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iter_bench.nim
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iter_bench.nim
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# MIT License
# Copyright (c) 2018 Mamy André-Ratsimbazafy
import
./tensor,
./iter01_global,
./iter02_pertensor,
./iter03_global_triot,
./iter05_fusedpertensor,
./metadata
import math, random, times, stats, strformat
proc warmup() =
# Warmup - make sure cpu is on max perf
let start = cpuTime()
var foo = 123
for i in 0 ..< 300_000_000:
foo += i*i mod 456
foo = foo mod 789
# Compiler shouldn't optimize away the results as cpuTime rely on sideeffects
let stop = cpuTime()
echo &"Warmup: {stop - start:>4.4f} s, result {foo} (displayed to avoid compiler optimizing warmup away)"
template printStats(name: string) {.dirty.} =
echo "\n" & name & " - float64"
echo &"Collected {stats.n} samples in {global_stop - global_start:>4.3f} seconds"
echo &"Average time: {stats.mean * 1000 :>4.3f}ms"
echo &"Stddev time: {stats.standardDeviationS * 1000 :>4.3f}ms"
echo &"Min time: {stats.min * 1000 :>4.3f}ms"
echo &"Max time: {stats.max * 1000 :>4.3f}ms"
echo "\nDisplay output[[0,0]] to make sure it's not optimized away"
echo output[[0, 0]] # Prevents compiler from optimizing stuff away
template bench(name: string, body: untyped) {.dirty.}=
var output = newTensor[float64](a.shape)
block: # Actual bench
var stats: RunningStat
let global_start = cpuTime()
for _ in 0 ..< nb_samples:
let start = cpuTime()
body
let stop = cpuTime()
stats.push stop - start
let global_stop = cpuTime()
printStats(name)
proc mainBench_global(a, b, c: Tensor, nb_samples: int) =
bench("Global reference iteration"):
materialize(output, a, b, c):
a + b - sin c
proc mainBench_perTensor(a, b, c: Tensor, nb_samples: int) =
bench("Per tensor reference iteration"):
forEach o in output, x in a, y in b, z in c:
o = x + y - sin z
proc mainBench_global_triot(a, b, c: Tensor, nb_samples: int) =
bench("Global TRIOT iteration"):
triotForEach o in output, x in a, y in b, z in c:
o = x + y - sin z
proc mainBench_fusedperTensor(a, b, c: Tensor, nb_samples: int) =
bench("Fused per tensor reference iteration"):
fusedForEach o in output, x in a, y in b, z in c:
o = x + y - sin z
when isMainModule:
randomize(42) # For reproducibility
warmup()
block: # All contiguous
let
a = randomTensor([1000, 1000], 1.0)
b = randomTensor([1000, 1000], 1.0)
c = randomTensor([1000, 1000], 1.0)
mainBench_global(a, b, c, 1000)
mainBench_perTensor(a, b, c, 1000)
mainBench_global_triot(a, b, c, 1000)
mainBench_fusedperTensor(a, b, c, 1000)
block: # Non C contiguous (but no Fortran contiguous fast-path)
let
a = randomTensor([100, 10000], 1.0)
b = randomTensor([10000, 100], 1.0).transpose
c = randomTensor([10000, 100], 1.0).transpose
mainBench_global(a, b, c, 1000)
mainBench_perTensor(a, b, c, 1000)
mainBench_global_triot(a, b, c, 1000)
mainBench_fusedperTensor(a, b, c, 1000)
# Warmup: 1.1933 s, result 224 (displayed to avoid compiler optimizing warmup away)
############################################
# Global reference iteration - float64
# Collected 1000 samples in 21.296 seconds
# Average time: 21.292ms
# Stddev time: 0.426ms
# Min time: 21.139ms
# Max time: 28.544ms
# Display output[[0,0]] to make sure it's not optimized away
# -0.41973403633413
# Per tensor reference iteration - float64
# Collected 1000 samples in 8.646 seconds
# Average time: 8.642ms
# Stddev time: 0.195ms
# Min time: 8.543ms
# Max time: 11.056ms
# Display output[[0,0]] to make sure it's not optimized away
# -0.41973403633413
# Global TRIOT iteration - float64
# Collected 1000 samples in 19.514 seconds
# Average time: 19.510ms
# Stddev time: 0.407ms
# Min time: 19.349ms
# Max time: 25.644ms
# Display output[[0,0]] to make sure it's not optimized away
# -0.41973403633413
# Fused per tensor reference iteration - float64
# Collected 1000 samples in 8.645 seconds
# Average time: 8.641ms
# Stddev time: 0.253ms
# Min time: 8.531ms
# Max time: 13.235ms
# Display output[[0,0]] to make sure it's not optimized away
# -0.41973403633413
############################################
# Global reference iteration - float64
# Collected 1000 samples in 49.648 seconds
# Average time: 49.644ms
# Stddev time: 2.316ms
# Min time: 47.169ms
# Max time: 78.987ms
# Display output[[0,0]] to make sure it's not optimized away
# 1.143903810108473
# Per tensor reference iteration - float64
# Collected 1000 samples in 36.795 seconds
# Average time: 36.790ms
# Stddev time: 1.175ms
# Min time: 34.855ms
# Max time: 49.315ms
# Display output[[0,0]] to make sure it's not optimized away
# 1.143903810108473
# Global TRIOT iteration - float64
# Collected 1000 samples in 47.085 seconds
# Average time: 47.080ms
# Stddev time: 1.337ms
# Min time: 45.313ms
# Max time: 68.331ms
# Display output[[0,0]] to make sure it's not optimized away
# 1.143903810108473
# Fused per tensor reference iteration - float64
# Collected 1000 samples in 30.588 seconds
# Average time: 30.583ms
# Stddev time: 1.169ms
# Min time: 28.384ms
# Max time: 41.547ms
# Display output[[0,0]] to make sure it's not optimized away
# 1.143903810108473