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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
// Copyright (C) 2021 The Eigen Team.
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at
// The following is an example GPU test.
#include "main.h" // Include the main test utilities.
// Define a kernel functor.
// The kernel must be a POD type and implement operator().
struct AddKernel {
// Parameters must be POD or serializable Eigen types (e.g. Matrix,
// Array). The return value must be a POD or serializable value type.
template<typename Type1, typename Type2, typename Type3>
Type3 operator()(const Type1& A, const Type2& B, Type3& C) const {
C = A + B; // Populate output parameter.
Type3 D = A + B; // Populate return value.
return D;
// Define a sub-test that uses the kernel.
template <typename T>
void test_add(const T& type) {
const Index rows = type.rows();
const Index cols = type.cols();
// Create random inputs.
const T A = T::Random(rows, cols);
const T B = T::Random(rows, cols);
T C; // Output parameter.
// Create kernel.
AddKernel add_kernel;
// Run add_kernel(A, B, C) via run(...).
// This will run on the GPU if using a GPU compiler, or CPU otherwise,
// facilitating generic tests that can run on either.
T D = run(add_kernel, A, B, C);
// Check that both output parameter and return value are correctly populated.
const T expected = A + B;
// In a GPU-only test, we can verify that the CPU and GPU produce the
// same results.
T C_cpu, C_gpu;
T D_cpu = run_on_cpu(add_kernel, A, B, C_cpu); // Runs on CPU.
T D_gpu = run_on_gpu(add_kernel, A, B, C_gpu); // Runs on GPU.
struct MultiplyKernel {
template<typename Type1, typename Type2, typename Type3>
Type3 operator()(const Type1& A, const Type2& B, Type3& C) const {
C = A * B;
return A * B;
template <typename T1, typename T2, typename T3>
void test_multiply(const T1& type1, const T2& type2, const T3& type3) {
const T1 A = T1::Random(type1.rows(), type1.cols());
const T2 B = T2::Random(type2.rows(), type2.cols());
T3 C;
MultiplyKernel multiply_kernel;
// The run(...) family of functions uses a memory buffer to transfer data back
// and forth to and from the device. The size of this buffer is estimated
// from the size of all input parameters. If the estimated buffer size is
// not sufficient for transferring outputs from device-to-host, then an
// explicit buffer size needs to be specified.
// 2 outputs of size (A * B). For each matrix output, the buffer will store
// the number of rows, columns, and the data.
size_t buffer_capacity_hint = 2 * ( // 2 output parameters
2 * sizeof(typename T3::Index) // # Rows, # Cols
+ A.rows() * B.cols() * sizeof(typename T3::Scalar)); // Output data
T3 D = run_with_hint(buffer_capacity_hint, multiply_kernel, A, B, C);
const T3 expected = A * B;
T3 C_cpu, C_gpu;
T3 D_cpu = run_on_cpu(multiply_kernel, A, B, C_cpu);
T3 D_gpu = run_on_gpu_with_hint(buffer_capacity_hint,
multiply_kernel, A, B, C_gpu);
// Declare the test fixture.
// For the number of repeats, call the desired subtests.
for(int i = 0; i < g_repeat; i++) {
// Call subtests with different sized/typed inputs.
CALL_SUBTEST( test_add(Eigen::Vector3f()) );
CALL_SUBTEST( test_add(Eigen::Matrix3d()) );
CALL_SUBTEST( test_add(Eigen::MatrixX<int>(10, 10)) );
CALL_SUBTEST( test_add(Eigen::Array44f()) );
CALL_SUBTEST( test_add(Eigen::ArrayXd(20)) );
CALL_SUBTEST( test_add(Eigen::ArrayXXi(13, 17)) );
CALL_SUBTEST( test_multiply(Eigen::Matrix3d(),
Eigen::Matrix3d()) );
CALL_SUBTEST( test_multiply(Eigen::MatrixX<int>(10, 10),
Eigen::MatrixX<int>(10, 10),
Eigen::MatrixX<int>()) );
CALL_SUBTEST( test_multiply(Eigen::MatrixXf(12, 1),
Eigen::MatrixXf(1, 32),
Eigen::MatrixXf()) );