hipBLAS is a BLAS marshalling library, with multiple supported backends. It sits between the application and a 'worker' BLAS library, marshalling inputs into the backend library and marshalling results back to the application. hipBLAS exports an interface that does not require the client to change, regardless of the chosen backend. Currently, hipBLAS supports rocBLAS and cuBLAS as backends.

Download pre-built packages either from ROCm's package servers or by clicking the github releases tab and manually downloading, which could be newer. Release notes are available for each release on the releases tab.

• sudo apt update && sudo apt install hipblas

The root of this repository has a helper bash script install.sh to build and install hipBLAS on Ubuntu with a single command. It does not take a lot of options and hard-codes configuration that can be specified through invoking cmake directly, but it's a great way to get started quickly and can serve as an example of how to build/install. A few commands in the script need sudo access, so it may prompt you for a password.

• ./install -h -- shows help
• ./install -id -- build library, build dependencies and install (-d flag only needs to be passed once on a system)

If you use a distro other than Ubuntu, or would like more control over the build process, the hipblas build wiki has helpful information on how to configure cmake and manually build.

A list of exported functions from hipblas can be found on the wiki

The hipBLAS interface is compatible with rocBLAS and cuBLAS-v2 APIs. Porting a CUDA application which originally calls the cuBLAS API to an application calling hipBLAS API should be relatively straightforward. For example, the hipBLAS SGEMV interface is

hipblasStatus_t
hipblasSgemv( hipblasHandle_t handle,
              hipblasOperation_t trans,
              int m, int n, const float *alpha,
              const float *A, int lda,
              const float *x, int incx, const float *beta,
              float *y, int incy );

hipBLAS GEMM can process matrices in batches with regular strides. There are several permutations of these API's, the following is an example that takes everything

hipblasStatus_t
hipblasSgemmStridedBatched( hipblasHandle_t handle,
              hipblasOperation_t transa, hipblasOperation_t transb,
              int m, int n, int k, const float *alpha,
              const float *A, int lda, long long bsa,
              const float *B, int ldb, long long bsb, const float *beta,
              float *C, int ldc, long long bsc,
              int batchCount);

hipBLAS assumes matrices A and vectors x, y are allocated in GPU memory space filled with data. Users are responsible for copying data from/to the host and device memory.