Added a few benchmarks for the tensor code
diff --git a/bench/tensors/tensor_benchmarks.h b/bench/tensors/tensor_benchmarks.h
new file mode 100644
index 0000000..525b9ac
--- /dev/null
+++ b/bench/tensors/tensor_benchmarks.h
@@ -0,0 +1,305 @@
+#ifndef THIRD_PARTY_EIGEN3_TENSOR_BENCHMARKS_H_
+#define THIRD_PARTY_EIGEN3_TENSOR_BENCHMARKS_H_
+
+typedef int TensorIndex;
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
+
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
+#include "testing/base/public/benchmark.h"
+
+using Eigen::Tensor;
+using Eigen::TensorMap;
+
+
+// TODO(bsteiner): also templatize on the input type since we have users
+// for int8 as well as floats.
+template <typename Device> class BenchmarkSuite {
+ public:
+ BenchmarkSuite(const Device& device, size_t m, size_t k, size_t n)
+ : m_(m), k_(k), n_(n), device_(device) {
+ initialize();
+ }
+
+ BenchmarkSuite(const Device& device, size_t m)
+ : m_(m), k_(m), n_(m), device_(device) {
+ initialize();
+ }
+
+ ~BenchmarkSuite() {
+ device_.deallocate(a_);
+ device_.deallocate(b_);
+ device_.deallocate(c_);
+ }
+
+ void memcpy(int num_iters) {
+ eigen_assert(m_ == k_ && k_ == n_);
+ StartBenchmarkTiming();
+ for (int iter = 0; iter < num_iters; ++iter) {
+ device_.memcpy(c_, a_, m_ * m_ * sizeof(float));
+ }
+ // Record the number of values copied per second
+ finalizeBenchmark(m_ * m_ * num_iters);
+ }
+
+ void random(int num_iters) {
+ eigen_assert(m_ == k_ && k_ == n_);
+ const Eigen::array<TensorIndex, 2> sizes(m_, m_);
+ TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes);
+
+ StartBenchmarkTiming();
+ for (int iter = 0; iter < num_iters; ++iter) {
+ C.device(device_) = C.random();
+ }
+ // Record the number of random numbers generated per second
+ finalizeBenchmark(m_ * m_ * num_iters);
+ }
+
+ void slicing(int num_iters) {
+ eigen_assert(m_ == k_ && k_ == n_);
+ const Eigen::array<TensorIndex, 2> sizes(m_, m_);
+ const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, sizes);
+ const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, sizes);
+ TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes);
+
+ const Eigen::DSizes<TensorIndex, 2> quarter_sizes(Eigen::array<TensorIndex, 2>(m_/2, m_/2));
+ const Eigen::DSizes<TensorIndex, 2> first_quadrant(Eigen::array<TensorIndex, 2>(0, 0));
+ const Eigen::DSizes<TensorIndex, 2> second_quadrant(Eigen::array<TensorIndex, 2>(0, m_/2));
+ const Eigen::DSizes<TensorIndex, 2> third_quadrant(Eigen::array<TensorIndex, 2>(m_/2, 0));
+ const Eigen::DSizes<TensorIndex, 2> fourth_quadrant(Eigen::array<TensorIndex, 2>(m_/2, m_/2));
+
+ StartBenchmarkTiming();
+ for (int iter = 0; iter < num_iters; ++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);
+ }
+ // Record the number of values copied from the rhs slice to the lhs slice
+ // each second
+ finalizeBenchmark(m_ * m_ * num_iters);
+ }
+
+ void shuffling(int num_iters) {
+ eigen_assert(m_ == n_);
+ const Eigen::array<TensorIndex, 2> size_a(m_, k_);
+ const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, size_a);
+ const Eigen::array<TensorIndex, 2> size_b(k_, m_);
+ TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, size_b);
+
+ const Eigen::array<int, 2> shuffle(1, 0);
+
+ StartBenchmarkTiming();
+ for (int iter = 0; iter < num_iters; ++iter) {
+ B.device(device_) = A.shuffle(shuffle);
+ }
+ // Record the number of values shuffled from A and copied to B each second
