}","page":"Backends","title":"GPUInspector.backendinfo","text":"Query information about a specific backend, e.g., what functionality the backend currently supports.\n\n\n\n\n\n","category":"method"},{"location":"refs/backends/#GPUInspector.clear_gpu_memory-Tuple{}","page":"Backends","title":"GPUInspector.clear_gpu_memory","text":"clear_gpu_memory(; device, gc)\n\nReclaim the unused memory of a GPU\n\n\n\n\n\n","category":"method"},{"location":"refs/backends/#GPUInspector.device-Tuple{}","page":"Backends","title":"GPUInspector.device","text":"Return the current device of the active backend.\n\n\n\n\n\n","category":"method"},{"location":"refs/backends/#GPUInspector.devices-Tuple{}","page":"Backends","title":"GPUInspector.devices","text":"Return the devices of the active backend.\n\n\n\n\n\n","category":"method"},{"location":"refs/backends/#GPUInspector.functional-Tuple{}","page":"Backends","title":"GPUInspector.functional","text":"Check if GPUInspector, and its GPU backend (e.g. CUDA.jl), is available and functional. 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Some features might not be available in Julia 1.6!","category":"page"},{"location":"#Getting-Started","page":"GPUInspector","title":"Getting Started","text":"","category":"section"},{"location":"#Backends","page":"GPUInspector","title":"Backends","text":"","category":"section"},{"location":"","page":"GPUInspector","title":"GPUInspector","text":"GPUInspector itself only provides limited functionality. Most of its features come to live - through package extensions - when you load a GPU backend, like CUDA.jl. Hence, most of the time, you want to add one of these backends next to GPUInspector to your Julia environment and then run","category":"page"},{"location":"","page":"GPUInspector","title":"GPUInspector","text":"using GPUInspector\nusing CUDA # loading a GPU backend triggers the pkg extension to load","category":"page"},{"location":"","page":"GPUInspector","title":"GPUInspector","text":"Note that you can check the current backend with the backend() function (and set it manually via backend!).","category":"page"},{"location":"#Examples","page":"GPUInspector","title":"Examples","text":"","category":"section"},{"location":"","page":"GPUInspector","title":"GPUInspector","text":"Pages = map(file -> joinpath(\"examples\", file), readdir(\"examples\"))\nDepth = 1","category":"page"},{"location":"refs/cuda_extension/#CUDA-Extension","page":"CUDA Extension","title":"CUDA Extension","text":"","category":"section"},{"location":"refs/cuda_extension/#Index","page":"CUDA Extension","title":"Index","text":"","category":"section"},{"location":"refs/cuda_extension/","page":"CUDA Extension","title":"CUDA Extension","text":"Pages = [\"cuda_extension.md\"]\nOrder = [:function, :type]","category":"page"},{"location":"refs/cuda_extension/#References","page":"CUDA Extension","title":"References","text":"","category":"section"},{"location":"refs/cuda_extension/","page":"CUDA Extension","title":"CUDA Extension","text":"Modules = [CUDAExt]\nPages = [\"cuda_wrappers.jl\", \"peakflops_gpu_fmas.jl\", \"peakflops_gpu_wmmas.jl\", \"peakflops_gpu_matmul.jl\", \"stresstest.jl\", \"stresstests.jl\", \"utility.jl\", \"membw.jl\", \"host2device_bandwidth.jl\", \"p2p_bandwidth.jl\", \"gpuinfo.jl\", \"monitoring.jl\"]","category":"page"},{"location":"refs/cuda_extension/#CUDAExt.get_gpu_utilization","page":"CUDA Extension","title":"CUDAExt.get_gpu_utilization","text":"get_gpu_utilization(device=CUDA.device())\n\nGet the current utilization of the given CUDA device in percent.\n\n\n\n\n\n","category":"function"},{"location":"refs/cuda_extension/#CUDAExt.get_gpu_utilizations","page":"CUDA Extension","title":"CUDAExt.get_gpu_utilizations","text":"get_gpu_utilizations(devices=CUDA.devices())\n\nGet the current utilization of the given CUDA devices in percent.\n\n\n\n\n\n","category":"function"},{"location":"refs/cuda_extension/#CUDAExt.get_power_usage-Tuple{CUDA.NVML.Device}","page":"CUDA Extension","title":"CUDAExt.get_power_usage","text":"get_power_usage(device=CUDA.device())\n\nGet current power usage of the given CUDA device in Watts.\n\n\n\n\n\n","category":"method"},{"location":"refs/cuda_extension/#CUDAExt.get_power_usages","page":"CUDA Extension","title":"CUDAExt.get_power_usages","text":"get_power_usages(devices=CUDA.devices())\n\nGet current power usage of the given CUDA devices in Watts.