From 5e9a1e7a7a7eccbb20a2c4eb44141727b0943f11 Mon Sep 17 00:00:00 2001 From: Mehdi Goli Date: Wed, 8 Mar 2017 14:17:48 +0000 Subject: Adding sycl Benchmarks. --- bench/tensors/README | 10 +- bench/tensors/tensor_benchmarks.h | 106 ++++++++++++++++++--- bench/tensors/tensor_benchmarks_sycl.cc | 73 ++++++++++++-- .../tensor_benchmarks_sycl_include_headers.cc | 2 + 4 files changed, 164 insertions(+), 27 deletions(-) create mode 100644 bench/tensors/tensor_benchmarks_sycl_include_headers.cc (limited to 'bench') diff --git a/bench/tensors/README b/bench/tensors/README index 3a5fdbe17..c4b742749 100644 --- a/bench/tensors/README +++ b/bench/tensors/README @@ -14,8 +14,12 @@ nvcc tensor_benchmarks_fp16_gpu.cu benchmark_main.cc -I ../../ -std=c++11 -O2 -D last but not least, we also provide a suite of benchmarks to measure the scalability of the contraction code on CPU. To compile these benchmarks, call g++ contraction_benchmarks_cpu.cc benchmark_main.cc -I ../../ -std=c++11 -O3 -DNDEBUG -pthread -mavx -o benchmarks_cpu -To compile the benchmark for SYCL, using ComputeCpp you currently need 2 passes (only for translation units containing device code): +To compile and run the benchmark for SYCL, using ComputeCpp you currently need following passes (only for translation units containing device code): 1. The device compilation pass that generates the device code (SYCL kernels and referenced device functions) and glue code needed by the host compiler to reference the device code from host code. -{ComputeCpp_ROOT}/bin/compute++ -I ../../ -I {ComputeCpp_ROOT}/include/ -std=c++11 -mllvm -inline-threshold=1000 -Wno-ignored-attributes -sycl -intelspirmetadata -emit-llvm -no-serial-memop -sycl-compress-name -DBUILD_PLATFORM_SPIR -DNDBUG -O3 -c tensor_benchmarks_sycl.cc +{ComputeCpp_ROOT}/bin/compute++ -I ../../ -I {ComputeCpp_ROOT}/include/ -std=c++11 -mllvm -inline-threshold=1000 -Wno-ignored-attributes -sycl -intelspirmetadata -emit-llvm -no-serial-memop -sycl-compress-name -DBUILD_PLATFORM_SPIR -DNDBUG -O3 -c tensor_benchmarks_sycl.cc -DEIGEN_USE_SYCL=1 2. The host compilation pass that generates the final host binary. -clang++-3.7 -include tensor_benchmarks_sycl.sycl benchmark_main.cc tensor_benchmarks_sycl.cc -pthread -I ../../ -I {ComputeCpp_ROOT}/include/ -L {ComputeCpp_ROOT}/lib/ -lComputeCpp -lOpenCL -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11 -o tensor_benchmark_sycl +clang++ -c benchmark_main.cc -pthread -I ../../ -D_GLIBCXX_USE_CXX11_ABI=0 -DEIGEN_USE_SYCL=1 -std=c++11 -o benchmark_main.o +clang++ tensor_benchmarks_sycl_include_headers.cc -pthread -I ../../ -I {ComputeCpp_ROOT}/include/ -L {ComputeCpp_ROOT}/lib/ -lComputeCpp -lOpenCL -D_GLIBCXX_USE_CXX11_ABI=0 -DEIGEN_USE_SYCL=1 -std=c++11 benchmark_main.o -o tensor_benchmark_sycl +export LD_LIBRARY_PATH={ComputeCpp_ROOT}/lib +3. Run the benchmark +./tensor_benchmark_sycl diff --git a/bench/tensors/tensor_benchmarks.h b/bench/tensors/tensor_benchmarks.h index c2fb3dede..325026113 100644 --- a/bench/tensors/tensor_benchmarks.h +++ b/bench/tensors/tensor_benchmarks.h @@ -35,6 +35,11 @@ template class BenchmarkSuite { void