hipBLAS is a Basic Linear Algebra Subprograms (BLAS) marshalling library with multiple supported backends. It sits between your application and a 'worker' BLAS library, where it marshals inputs to the backend library and marshals results to your application. hipBLAS exports an interface that doesn't require the client to change, regardless of the chosen backend. Currently, hipBLAS supports rocBLAS and cuBLAS backends.

To use hipBLAS, you must first install rocBLAS, rocSPARSE, and rocSOLVER or cuBLAS.

To build our documentation locally, use the following code:

cd docs

pip3 install -r sphinx/requirements.txt

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

Alternatively, build with CMake:

cmake -DBUILD_DOCS=ON ...

1. Checkout the hipBLAS code using either a sparse checkout or a full clone of the rocm-libraries repository.

   To limit your local checkout to only the hipBLAS project, configure sparse-checkout before cloning. This uses the Git partial clone feature (--filter=blob:none) to reduce how much data is downloaded. Use the following commands for a sparse checkout:

   git clone --no-checkout --filter=blob:none https://github.com/ROCm/rocm-libraries.git
   cd rocm-libraries
   git sparse-checkout init --cone
   git sparse-checkout set projects/hipblas # add projects/rocsolver projects/rocblas projects/hipblas-common to include dependencies
   git checkout develop # or use the branch you want to work with

   To clone the entire rocm-libraries repository, use the following commands. This process takes more time, but is recommended if you want to work with a large number of libraries.

   # Clone rocm-libraries, including hipBLAS, using Git
   git clone https://github.com/ROCm/rocm-libraries.git
   
   # Go to hipBLAS directory
   cd rocm-libraries/projects/hipblas

       hipBLAS requires specific versions of rocBLAS and rocSOLVER. Refer to
       [CMakeLists.txt](https://github.com/ROCm/rocm-libraries/blob/develop/projects/hipblas/library/CMakeLists.txt)
       for details.
   

2. Build hipBLAS using the install.sh script and install it into /opt/rocm/hipblas:

       cd rocm-libraries/projects/hipblas
       ./install.sh -i

The hipBLAS interface is compatible with rocBLAS and cuBLAS-v2 APIs. Porting a CUDA application that originally calls the cuBLAS API to an application that calls the hipBLAS API is 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 by using the strided-batched version of the API:

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 matrix A and vectors x, y are allocated in GPU memory space filled with data. You are responsible for copying data to and from the host and device memory.