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https://gitlab.com/libeigen/eigen.git
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Added a few benchmarks for the tensor code
This commit is contained in:
Benoit Steiner
parent
14f537c296
commit
46fc881e4a
305
bench/tensors/tensor_benchmarks.h
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305
bench/tensors/tensor_benchmarks.h
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#ifndef THIRD_PARTY_EIGEN3_TENSOR_BENCHMARKS_H_
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#define THIRD_PARTY_EIGEN3_TENSOR_BENCHMARKS_H_
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typedef int TensorIndex;
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#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
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#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
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#include "testing/base/public/benchmark.h"
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using Eigen::Tensor;
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using Eigen::TensorMap;
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// TODO(bsteiner): also templatize on the input type since we have users
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// for int8 as well as floats.
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template <typename Device> class BenchmarkSuite {
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public:
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BenchmarkSuite(const Device& device, size_t m, size_t k, size_t n)
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: m_(m), k_(k), n_(n), device_(device) {
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initialize();
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}
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BenchmarkSuite(const Device& device, size_t m)
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: m_(m), k_(m), n_(m), device_(device) {
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initialize();
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}
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~BenchmarkSuite() {
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device_.deallocate(a_);
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device_.deallocate(b_);
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device_.deallocate(c_);
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}
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void memcpy(int num_iters) {
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eigen_assert(m_ == k_ && k_ == n_);
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StartBenchmarkTiming();
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for (int iter = 0; iter < num_iters; ++iter) {
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device_.memcpy(c_, a_, m_ * m_ * sizeof(float));
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}
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// Record the number of values copied per second
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finalizeBenchmark(m_ * m_ * num_iters);
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}
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void random(int num_iters) {
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eigen_assert(m_ == k_ && k_ == n_);
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const Eigen::array<TensorIndex, 2> sizes(m_, m_);
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TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes);
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StartBenchmarkTiming();
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for (int iter = 0; iter < num_iters; ++iter) {
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C.device(device_) = C.random();
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}
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// Record the number of random numbers generated per second
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finalizeBenchmark(m_ * m_ * num_iters);
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}
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void slicing(int num_iters) {
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eigen_assert(m_ == k_ && k_ == n_);
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const Eigen::array<TensorIndex, 2> sizes(m_, m_);
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const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, sizes);
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const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, sizes);
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TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes);
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const Eigen::DSizes<TensorIndex, 2> quarter_sizes(Eigen::array<TensorIndex, 2>(m_/2, m_/2));
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const Eigen::DSizes<TensorIndex, 2> first_quadrant(Eigen::array<TensorIndex, 2>(0, 0));
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const Eigen::DSizes<TensorIndex, 2> second_quadrant(Eigen::array<TensorIndex, 2>(0, m_/2));
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const Eigen::DSizes<TensorIndex, 2> third_quadrant(Eigen::array<TensorIndex, 2>(m_/2, 0));
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const Eigen::DSizes<TensorIndex, 2> fourth_quadrant(Eigen::array<TensorIndex, 2>(m_/2, m_/2));
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StartBenchmarkTiming();
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for (int iter = 0; iter < num_iters; ++iter) {
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C.slice(first_quadrant, quarter_sizes).device(device_) =
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A.slice(first_quadrant, quarter_sizes);
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C.slice(second_quadrant, quarter_sizes).device(device_) =
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B.slice(second_quadrant, quarter_sizes);
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C.slice(third_quadrant, quarter_sizes).device(device_) =
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A.slice(third_quadrant, quarter_sizes);
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C.slice(fourth_quadrant, quarter_sizes).device(device_) =
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B.slice(fourth_quadrant, quarter_sizes);
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}
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// Record the number of values copied from the rhs slice to the lhs slice
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// each second
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finalizeBenchmark(m_ * m_ * num_iters);
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}
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void shuffling(int num_iters) {
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eigen_assert(m_ == n_);
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const Eigen::array<TensorIndex, 2> size_a(m_, k_);
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const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, size_a);
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const Eigen::array<TensorIndex, 2> size_b(k_, m_);
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TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, size_b);
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const Eigen::array<int, 2> shuffle(1, 0);
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StartBenchmarkTiming();
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for (int iter = 0; iter < num_iters; ++iter) {
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B.device(device_) = A.shuffle(shuffle);
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}