memcpy(int num_iters) { eigen_assert(m_ == k_ && k_ == n_); +#ifdef EIGEN_USE_SYCL // warmup for sycl + for (int iter = 0; iter < 10; ++iter) { + device_.memcpy(c_, a_, m_ * m_ * sizeof(T)); + } +#endif StartBenchmarkTiming(); for (int iter = 0; iter < num_iters; ++iter) { device_.memcpy(c_, a_, m_ * m_ * sizeof(T)); @@ -55,7 +60,11 @@ template class BenchmarkSuite { } const TensorMap, Eigen::Aligned> A((int*)a_, sizes); TensorMap, Eigen::Aligned> B(b_, sizes); - +#ifdef EIGEN_USE_SYCL // warmup for sycl + for (int iter = 0; iter < 10; ++iter) { + B.device(device_) = A.template cast(); + } +#endif StartBenchmarkTiming(); for (int iter = 0; iter < num_iters; ++iter) { B.device(device_) = A.template cast(); @@ -70,7 +79,6 @@ template class BenchmarkSuite { sizes[0] = m_; sizes[1] = m_; TensorMap, Eigen::Aligned> C(c_, sizes); - StartBenchmarkTiming(); for (int iter = 0; iter < num_iters; ++iter) { C.device(device_) = C.random(); @@ -93,7 +101,18 @@ template class BenchmarkSuite { const Eigen::DSizes second_quadrant(0, m_/2); const Eigen::DSizes third_quadrant(m_/2, 0); const Eigen::DSizes fourth_quadrant(m_/2, m_/2); - +#ifdef EIGEN_USE_SYCL // warmup for sycl + for (int iter = 0; iter < 10; ++iter) { + C.slice(first_quadrant, quarter_sizes).device(device_) = + A.slice(first_quadrant, quarter_sizes); + C.slice(second_quadrant, quarter_sizes).device(device_) = + B.slice(second_quadrant, quarter_sizes); + C.slice(third_quadrant, quarter_sizes).device(device_) = + A.slice(third_quadrant, quarter_sizes); + C.slice(fourth_quadrant, quarter_sizes).device(device_) = + B.slice(fourth_quadrant, quarter_sizes); + } +#endif StartBenchmarkTiming(); for (int iter = 0; iter < num_iters; ++iter) { C.slice(first_quadrant, quarter_sizes).device(device_) = @@ -118,7 +137,11 @@ template class BenchmarkSuite { Eigen::array output_size; output_size[0] = n_; TensorMap, Eigen::Aligned> C(c_, output_size); - +#ifdef EIGEN_USE_SYCL // warmup for sycl + for (int iter = 0; iter < 10; ++iter) { + C.device(device_) = B.chip(iter % k_, 0); + } +#endif StartBenchmarkTiming(); for (int iter = 0; iter < num_iters; ++iter) { C.device(device_) = B.chip(iter % k_, 0); @@ -135,7 +158,11 @@ template class BenchmarkSuite { Eigen::array output_size; output_size[0] = n_; TensorMap, Eigen::Aligned> C(c_, output_size); - +#ifdef EIGEN_USE_SYCL // warmup for sycl + for (int iter = 0; iter < 10; ++iter) { + C.device(device_) = B.chip(iter % n_, 1); + } +#endif StartBenchmarkTiming(); for (int iter = 0; iter < num_iters; ++iter) { C.device(device_) = B.chip(iter % n_, 1); @@ -158,7 +185,11 @@ template class BenchmarkSuite { Eigen::array shuffle; shuffle[0] = 1; shuffle[1] = 0; - +#ifdef EIGEN_USE_SYCL // warmup for sycl + for (int iter = 0; iter < 10; ++iter) { + B.device(device_) = A.shuffle(shuffle); + } +#endif StartBenchmarkTiming(); for (int iter = 0; iter < num_iters; ++iter) { B.device(device_) = A.shuffle(shuffle); @@ -186,7 +217,11 @@ template class BenchmarkSuite { paddings[0] = Eigen::IndexPair(0, 0); paddings[1] = Eigen::IndexPair(2, 1); #endif - +#ifdef EIGEN_USE_SYCL // warmup for sycl + for (int iter = 0; iter < 