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Update index.md to fix a typo (#333)
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| title: "Dristhi" | ||
| title: "Drishti" | ||
| authors: [jeanlucabez, "Suren Byna"] | ||
| author_notes: ["Research Scientist, Lawrence Berkeley Lab", " The Ohio State University (OSU)"] | ||
| tags: ["osre24", "uc", "LBNL", "data science", "visualization", "performance analysis"] | ||
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@@ -9,7 +9,7 @@ lastmod: 2024-01-30T10:15:00-07:00 | |
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| [Drishti](https://github.com/hpc-io/drishti) is a novel interactive web-based analysis framework to visualize I/O traces, highlight bottlenecks, and help understand the I/O behavior of scientific applications. Drishti aims to fill the gap between the trace collection, analysis, and tuning phases. The framework contains an interactive I/O trace analysis component for end-users to visually inspect their applications' I/O behavior, focusing on areas of interest and getting a clear picture of common root causes of I/O performance bottlenecks. Based on the automatic detection of I/O performance bottlenecks, our framework maps numerous common and well-known bottlenecks and their solution recommendations that can be implemented by users. | ||
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| ### Dristhi / Server-side Visualization Service | ||
| ### Drishti / Server-side Visualization Service | ||
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| The proposed work will include investigating and building server-side solutions to support the visualization of larger I/O traces and logs, while integrating with the existing analysis, reports, and recommendations. | ||
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@@ -19,7 +19,7 @@ The proposed work will include investigating and building server-side solutions | |
| - **Size:** Large (350 hours) | ||
| - **Mentors:** {{% mention jeanlucabez %}} and [Suren Byna](mailto:[email protected]) | ||
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| ### Dristhi / Visualization and Analysis of AI-based Applications | ||
| ### Drishti / Visualization and Analysis of AI-based Applications | ||
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| Drishti to handle metrics from non-MPI applications, specifically, AI/ML codes and applications. This work entails adapting the existing framework, heuristics, and recommendations to support metrics collected from AI/ML workloads. | ||
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