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rocSOLVER

rocSOLVER is a work-in-progress implementation of a subset of LAPACK functionality on the ROCm platform.

Documentation

For a detailed description of the rocSOLVER library, its implemented routines, the installation process and user guide, see the rocSOLVER documentation.

How to build documentation

Please follow the instructions below to build the documentation.

cd docs

pip3 install -r .sphinx/requirements.txt

python3 -m sphinx -T -E -b html -d _build/doctrees -D language=en . _build/html

Building rocSOLVER

To download the rocSOLVER source code, clone this repository with the command:

git clone https://github.com/ROCmSoftwarePlatform/rocSOLVER.git

rocSOLVER requires rocBLAS as a companion GPU BLAS implementation. For more information about rocBLAS and how to install it, see the rocBLAS documentation.

After a standard installation of rocBLAS, the following commands will build rocSOLVER and install to /opt/rocm:

cd rocSOLVER
./install.sh -i

Once installed, rocSOLVER can be used just like any other library with a C API. The header file will need to be included in the user code, and both the rocBLAS and rocSOLVER shared libraries will become link-time and run-time dependencies for the user application.

If you are a developer contributing to rocSOLVER, you may wish to run ./scripts/install-hooks to install the git hooks for autoformatting. You may also want to take a look at the contributing guidelines

Using rocSOLVER

The following code snippet shows how to compute the QR factorization of a general m-by-n real matrix in double precision using rocSOLVER. A longer version of this example is provided by example_basic.cpp in the samples directory. For a description of the rocsolver_dgeqrf function, see the rocSOLVER API documentation.

/////////////////////////////
// example.cpp source code //
/////////////////////////////

#include <algorithm> // for std::min
#include <stddef.h>  // for size_t
#include <vector>
#include <hip/hip_runtime_api.h> // for hip functions
#include <rocsolver/rocsolver.h> // for all the rocsolver C interfaces and type declarations

int main() {
  rocblas_int M;
  rocblas_int N;
  rocblas_int lda;

  // here is where you would initialize M, N and lda with desired values

  rocblas_handle handle;
  rocblas_create_handle(&handle);

  size_t size_A = size_t(lda) * N;          // the size of the array for the matrix
  size_t size_piv = size_t(std::min(M, N)); // the size of array for the Householder scalars

  std::vector<double> hA(size_A);      // creates array for matrix in CPU
  std::vector<double> hIpiv(size_piv); // creates array for householder scalars in CPU

  double *dA, *dIpiv;
  hipMalloc(&dA, sizeof(double)*size_A);      // allocates memory for matrix in GPU
  hipMalloc(&dIpiv, sizeof(double)*size_piv); // allocates memory for scalars in GPU

  // here is where you would initialize matrix A (array hA) with input data
  // note: matrices must be stored in column major format,
  //       i.e. entry (i,j) should be accessed by hA[i + j*lda]

  // copy data to GPU
  hipMemcpy(dA, hA.data(), sizeof(double)*size_A, hipMemcpyHostToDevice);
  // compute the QR factorization on the GPU
  rocsolver_dgeqrf(handle, M, N, dA, lda, dIpiv);
  // copy the results back to CPU
  hipMemcpy(hA.data(), dA, sizeof(double)*size_A, hipMemcpyDeviceToHost);
  hipMemcpy(hIpiv.data(), dIpiv, sizeof(double)*size_piv, hipMemcpyDeviceToHost);

  // the results are now in hA and hIpiv, so you can use them here

  hipFree(dA);                        // de-allocate GPU memory
  hipFree(dIpiv);
  rocblas_destroy_handle(handle);     // destroy handle
}

The exact command used to compile the example above may vary depending on the system environment, but here is a typical example:

/opt/rocm/bin/hipcc -I/opt/rocm/include -c example.cpp
/opt/rocm/bin/hipcc -o example -L/opt/rocm/lib -lrocsolver -lrocblas example.o