10; ++iter) { + B.device(device_) = A.pad(paddings); + } +#endif StartBenchmarkTiming(); for (int iter = 0; iter < num_iters; ++iter) { B.device(device_) = A.pad(paddings); @@ -216,6 +251,11 @@ template class BenchmarkSuite { Eigen::IndexList, Eigen::type2index<2> > strides; #endif +#ifdef EIGEN_USE_SYCL // warmup for sycl + for (int iter = 0; iter < 10; ++iter) { + B.device(device_) = A.stride(strides); + } +#endif StartBenchmarkTiming(); for (int iter = 0; iter < num_iters; ++iter) { B.device(device_) = A.stride(strides); @@ -245,6 +285,11 @@ template class BenchmarkSuite { broadcast.set(1, n_); #endif +#ifdef EIGEN_USE_SYCL // warmup for sycl + for (int iter = 0; iter < 10; ++iter) { + C.device(device_) = A.broadcast(broadcast); + } +#endif StartBenchmarkTiming(); for (int iter = 0; iter < num_iters; ++iter) { C.device(device_) = A.broadcast(broadcast); @@ -261,7 +306,11 @@ template class BenchmarkSuite { const TensorMap, Eigen::Aligned> A(a_, sizes); const TensorMap, Eigen::Aligned> B(b_, sizes); TensorMap, Eigen::Aligned> C(c_, sizes); - +#ifdef EIGEN_USE_SYCL // warmup for sycl + for (int iter = 0; iter < 10; ++iter) { + C.device(device_) = A * A.constant(static_cast(3.14)) + B * B.constant(static_cast(2.7)); + } +#endif StartBenchmarkTiming(); for (int iter = 0; iter < num_iters; ++iter) { C.device(device_) = A * A.constant(static_cast(3.14)) + B * B.constant(static_cast(2.7)); @@ -280,6 +329,11 @@ template class BenchmarkSuite { const TensorMap, Eigen::Aligned> B(b_, sizes); TensorMap, Eigen::Aligned> C(c_, sizes); +#ifdef EIGEN_USE_SYCL // warmup for sycl +for (int iter = 0; iter < 10; ++iter) { + C.device(device_) = A.rsqrt() + B.sqrt() * B.square(); +} +#endif StartBenchmarkTiming(); for (int iter = 0; iter < num_iters; ++iter) { C.device(device_) = A.rsqrt() + B.sqrt() * B.square(); @@ -297,7 +351,11 @@ template class BenchmarkSuite { const TensorMap, Eigen::Aligned> A(a_, sizes); const TensorMap, Eigen::Aligned> B(b_, sizes); TensorMap, Eigen::Aligned> C(c_, sizes); - +#ifdef EIGEN_USE_SYCL // warmup for sycl + for (int iter = 0; iter < 10; ++iter) { + C.device(device_) = A.exp() + B.log(); + } +#endif StartBenchmarkTiming(); for (int iter = 0; iter < num_iters; ++iter) { C.device(device_) = A.exp() + B.log(); @@ -325,7 +383,11 @@ template class BenchmarkSuite { // optimize the code. Eigen::IndexList> sum_along_dim; #endif - +#ifdef EIGEN_USE_SYCL // warmup for sycl + for (int iter = 0; iter < 10; ++iter) { + C.device(device_) = B.sum(sum_along_dim); + } +#endif StartBenchmarkTiming(); for (int iter = 0; iter < num_iters; ++iter) { C.device(device_) = B.sum(sum_along_dim); @@ -355,7 +417,11 @@ template class BenchmarkSuite { // optimize the code. Eigen::IndexList> sum_along_dim; #endif - +#ifdef EIGEN_USE_SYCL // warmup for sycl + for (int iter = 0; iter < 10; ++iter) { + C.device(device_) = B.sum(sum_along_dim); + } +#endif StartBenchmarkTiming(); for (int iter = 0; iter < num_iters; ++iter) { C.device(device_) = B.sum(sum_along_dim); @@ -375,7 +441,11 @@ template class BenchmarkSuite { Eigen::array output_size; TensorMap, Eigen::Aligned> C( c_, output_size); - +#ifdef