+ finalizeBenchmark(m_ * k_ * num_iters);
+ }
+
+ void padding(int num_iters) {
+ eigen_assert(m_ == k_);
+ const Eigen::array<TensorIndex, 2> size_a(m_, k_-3);
+ const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, size_a);
+ const Eigen::array<TensorIndex, 2> size_b(k_, m_);
+ TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, size_b);
+
+ Eigen::array<Eigen::IndexPair<TensorIndex>, 2> paddings;
+ paddings[0] = Eigen::IndexPair<TensorIndex>(0, 0);
+ paddings[1] = Eigen::IndexPair<TensorIndex>(2, 1);
+
+ StartBenchmarkTiming();
+ for (int iter = 0; iter < num_iters; ++iter) {
+ B.device(device_) = A.pad(paddings);
+ }
+ // Record the number of values copied from the padded tensor A each second
+ finalizeBenchmark(m_ * k_ * num_iters);
+ }
+
+ void striding(int num_iters) {
+ eigen_assert(m_ == k_);
+ const Eigen::array<TensorIndex, 2> size_a(m_, k_);
+ const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, size_a);
+ const Eigen::array<TensorIndex, 2> size_b(m_, k_ / 2);
+ TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, size_b);
+
+ const Eigen::array<TensorIndex, 2> strides(1, 2);
+
+ StartBenchmarkTiming();
+ for (int iter = 0; iter < num_iters; ++iter) {
+ B.device(device_) = A.stride(strides);
+ }
+ // Record the number of values copied from the padded tensor A each second
+ finalizeBenchmark(m_ * k_ * num_iters);
+ }
+
+ void broadcasting(int num_iters) {
+ const Eigen::array<TensorIndex, 2> size_a(m_, 1);
+ const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, size_a);
+ const Eigen::array<TensorIndex, 2> size_c(m_, n_);
+ TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, size_c);
+
+#if defined(__CUDACC__)
+ // nvcc doesn't support cxx11
+ const Eigen::array<int, 2> broadcast(1, n_);
+#else
+ // Take advantage of cxx11 to give the compiler information it can use to
+ // optimize the code.
+ Eigen::IndexList<Eigen::type2index<1>, int> broadcast;
+ broadcast.set(1, n_);
+#endif
+
+ StartBenchmarkTiming();
+ for (int iter = 0; iter < num_iters; ++iter) {
+ C.device(device_) = A.broadcast(broadcast);
+ }
+ // Record the number of values broadcasted from A and copied to C each second
+ finalizeBenchmark(m_ * n_ * num_iters);
+ }
+
+ void coeffWiseOp(int num_iters) {
+ eigen_assert(m_ == k_ && k_ == n_);
+ const Eigen::array<TensorIndex, 2> sizes(m_, m_);
+ const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, sizes);
+ const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, sizes);
+ TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes);
+
+ StartBenchmarkTiming();
+ for (int iter = 0; iter < num_iters; ++iter) {
+ C.device(device_) = A * A.constant(3.14) + B * B.constant(2.7);
+ }
+ // Record the number of FLOP executed per second (2 multiplications and
+ // 1 addition per value)
+ finalizeBenchmark(3 * m_ * m_ * num_iters);
+ }
+
+ void algebraicFunc(int num_iters) {
+ eigen_assert(m_ == k_ && k_ == n_);
+ const Eigen::array<TensorIndex, 2> sizes(m_, m_);
+ const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, sizes);
+ const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, sizes);
+ TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes);
+
+ StartBenchmarkTiming();
+ for (int iter = 0; iter < num_iters; ++iter) {
+ C.device(device_) = A.rsqrt() + B.sqrt() * B.square();
+ }
+ // Record the number of FLOP executed per second (assuming one operation
+ // per value)
