A BLAS implementation on top of AMD’s Radeon Open Compute ROCm runtime and toolchains. rocBLAS is implemented in the HIP programming language and optimized for AMD’s latest discrete GPUs.

Installing pre-built packages

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 rocblas

Quickstart rocBLAS build

Bash helper build script (Ubuntu only)

The root of this repository has a helper bash script to build and install rocBLAS 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)

Manual build (all supported platforms)

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

Functions supported

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

rocBLAS interface examples

In general, the rocBLAS interface is compatible with CPU oriented Netlib BLAS and the cuBLAS-v2 API, with the explicit exception that traditional BLAS interfaces do not accept handles. The cuBLAS’ cublasHandle_t is replaced with rocblas_handle everywhere. Thus, porting a CUDA application which originally calls the cuBLAS API to a HIP application calling rocBLAS API should be relatively straightforward. For example, the rocBLAS SGEMV interface is


rocblas_sgemv(rocblas_handle handle,
              rocblas_operation trans,
              rocblas_int m, rocblas_int n,
              const float* alpha,
              const float* A, rocblas_int lda,
              const float* x, rocblas_int incx,
              const float* beta,
              float* y, rocblas_int incy);

Batched and strided GEMM API

rocBLAS 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

    rocblas_handle handle,
    rocblas_operation transa, rocblas_operation transb,
    rocblas_int m, rocblas_int n, rocblas_int k,
    const float* alpha,
    const float* A, rocblas_int ls_a, rocblas_int ld_a, rocblas_int bs_a,
    const float* B, rocblas_int ls_b, rocblas_int ld_b, rocblas_int bs_b,
    const float* beta,
          float* C, rocblas_int ls_c, rocblas_int ld_c, rocblas_int bs_c,
    rocblas_int batch_count )

rocBLAS 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. HIP provides memcpy style API’s to facilitate data management.

Asynchronous API

Except a few routines (like TRSM) having memory allocation inside preventing asynchronicity, most of the library routines (like BLAS-1 SCAL, BLAS-2 GEMV, BLAS-3 GEMM) are configured to operate in asynchronous fashion with respect to CPU, meaning these library functions return immediately.