EIGEN_USE_SYCL // warmup for sycl + for (int iter = 0; iter < 10; ++iter) { + C.device(device_) = B.sum(); + } +#endif StartBenchmarkTiming(); for (int iter = 0; iter < num_iters; ++iter) { C.device(device_) = B.sum(); @@ -404,7 +474,11 @@ template class BenchmarkSuite { typedef typename Tensor::DimensionPair DimPair; Eigen::array dims; dims[0] = DimPair(1, 0); - +#ifdef EIGEN_USE_SYCL // warmup for sycl + for (int iter = 0; iter < 10; ++iter) { + C.device(device_) = A.contract(B, dims); + } +#endif StartBenchmarkTiming(); for (int iter = 0; iter < num_iters; ++iter) { C.device(device_) = A.contract(B, dims); @@ -430,7 +504,11 @@ template class BenchmarkSuite { Eigen::array dims; dims[0] = 0; dims[1] = 1; - +#ifdef EIGEN_USE_SYCL // warmup for sycl + for (int iter = 0; iter < 10; ++iter) { + C.device(device_) = A.convolve(B, dims); + } +#endif StartBenchmarkTiming(); for (int iter = 0; iter < num_iters; ++iter) { C.device(device_) = A.convolve(B, dims); diff --git a/bench/tensors/tensor_benchmarks_sycl.cc b/bench/tensors/tensor_benchmarks_sycl.cc index 6df190869..cb6daac15 100644 --- a/bench/tensors/tensor_benchmarks_sycl.cc +++ b/bench/tensors/tensor_benchmarks_sycl.cc @@ -1,20 +1,73 @@ -#define EIGEN_USE_SYCL +#ifdef EIGEN_USE_SYCL #include #include #include "tensor_benchmarks.h" -#define BM_FuncGPU(FUNC) \ - static void BM_##FUNC(int iters, int N) { \ - StopBenchmarkTiming(); \ - cl::sycl::gpu_selector selector; \ - Eigen::QueueInterface queue(selector); \ - Eigen::SyclDevice device(&queue); \ - BenchmarkSuite suite(device, N); \ - suite.FUNC(iters); \ - } \ +#define BM_FuncGPU(FUNC) \ + static void BM_##FUNC(int iters, int N) { \ + StopBenchmarkTiming(); \ + cl::sycl::gpu_selector selector; \ + Eigen::QueueInterface queue(selector); \ + Eigen::SyclDevice device(&queue); \ + BenchmarkSuite suite(device, N); \ + suite.FUNC(iters); \ + } \ BENCHMARK_RANGE(BM_##FUNC, 10, 5000); +BM_FuncGPU(memcpy); +BM_FuncGPU(typeCasting); +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); +BM_FuncGPU(fullReduction); + + +// Contractions +#define BM_FuncWithInputDimsGPU(FUNC, D1, D2, D3) \ + static void BM_##FUNC##_##D1##x##D2##x##D3(int iters, int N) { \ + StopBenchmarkTiming(); \ + cl::sycl::gpu_selector selector; \ + Eigen::QueueInterface queue(selector); \ + Eigen::SyclDevice device(&queue); \ + BenchmarkSuite suite(device, D1, D2, D3); \ + 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(); \ + cl::sycl::gpu_selector selector; \ + Eigen::QueueInterface queue(selector); \ + Eigen::SyclDevice device(&queue); \ + BenchmarkSuite suite(device, N); \ + 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); +#endif diff --git a/bench/tensors/tensor_benchmarks_sycl_include_headers.cc b/bench/tensors/tensor_benchmarks_sycl_include_headers.cc new file mode 100644 index 000000000..4b3110b85 --- /dev/null +++ b/bench/tensors/tensor_benchmarks_sycl_include_headers.cc @@ -0,0 +1,2 @@ +#include "/home/mehdi/Projects/upstr_benoit/upstr_7MAR17/bench/tensors/tensor_benchmarks_sycl.cc" +#include "/home/mehdi/Projects/upstr_benoit/upstr_7MAR17/bench/tensors/tensor_benchmarks_sycl.sycl" -- cgit v1.2.3