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Added benchmarks for fp16

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This commit is contained in:
Benoit Steiner 2016-02-26 12:21:25 -08:00
parent 2cd32cad27
commit 002824e32d
2 changed files with 82 additions and 2 deletions
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bench/tensors/README
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Each benchmark comes in 2 flavors: one that runs on CPU, and one that runs on GPU.
To compile the CPU benchmarks, simply call:
To compile the floating point CPU benchmarks, simply call:
g++ tensor_benchmarks_cpu.cc benchmark_main.cc -I ../../ -std=c++11 -O3 -DNDEBUG -pthread -mavx -o benchmarks_cpu
To compile the GPU benchmarks, simply call:
To compile the floating point GPU benchmarks, simply call:
nvcc tensor_benchmarks_gpu.cu benchmark_main.cc -I ../../ -std=c++11 -O2 -DNDEBUG -arch compute_35 -o benchmarks_gpu
To compile the half float GPU benchmarks, simply call the command line below. You'll need a recent GPU that supports compute capability 5.3 or higher to run them and nvcc 7.5 or higher to compile the code.
nvcc tensor_benchmarks_fp16_gpu.cu benchmark_main.cc -I ../../ -std=c++11 -O2 -DNDEBUG -arch compute_53 -o benchmarks_gpu

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bench/tensors/tensor_benchmarks_fp16_gpu.cu Normal file
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#define EIGEN_USE_GPU
#include <cuda.h>
#include <cuda_runtime.h>
#include <iostream>
#include "tensor_benchmarks.h"
// Simple functions
#define BM_FuncGPU(FUNC) \
static void BM_##FUNC(int iters, int N) { \
StopBenchmarkTiming(); \
Eigen::CudaStreamDevice stream; \
Eigen::GpuDevice device(&stream); \
BenchmarkSuite<Eigen::GpuDevice, half> suite(device, N); \
cudaDeviceSynchronize(); \
suite.FUNC(iters); \
} \
BENCHMARK_RANGE(BM_##FUNC, 10, 5000);
BM_FuncGPU(memcpy);
//BM_FuncGPU(typeCasting);
//BM_FuncGPU(random);
BM_FuncGPU(slicing);
BM_FuncGPU(rowChip);
BM_FuncGPU(colChip);
BM_FuncGPU(shuffling);
BM_FuncGPU(padding);
BM_FuncGPU(striding);
BM_FuncGPU(broadcasting);
//BM_FuncGPU(coeffWiseOp);
//BM_FuncGPU(algebraicFunc);
//BM_FuncGPU(transcendentalFunc);
BM_FuncGPU(rowReduction);
BM_FuncGPU(colReduction);
// Contractions
#define BM_FuncWithInputDimsGPU(FUNC, D1, D2, D3) \
static void BM_##FUNC##_##D1##x##D2##x##D3(int iters, int N) { \
StopBenchmarkTiming(); \
Eigen::CudaStreamDevice stream; \
Eigen::GpuDevice device(&stream); \
BenchmarkSuite<Eigen::GpuDevice, half> suite(device, D1, D2, D3); \
cudaDeviceSynchronize(); \
suite.FUNC(iters); \
} \
BENCHMARK_RANGE(BM_##FUNC##_##D1##x##D2##x##D3, 10, 5000);
/*BM_FuncWithInputDimsGPU(contraction, N, N, N);
BM_FuncWithInputDimsGPU(contraction, 64, N, N);
BM_FuncWithInputDimsGPU(contraction, N, 64, N);
BM_FuncWithInputDimsGPU(contraction, N, N, 64);
*/
// Convolutions
#define BM_FuncWithKernelDimsGPU(FUNC, DIM1, DIM2) \
static void BM_##FUNC##_##DIM1##x##DIM2(int iters, int N) { \
StopBenchmarkTiming(); \
Eigen::CudaStreamDevice stream; \
Eigen::GpuDevice device(&stream); \
BenchmarkSuite<Eigen::GpuDevice, half> suite(device, N); \
cudaDeviceSynchronize(); \
suite.FUNC(iters, DIM1, DIM2); \
} \
BENCHMARK_RANGE(BM_##FUNC##_##DIM1##x##DIM2, 128, 5000);
/*
BM_FuncWithKernelDimsGPU(convolution, 7, 1);
BM_FuncWithKernelDimsGPU(convolution, 1, 7);
BM_FuncWithKernelDimsGPU(convolution, 7, 4);
BM_FuncWithKernelDimsGPU(convolution, 4, 7);
BM_FuncWithKernelDimsGPU(convolution, 7, 64);
BM_FuncWithKernelDimsGPU(convolution, 64, 7);
*/