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// Record the number of values shuffled from A and copied to B each second
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finalizeBenchmark(m_ * k_ * num_iters);
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}
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void padding(int num_iters) {
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eigen_assert(m_ == k_);
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const Eigen::array<TensorIndex, 2> size_a(m_, k_-3);
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const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, size_a);
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const Eigen::array<TensorIndex, 2> size_b(k_, m_);
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TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, size_b);
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Eigen::array<Eigen::IndexPair<TensorIndex>, 2> paddings;
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paddings[0] = Eigen::IndexPair<TensorIndex>(0, 0);
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paddings[1] = Eigen::IndexPair<TensorIndex>(2, 1);
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StartBenchmarkTiming();
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for (int iter = 0; iter < num_iters; ++iter) {
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B.device(device_) = A.pad(paddings);
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}
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// Record the number of values copied from the padded tensor A each second
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finalizeBenchmark(m_ * k_ * num_iters);
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}
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void striding(int num_iters) {
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eigen_assert(m_ == k_);
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const Eigen::array<TensorIndex, 2> size_a(m_, k_);
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const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, size_a);
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const Eigen::array<TensorIndex, 2> size_b(m_, k_ / 2);
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TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, size_b);
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const Eigen::array<TensorIndex, 2> strides(1, 2);
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StartBenchmarkTiming();
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for (int iter = 0; iter < num_iters; ++iter) {
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B.device(device_) = A.stride(strides);
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}
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// Record the number of values copied from the padded tensor A each second
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finalizeBenchmark(m_ * k_ * num_iters);
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}
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void broadcasting(int num_iters) {
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const Eigen::array<TensorIndex, 2> size_a(m_, 1);
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const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, size_a);
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const Eigen::array<TensorIndex, 2> size_c(m_, n_);
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TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, size_c);
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#if defined(__CUDACC__)
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// nvcc doesn't support cxx11
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const Eigen::array<int, 2> broadcast(1, n_);
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#else
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// Take advantage of cxx11 to give the compiler information it can use to
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// optimize the code.
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Eigen::IndexList<Eigen::type2index<1>, int> broadcast;
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broadcast.set(1, n_);
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#endif
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StartBenchmarkTiming();
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for (int iter = 0; iter < num_iters; ++iter) {
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C.device(device_) = A.broadcast(broadcast);
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}
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// Record the number of values broadcasted from A and copied to C each second
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finalizeBenchmark(m_ * n_ * num_iters);
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}
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void coeffWiseOp(int num_iters) {
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eigen_assert(m_ == k_ && k_ == n_);
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const Eigen::array<TensorIndex, 2> sizes(m_, m_);
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const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, sizes);
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const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, sizes);
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TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes);
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StartBenchmarkTiming();
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for (int iter = 0; iter < num_iters; ++iter) {
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C.device(device_) = A * A.constant(3.14) + B * B.constant(2.7);
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}
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// Record the number of FLOP executed per second (2 multiplications and
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// 1 addition per value)
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finalizeBenchmark(3 * m_ * m_ * num_iters);
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}
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void algebraicFunc(int num_iters) {
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eigen_assert(m_ == k_ && k_ == n_);
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const Eigen::array<TensorIndex, 2> sizes(m_, m_);
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const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, sizes);
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const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, sizes);
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TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes);
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StartBenchmarkTiming();
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for (int iter = 0; iter < num_iters; ++iter) {
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C.device(device_) = A.rsqrt() + B.sqrt() * B.square();
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}
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// Record the number of FLOP executed per second (assuming one operation
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// per value)
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finalizeBenchmark(m_ * m_ * num_iters);
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}
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void transcendentalFunc(int num_iters) {
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eigen_assert(m_ == k_ && k_ == n_);
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const Eigen::array<TensorIndex, 2> sizes(m_, m_);