+ finalizeBenchmark(m_ * m_ * num_iters);
+ }
+
+ void transcendentalFunc(int num_iters) {
+ eigen_assert(m_ == k_ && k_ == n_);
+ const Eigen::array<TensorIndex, 2> sizes(m_, m_);
+ const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, sizes);
+ const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, sizes);
+ TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes);
+
+ StartBenchmarkTiming();
+ for (int iter = 0; iter < num_iters; ++iter) {
+ C.device(device_) = A.exp() + B.log();
+ }
+ // Record the number of FLOP executed per second (assuming one operation
+ // per value)
+ finalizeBenchmark(m_ * m_ * num_iters);
+ }
+
+ // Simple reduction
+ void reduction(int num_iters) {
+ const Eigen::array<TensorIndex, 2> input_size(k_, n_);
+ const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, input_size);
+ const Eigen::array<TensorIndex, 1> output_size(n_);
+ TensorMap<Tensor<float, 1>, Eigen::Aligned> C(c_, output_size);
+
+ const Eigen::array<TensorIndex, 1> sum_along_dim(0);
+
+ StartBenchmarkTiming();
+ for (int iter = 0; iter < num_iters; ++iter) {
+ C.device(device_) = B.sum(sum_along_dim);
+ }
+ // Record the number of FLOP executed per second (assuming one operation
+ // per value)
+ finalizeBenchmark(m_ * m_ * num_iters);
+ }
+
+ // do a contraction which is equivalent to a matrix multiplication
+ void contraction(int num_iters) {
+ const Eigen::array<TensorIndex, 2> sizeA(m_, k_);
+ const Eigen::array<TensorIndex, 2> sizeB(k_, n_);
+ const Eigen::array<TensorIndex, 2> sizeC(m_, n_);
+
+ const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, sizeA);
+ const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, sizeB);
+ TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizeC);
+
+ typedef typename Tensor<float, 2>::DimensionPair DimPair;
+ const Eigen::array<DimPair, 1> dims(DimPair(1, 0));
+
+ StartBenchmarkTiming();
+ for (int iter = 0; iter < num_iters; ++iter) {
+ C.device(device_) = A.contract(B, dims);
+ }
+ // Record the number of FLOP executed per second (size_ multiplications and
+ // additions for each value in the resulting tensor)
+ finalizeBenchmark(static_cast<int64>(2) * m_ * n_ * k_ * num_iters);
+ }
+
+ void convolution(int num_iters, int kernel_x, int kernel_y) {
+ const Eigen::array<TensorIndex, 2> input_sizes(m_, n_);
+ TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, input_sizes);
+ const Eigen::array<TensorIndex, 2> kernel_sizes(kernel_x, kernel_y);
+ TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, kernel_sizes);
+ const Eigen::array<TensorIndex, 2> result_sizes(
+ m_ - kernel_x + 1, n_ - kernel_y + 1);
+ TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, result_sizes);
+ Eigen::array<Tensor<float, 2>::Index, 2> dims(0, 1);
+
+ StartBenchmarkTiming();
+ for (int iter = 0; iter < num_iters; ++iter) {
+ C.device(device_) = A.convolve(B, dims);
+ }
+ // Record the number of FLOP executed per second (kernel_size
+ // multiplications and additions for each value in the resulting tensor)
+ finalizeBenchmark(
+ (m_ - kernel_x + 1) * (n_ - kernel_y + 1) * kernel_x * kernel_y * 2 * num_iters);
+ }
+
+ private:
+ void initialize() {
+ a_ = (float *) device_.allocate(m_ * k_ * sizeof(float));
+ b_ = (float *) device_.allocate(k_ * n_ * sizeof(float));
+ c_ = (float *) device_.allocate(m_ * n_ * sizeof(float));
+
+ // Initialize the content of the memory pools to prevent asan from
+ // complaining.
