EigenCuda

Offload the Eigen3 matrix matrix multiplication to an Nvidia GPU
using cublas.

CMake Installation

To compile execute:

cmake -H. -Bbuild && cmake --build build

To Debug compile as:

cmake -H. -Bbuild  -DCMAKE_BUILD_TYPE=Debug && cmake --build build

Dependencies

This packages assumes that you have installed the following packages:

• Cudatoolkit
• Eigen3

Usage

Matrix Multiplication

#include "eigencuda.hpp"
#include "cudapipeline.hpp"
using eigencuda::CudaPipeline;
using eigencuda::CudaMatrix;
  // Call the class to handle GPU resources
  CudaPipeline cuda_pip;
Eigen::MatrixXd A = Eigen::MatrixXd::Zero(2, 2);
Eigen::MatrixXd B = Eigen::MatrixXd::Zero(3, 2);
Eigen::MatrixXd C = Eigen::MatrixXd::Zero(3, 2);
Eigen::MatrixXd D = Eigen::MatrixXd::Zero(3, 2);
Eigen::MatrixXd X = Eigen::MatrixXd::Zero(3, 2);
Eigen::MatrixXd Y = Eigen::MatrixXd::Zero(3, 2);
Eigen::MatrixXd Z = Eigen::MatrixXd::Zero(3, 2);
// Define matrices
A << 1., 2., 3., 4.;
B << 5., 6., 7., 8., 9., 10.;
C << 9., 10., 11., 12., 13., 14.;
D << 13., 14., 15., 16., 17., 18.;
X << 23., 34., 31., 46., 39., 58.;
Y << 39., 58., 47., 70., 55., 82.;
Z << 55., 82., 63., 94., 71., 106.;
std::vector<Eigen::MatrixXd> tensor{B, C, D};
std::vector<Eigen::MatrixXd> results(3, Eigen::MatrixXd::Zero(3, 2));
CudaMatrix cuma_A{A, cuda_pip.get_stream()};
CudaMatrix cuma_B{3, 2, cuda_pip.get_stream()};
CudaMatrix cuma_C{3, 2, cuda_pip.get_stream()};
for (Index i = 0; i < 3; i++) {
  cuma_B.copy_to_gpu(tensor[i]);
  cuda_pip.gemm(cuma_B, cuma_A, cuma_C);
  results[i] = cuma_C;
}
// Expected results
bool pred_1 = X.isApprox(results[0]);
bool pred_2 = Y.isApprox(results[1]);
bool pred_3 = Z.isApprox(results[2]);
}