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const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, sizes);
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const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, sizes);
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TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes);
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StartBenchmarkTiming();
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for (int iter = 0; iter < num_iters; ++iter) {
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C.device(device_) = A.exp() + B.log();
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}
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// Record the number of FLOP executed per second (assuming one operation
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// per value)
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finalizeBenchmark(m_ * m_ * num_iters);
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}
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// Simple reduction
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void reduction(int num_iters) {
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const Eigen::array<TensorIndex, 2> input_size(k_, n_);
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const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, input_size);
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const Eigen::array<TensorIndex, 1> output_size(n_);
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TensorMap<Tensor<float, 1>, Eigen::Aligned> C(c_, output_size);
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const Eigen::array<TensorIndex, 1> sum_along_dim(0);
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StartBenchmarkTiming();
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for (int iter = 0; iter < num_iters; ++iter) {
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C.device(device_) = B.sum(sum_along_dim);
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}
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// Record the number of FLOP executed per second (assuming one operation
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// per value)
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finalizeBenchmark(m_ * m_ * num_iters);
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}
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// do a contraction which is equivalent to a matrix multiplication
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void contraction(int num_iters) {
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const Eigen::array<TensorIndex, 2> sizeA(m_, k_);
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const Eigen::array<TensorIndex, 2> sizeB(k_, n_);
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const Eigen::array<TensorIndex, 2> sizeC(m_, n_);
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const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, sizeA);
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const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, sizeB);
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TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizeC);
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typedef typename Tensor<float, 2>::DimensionPair DimPair;
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const Eigen::array<DimPair, 1> dims(DimPair(1, 0));
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StartBenchmarkTiming();
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for (int iter = 0; iter < num_iters; ++iter) {
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C.device(device_) = A.contract(B, dims);
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}
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// Record the number of FLOP executed per second (size_ multiplications and
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// additions for each value in the resulting tensor)
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finalizeBenchmark(static_cast<int64>(2) * m_ * n_ * k_ * num_iters);
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}
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void convolution(int num_iters, int kernel_x, int kernel_y) {
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const Eigen::array<TensorIndex, 2> input_sizes(m_, n_);
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TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, input_sizes);
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const Eigen::array<TensorIndex, 2> kernel_sizes(kernel_x, kernel_y);
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TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, kernel_sizes);
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const Eigen::array<TensorIndex, 2> result_sizes(
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m_ - kernel_x + 1, n_ - kernel_y + 1);
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TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, result_sizes);
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Eigen::array<Tensor<float, 2>::Index, 2> dims(0, 1);
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StartBenchmarkTiming();
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for (int iter = 0; iter < num_iters; ++iter) {
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C.device(device_) = A.convolve(B, dims);
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}
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// Record the number of FLOP executed per second (kernel_size
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// multiplications and additions for each value in the resulting tensor)
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finalizeBenchmark(
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(m_ - kernel_x + 1) * (n_ - kernel_y + 1) * kernel_x * kernel_y * 2 * num_iters);
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}
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private:
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void initialize() {
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a_ = (float *) device_.allocate(m_ * k_ * sizeof(float));
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b_ = (float *) device_.allocate(k_ * n_ * sizeof(float));
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c_ = (float *) device_.allocate(m_ * n_ * sizeof(float));
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// Initialize the content of the memory pools to prevent asan from
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// complaining.
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device_.memset(a_, 12, m_ * k_ * sizeof(float));
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device_.memset(b_, 23, k_ * n_ * sizeof(float));
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device_.memset(c_, 31, m_ * n_ * sizeof(float));
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BenchmarkUseRealTime();
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}
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inline void finalizeBenchmark(int64 num_items) {
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#if defined(EIGEN_USE_GPU) && defined(__CUDACC__)
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if (Eigen::internal::is_same<Device, Eigen::GpuDevice>::value) {
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device_.synchronize();
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}
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#endif
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StopBenchmarkTiming();
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SetBenchmarkItemsProcessed(num_items);
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}
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size_t m_;
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size_t k_;