+ device_.memset(a_, 12, m_ * k_ * sizeof(float));
+ device_.memset(b_, 23, k_ * n_ * sizeof(float));
+ device_.memset(c_, 31, m_ * n_ * sizeof(float));
+
+ BenchmarkUseRealTime();
+ }
+
+ inline void finalizeBenchmark(int64 num_items) {
+#if defined(EIGEN_USE_GPU) && defined(__CUDACC__)
+ if (Eigen::internal::is_same<Device, Eigen::GpuDevice>::value) {
+ device_.synchronize();
+ }
+#endif
+ StopBenchmarkTiming();
+ SetBenchmarkItemsProcessed(num_items);
+ }
+
+
+ size_t m_;
+ size_t k_;
+ size_t n_;
+ float* a_;
+ float* b_;
+ float* c_;
+ Device device_;
+};
+#endif // THIRD_PARTY_EIGEN3_TENSOR_BENCHMARKS_H_
diff --git a/bench/tensors/tensor_benchmarks_cpu.cc b/bench/tensors/tensor_benchmarks_cpu.cc
new file mode 100644
index 0000000..68653ba
--- /dev/null
+++ b/bench/tensors/tensor_benchmarks_cpu.cc
@@ -0,0 +1,156 @@
+#define EIGEN_USE_THREADS
+
+#include "base/sysinfo.h"
+#include "strings/strcat.h"
+#include "third_party/eigen3/tensor_benchmarks.h"
+#include "thread/threadpool.h"
+
+#ifdef __ANDROID__
+#define CREATE_THREAD_POOL(threads) \
+Eigen::ThreadPoolDevice device(threads);
+#else
+#define CREATE_THREAD_POOL(threads) \
+ThreadPool tp(threads); \
+tp.StartWorkers(); \
+Eigen::ThreadPoolDevice device(&tp, threads);
+#endif
+
+// Simple functions
+#define BM_FuncCPU(FUNC, THREADS) \
+ static void BM_##FUNC##_##THREADS##T(int iters, int N) { \
+ StopBenchmarkTiming(); \
+ CREATE_THREAD_POOL(THREADS); \
+ BenchmarkSuite<Eigen::ThreadPoolDevice> suite(device, N); \
+ suite.FUNC(iters); \
+ SetBenchmarkLabel(StrCat("using ", THREADS, " threads")); \
+ } \
+ BENCHMARK_RANGE(BM_##FUNC##_##THREADS##T, 10, 5000);
+
+BM_FuncCPU(memcpy, 4);
+BM_FuncCPU(memcpy, 8);
+BM_FuncCPU(memcpy, 12);
+
+BM_FuncCPU(random, 4);
+BM_FuncCPU(random, 8);
+BM_FuncCPU(random, 12);
+
+BM_FuncCPU(slicing, 4);
+BM_FuncCPU(slicing, 8);
+BM_FuncCPU(slicing, 12);
+
+BM_FuncCPU(shuffling, 4);
+BM_FuncCPU(shuffling, 8);
+BM_FuncCPU(shuffling, 12);
+
+BM_FuncCPU(padding, 4);
+BM_FuncCPU(padding, 8);
+BM_FuncCPU(padding, 12);
+
+BM_FuncCPU(striding, 4);
+BM_FuncCPU(striding, 8);
+BM_FuncCPU(striding, 12);
+
+BM_FuncCPU(broadcasting, 4);
+BM_FuncCPU(broadcasting, 8);
+BM_FuncCPU(broadcasting, 12);
+
+BM_FuncCPU(coeffWiseOp, 4);
+BM_FuncCPU(coeffWiseOp, 8);
+BM_FuncCPU(coeffWiseOp, 12);
+
+BM_FuncCPU(algebraicFunc, 4);
+BM_FuncCPU(algebraicFunc, 8);
+BM_FuncCPU(algebraicFunc, 12);
+
+BM_FuncCPU(transcendentalFunc, 4);
+BM_FuncCPU(transcendentalFunc, 8);
+BM_FuncCPU(transcendentalFunc, 12);
+
+BM_FuncCPU(reduction, 4);
+BM_FuncCPU(reduction, 8);
+BM_FuncCPU(reduction, 12);
+
+
+// Contractions
