gotch/ts

BENCHMARK

Convolution 2D

Ref.

1. https://tigress-web.princeton.edu/~jdh4/PyTorchPerformanceTuningGuide_GTC2021.pdf
2. https://github.com/soumith/convnet-benchmarks

Benchmark tensor operation conv2d forward propagation:

• input shape: [32, 64, 64, 64]
• kernel: [64, 3, 3]

goos: linux goarch: amd64 pkg: github.com/sugarme/gotch/ts cpu: Intel(R) Core(TM) i7-6700HQ CPU @ 2.60GHz BenchmarkConv2dCPU-8 100 21198303 ns/op BenchmarkConv2dCUDA-8 100 2201213 ns/op

name          time/op
Conv2dCPU-8   21.2ms ± 0%
Conv2dCUDA-8  2.20ms ± 0%

Benchmark against Pytorch 1.11 CUDA 11

conv2d-CPU(x):   56.7 ms
conv2d-CUDA(x):   38.0 ms

benchmark Python code below

import torch
import torch.nn.functional as F
import timeit

x = torch.randn(32, 64, 64, 64)

def conv2dCPU(x):
    conv1 = torch.nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=0, bias=False)
    return conv1(x)

def conv2dCUDA(x):
    x = x.cuda()
    conv1 = torch.nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=0, bias=False).cuda()
    return conv1(x)

t0 = timeit.Timer(
    stmt='conv2dCPU(x)',
    setup='from __main__ import conv2dCPU',
    globals={'x': x})

t1 = timeit.Timer(
    stmt='conv2dCUDA(x)',
    setup='from __main__ import conv2dCUDA',
    globals={'x': x})

print(f'conv2d-CPU(x):  {t0.timeit(100) / 100 * 1e3:>5.1f} ms')
print(f'conv2d-CUDA(x):  {t1.timeit(100) / 100 * 1e3:>5.1f} ms')