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size_t n_;
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float* a_;
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float* b_;
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float* c_;
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Device device_;
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};
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#endif // THIRD_PARTY_EIGEN3_TENSOR_BENCHMARKS_H_
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156
bench/tensors/tensor_benchmarks_cpu.cc
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156
bench/tensors/tensor_benchmarks_cpu.cc
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#define EIGEN_USE_THREADS
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#include "base/sysinfo.h"
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#include "strings/strcat.h"
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#include "third_party/eigen3/tensor_benchmarks.h"
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#include "thread/threadpool.h"
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#ifdef __ANDROID__
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#define CREATE_THREAD_POOL(threads) \
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Eigen::ThreadPoolDevice device(threads);
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#else
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#define CREATE_THREAD_POOL(threads) \
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ThreadPool tp(threads); \
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tp.StartWorkers(); \
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Eigen::ThreadPoolDevice device(&tp, threads);
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#endif
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// Simple functions
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#define BM_FuncCPU(FUNC, THREADS) \
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static void BM_##FUNC##_##THREADS##T(int iters, int N) { \
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StopBenchmarkTiming(); \
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CREATE_THREAD_POOL(THREADS); \
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BenchmarkSuite<Eigen::ThreadPoolDevice> suite(device, N); \
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suite.FUNC(iters); \
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SetBenchmarkLabel(StrCat("using ", THREADS, " threads")); \
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} \
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BENCHMARK_RANGE(BM_##FUNC##_##THREADS##T, 10, 5000);
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BM_FuncCPU(memcpy, 4);
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BM_FuncCPU(memcpy, 8);
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BM_FuncCPU(memcpy, 12);
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BM_FuncCPU(random, 4);
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BM_FuncCPU(random, 8);
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BM_FuncCPU(random, 12);
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BM_FuncCPU(slicing, 4);
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BM_FuncCPU(slicing, 8);
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BM_FuncCPU(slicing, 12);
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BM_FuncCPU(shuffling, 4);
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BM_FuncCPU(shuffling, 8);
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BM_FuncCPU(shuffling, 12);
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BM_FuncCPU(padding, 4);
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BM_FuncCPU(padding, 8);
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BM_FuncCPU(padding, 12);
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BM_FuncCPU(striding, 4);
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BM_FuncCPU(striding, 8);
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BM_FuncCPU(striding, 12);
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BM_FuncCPU(broadcasting, 4);
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BM_FuncCPU(broadcasting, 8);
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BM_FuncCPU(broadcasting, 12);
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BM_FuncCPU(coeffWiseOp, 4);
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BM_FuncCPU(coeffWiseOp, 8);
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BM_FuncCPU(coeffWiseOp, 12);
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BM_FuncCPU(algebraicFunc, 4);
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BM_FuncCPU(algebraicFunc, 8);
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BM_FuncCPU(algebraicFunc, 12);
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BM_FuncCPU(transcendentalFunc, 4);
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BM_FuncCPU(transcendentalFunc, 8);
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BM_FuncCPU(transcendentalFunc, 12);
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BM_FuncCPU(reduction, 4);
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BM_FuncCPU(reduction, 8);
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BM_FuncCPU(reduction, 12);
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// Contractions
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#define BM_FuncWithInputDimsCPU(FUNC, D1, D2, D3, THREADS) \
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static void BM_##FUNC##_##D1##x##D2##x##D3##_##THREADS##T(int iters, int N) {\
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StopBenchmarkTiming(); \
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if (THREADS == 1) { \
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Eigen::DefaultDevice device; \
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BenchmarkSuite<Eigen::DefaultDevice> suite(device, D1, D2, D3); \
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suite.FUNC(iters); \
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} else { \
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CREATE_THREAD_POOL(THREADS); \
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BenchmarkSuite<Eigen::ThreadPoolDevice> suite(device, D1, D2, D3); \
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suite.FUNC(iters); \
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} \
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SetBenchmarkLabel(StrCat("using ", THREADS, " threads")); \
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} \
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BENCHMARK_RANGE(BM_##FUNC##_##D1##x##D2##x##D3##_##THREADS##T, 10, 5000);
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BM_FuncWithInputDimsCPU(contraction, N, N, N, 1);
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BM_FuncWithInputDimsCPU(contraction, N, N, N, 4);
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BM_FuncWithInputDimsCPU(contraction, N, N, N, 8);
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BM_FuncWithInputDimsCPU(contraction, N, N, N, 12);
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BM_FuncWithInputDimsCPU(contraction, N, N, N, 16);
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BM_FuncWithInputDimsCPU(contraction, 64, N, N, 1);
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BM_FuncWithInputDimsCPU(contraction, 64, N, N, 4);
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BM_FuncWithInputDimsCPU(contraction, 64, N, N, 8);
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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);
|
||||
75
bench/tensors/tensor_benchmarks_gpu.cc
Normal file
75
bench/tensors/tensor_benchmarks_gpu.cc
Normal file
@ -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);
|
||||
Loading…
x
Reference in New Issue
Block a user