+#define BM_FuncWithInputDimsCPU(FUNC, D1, D2, D3, THREADS) \
+ static void BM_##FUNC##_##D1##x##D2##x##D3##_##THREADS##T(int iters, int N) {\
+ StopBenchmarkTiming(); \
+ if (THREADS == 1) { \
+ Eigen::DefaultDevice device; \
+ BenchmarkSuite<Eigen::DefaultDevice> suite(device, D1, D2, D3); \
+ suite.FUNC(iters); \
+ } else { \
+ CREATE_THREAD_POOL(THREADS); \
+ BenchmarkSuite<Eigen::ThreadPoolDevice> suite(device, D1, D2, D3); \
+ suite.FUNC(iters); \
+ } \
+ SetBenchmarkLabel(StrCat("using ", THREADS, " threads")); \
+ } \
+ BENCHMARK_RANGE(BM_##FUNC##_##D1##x##D2##x##D3##_##THREADS##T, 10, 5000);
+
+
+BM_FuncWithInputDimsCPU(contraction, N, N, N, 1);
+BM_FuncWithInputDimsCPU(contraction, N, N, N, 4);
+BM_FuncWithInputDimsCPU(contraction, N, N, N, 8);
+BM_FuncWithInputDimsCPU(contraction, N, N, N, 12);
+BM_FuncWithInputDimsCPU(contraction, N, N, N, 16);
+
+BM_FuncWithInputDimsCPU(contraction, 64, N, N, 1);
+BM_FuncWithInputDimsCPU(contraction, 64, N, N, 4);
+BM_FuncWithInputDimsCPU(contraction, 64, N, N, 8);
+BM_FuncWithInputDimsCPU(contraction, 64, N, N, 12);
+BM_FuncWithInputDimsCPU(contraction, 64, N, N, 16);
+
+BM_FuncWithInputDimsCPU(contraction, N, 64, N, 1);
+BM_FuncWithInputDimsCPU(contraction, N, 64, N, 4);
+BM_FuncWithInputDimsCPU(contraction, N, 64, N, 8);
+BM_FuncWithInputDimsCPU(contraction, N, 64, N, 12);
+BM_FuncWithInputDimsCPU(contraction, N, 64, N, 16);
+
+BM_FuncWithInputDimsCPU(contraction, 1, N, N, 1);
+BM_FuncWithInputDimsCPU(contraction, 1, N, N, 4);
+BM_FuncWithInputDimsCPU(contraction, 1, N, N, 8);
+BM_FuncWithInputDimsCPU(contraction, 1, N, N, 12);
+BM_FuncWithInputDimsCPU(contraction, 1, N, N, 16);
+
+BM_FuncWithInputDimsCPU(contraction, N, N, 1, 1);
+BM_FuncWithInputDimsCPU(contraction, N, N, 1, 4);
+BM_FuncWithInputDimsCPU(contraction, N, N, 1, 8);
+BM_FuncWithInputDimsCPU(contraction, N, N, 1, 12);
+BM_FuncWithInputDimsCPU(contraction, N, N, 1, 16);
+
+
+// Convolutions
+#define BM_FuncWithKernelDimsCPU(FUNC, DIM1, DIM2, THREADS) \
+ static void BM_##FUNC##_##DIM1##x##DIM2##_##THREADS##T(int iters, int N) { \
+ StopBenchmarkTiming(); \
+ CREATE_THREAD_POOL(THREADS); \
+ BenchmarkSuite<Eigen::ThreadPoolDevice> suite(device, N); \
+ suite.FUNC(iters, DIM1, DIM2); \
+ SetBenchmarkLabel(StrCat("using ", THREADS, " threads")); \
+ } \
+ BENCHMARK_RANGE(BM_##FUNC##_##DIM1##x##DIM2##_##THREADS##T, 128, 5000);
+
+BM_FuncWithKernelDimsCPU(convolution, 7, 1, 4);
+BM_FuncWithKernelDimsCPU(convolution, 7, 1, 8);
+BM_FuncWithKernelDimsCPU(convolution, 7, 1, 12);
+
+BM_FuncWithKernelDimsCPU(convolution, 1, 7, 4);
+BM_FuncWithKernelDimsCPU(convolution, 1, 7, 8);
