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authorGravatar Benoit Steiner <benoit.steiner.goog@gmail.com>2016-02-26 12:21:25 -0800
committerGravatar Benoit Steiner <benoit.steiner.goog@gmail.com>2016-02-26 12:21:25 -0800
commit002824e32def5c9a430acac4bd9fc05308c923bb (patch)
treebb669ed87de715400ff87a6623a7d188b68da4fa /bench/tensors
parent2cd32cad2782c467316297cec1a2b0ddff89c686 (diff)
Added benchmarks for fp16
Diffstat (limited to 'bench/tensors')
-rw-r--r--bench/tensors/README8
-rw-r--r--bench/tensors/tensor_benchmarks_fp16_gpu.cu76
2 files changed, 82 insertions, 2 deletions
diff --git a/bench/tensors/README b/bench/tensors/README
index 6b51fe878..1de0a5786 100644
--- a/bench/tensors/README
+++ b/bench/tensors/README
@@ -1,8 +1,12 @@
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
+
diff --git a/bench/tensors/tensor_benchmarks_fp16_gpu.cu b/bench/tensors/tensor_benchmarks_fp16_gpu.cu
new file mode 100644
index 000000000..d841bcdac
--- /dev/null
+++ b/bench/tensors/tensor_benchmarks_fp16_gpu.cu
@@ -0,0 +1,76 @@
+#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);
+*/ \ No newline at end of file