\n\n\n\n\n\n","category":"function"},{"location":"refs/cuda_extension/#CUDAExt.get_temperature","page":"CUDA Extension","title":"CUDAExt.get_temperature","text":"get_temperature(device=CUDA.device())\n\nGet current temperature of the given CUDA device in degrees Celsius.\n\n\n\n\n\n","category":"function"},{"location":"refs/cuda_extension/#CUDAExt.get_temperatures","page":"CUDA Extension","title":"CUDAExt.get_temperatures","text":"get_temperatures(devices=CUDA.devices())\n\nGet current temperature of the given CUDA devices in degrees Celsius.\n\n\n\n\n\n","category":"function"},{"location":"refs/cuda_extension/#CUDAExt.gpuid","page":"CUDA Extension","title":"CUDAExt.gpuid","text":"Get GPU index of the given device.\n\nNote: GPU indices start with zero.\n\n\n\n\n\n","category":"function"},{"location":"refs/cuda_extension/#CUDAExt._kernel_fma-NTuple{4, Any}","page":"CUDA Extension","title":"CUDAExt._kernel_fma","text":"Dummy kernel doing _kernel_fma_nfmas() many FMAs (default: 100_000).\n\n\n\n\n\n","category":"method"},{"location":"refs/cuda_extension/#CUDAExt._peakflops_gpu_fmas-Tuple{}","page":"CUDA Extension","title":"CUDAExt._peakflops_gpu_fmas","text":"_peakflops_gpu_fmas(; size::Integer=5_000_000, dtype=Float32, nbench=5, nkernel=5, device=CUDA.device(), verbose=true)\n\nTries to estimate the peak performance of a GPU in TFLOP/s by measuring the time it takes to perform _kernel_fma_nfmas() * size many FMAs on CUDA cores.\n\nKeyword arguments:\n\ndevice (default: CUDA.device()): CUDA device to be used.\ndtype (default: Float32): element type of the matrices.\nsize (default: 5_000_000): length of vectors.\nnkernel (default: 5): number of kernel calls that make up one benchmarking sample.\nnbench (default: 5): number of measurements to be performed the best of which is used for the TFLOP/s computation.\nverbose (default: true): toggle printing.\nio (default: stdout): set the stream where the results should be printed.\n\n\n\n\n\n","category":"method"},{"location":"refs/cuda_extension/#CUDAExt._peakflops_gpu_wmmas-Tuple{}","page":"CUDA Extension","title":"CUDAExt._peakflops_gpu_wmmas","text":"_peakflops_gpu_wmmas()\n\nTries to estimate the peak performance of a GPU in TFLOP/s by measuring the time it takes to perform _kernel_wmma_nwmmas() many WMMAs on Tensor Cores.\n\nKeyword arguments:\n\ndevice (default: CUDA.device()): CUDA device to be used.\ndtype (default: Float16): element type of the matrices. 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In the latter case, we estimate how long a synced matmul takes and set niter accordingly.\n\n\n\n\n\n","category":"type"},{"location":"refs/cuda_extension/#CUDAExt.StressTestStoreResults","page":"CUDA Extension","title":"CUDAExt.StressTestStoreResults","text":"GPU stress test (matrix multiplications) in which we store all matmul results and try to run as many iterations as possible for a certain memory limit (default: 90% of free memory).\n\nThis stress test is somewhat inspired by gpu-burn by Ville Timonen.\n\n\n\n\n\n","category":"type"},{"location":"refs/cuda_extension/#CUDAExt.alloc_mem-Tuple{UnitPrefixedBytes}","page":"CUDA Extension","title":"CUDAExt.alloc_mem","text":"alloc_mem(memsize::UnitPrefixedBytes; devs=(CUDA.device(),), dtype=Float32)\n\nAllocates memory on the devices whose IDs are provided via devs. Returns a vector of memory handles (i.e. CuArrays).\n\nExamples:\n\nalloc_mem(MiB(1024)) # allocate on the currently active device\nalloc_mem(B(40_000_000); devs=(0,1)) # allocate on GPU0 and GPU1\n\n\n\n\n\n","category":"method"},{"location":"refs/cuda_extension/#CUDAExt.hastensorcores","page":"CUDA Extension","title":"CUDAExt.hastensorcores","text":"Checks whether the given CuDevice has Tensor Cores.\n\n\n\n\n\n","category":"function"},{"location":"refs/cuda_extension/#CUDAExt.toggle_tensorcoremath","page":"CUDA Extension","title":"CUDAExt.toggle_tensorcoremath","text":"toggle_tensorcoremath([enable::Bool; verbose=true])\n\nSwitches the CUDA.math_mode between CUDA.FAST_MATH (enable=true) and CUDA.DEFAULT_MATH (enable=false). For matmuls of CuArray{Float32}s, this should have the effect of using/enabling and not using/disabling tensor cores. 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