+BM_FuncWithKernelDimsCPU(convolution, 1, 7, 12);
+
+BM_FuncWithKernelDimsCPU(convolution, 7, 4, 4);
+BM_FuncWithKernelDimsCPU(convolution, 7, 4, 8);
+BM_FuncWithKernelDimsCPU(convolution, 7, 4, 12);
+
+BM_FuncWithKernelDimsCPU(convolution, 4, 7, 4);
+BM_FuncWithKernelDimsCPU(convolution, 4, 7, 8);
+BM_FuncWithKernelDimsCPU(convolution, 4, 7, 12);
+
+BM_FuncWithKernelDimsCPU(convolution, 7, 64, 4);
+BM_FuncWithKernelDimsCPU(convolution, 7, 64, 8);
+BM_FuncWithKernelDimsCPU(convolution, 7, 64, 12);
+
+BM_FuncWithKernelDimsCPU(convolution, 64, 7, 4);
+BM_FuncWithKernelDimsCPU(convolution, 64, 7, 8);
+BM_FuncWithKernelDimsCPU(convolution, 64, 7, 12);
diff --git a/bench/tensors/tensor_benchmarks_gpu.cc b/bench/tensors/tensor_benchmarks_gpu.cc
new file mode 100644
index 0000000..adea754
--- /dev/null
+++ b/bench/tensors/tensor_benchmarks_gpu.cc
@@ -0,0 +1,75 @@
+#define EIGEN_USE_GPU
+
+#include <cuda.h>
+#include <cuda_runtime.h>
+#include <iostream>
+#include "strings/strcat.h"
+#include "third_party/eigen3/tensor_benchmarks.h"
+
+
+
+// Simple functions
+#define BM_FuncGPU(FUNC) \
+ static void BM_##FUNC(int iters, int N) { \
+ StopBenchmarkTiming(); \
+ cudaStream_t stream; \
+ cudaStreamCreate(&stream); \
+ Eigen::GpuDevice device(&stream); \
+ BenchmarkSuite<Eigen::GpuDevice> suite(device, N); \
+ cudaDeviceSynchronize(); \
+ suite.FUNC(iters); \
+ cudaStreamDestroy(stream); \
+ } \
+ BENCHMARK_RANGE(BM_##FUNC, 10, 5000);
+
+BM_FuncGPU(memcpy);
+BM_FuncGPU(random);
+BM_FuncGPU(slicing);
+BM_FuncGPU(shuffling);
+BM_FuncGPU(padding);
+BM_FuncGPU(striding);
+BM_FuncGPU(broadcasting);
+BM_FuncGPU(coeffWiseOp);
+BM_FuncGPU(reduction);
+
+
+// Contractions
+#define BM_FuncWithInputDimsGPU(FUNC, D1, D2, D3) \
+ static void BM_##FUNC##_##D1##x##D2##x##D3(int iters, int N) { \
+ StopBenchmarkTiming(); \
+ cudaStream_t stream; \
+ cudaStreamCreate(&stream); \
+ Eigen::GpuDevice device(&stream); \
+ BenchmarkSuite<Eigen::GpuDevice> suite(device, D1, D2, D3); \
+ cudaDeviceSynchronize(); \
+ suite.FUNC(iters); \
+ cudaStreamDestroy(stream); \
+ } \
+ 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);
+
+
+// Convolutions
+#define BM_FuncWithKernelDimsGPU(FUNC, DIM1, DIM2) \
+ static void BM_##FUNC##_##DIM1##x##DIM2(int iters, int N) { \
+ StopBenchmarkTiming(); \
+ cudaStream_t stream; \
+ cudaStreamCreate(&stream); \
+ Eigen::GpuDevice device(&stream); \
+ BenchmarkSuite<Eigen::GpuDevice> suite(device, N); \
+ cudaDeviceSynchronize(); \
+ suite.FUNC(iters, DIM1, DIM2); \
+ cudaStreamDestroy(stream); \
+ } \
+ 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);
