hipSPARSE is a SPARSE marshalling library, with multiple supported backends. It sits between the application and a 'worker' SPARSE library, marshalling inputs into the backend library and marshalling results back to the application. hipSPARSE exports an interface that does not require the client to change, regardless of the chosen backend. Currently, hipSPARSE supports rocSPARSE and cuSPARSE 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 hipsparse
The root of this repository has a helper bash script install.sh
to build and install hipSPARSE 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 hipSPARSE build wiki has helpful information on how to configure cmake and manually build.
A list of exported functions from hipSPARSE can be found on the wiki.
The hipSPARSE interface is compatible with rocSPARSE and cuSPARSE-v2 APIs. Porting a CUDA application which originally calls the cuSPARSE API to an application calling hipSPARSE API should be relatively straightforward. For example, the hipSPARSE SCSRMV interface is
hipsparseStatus_t
hipsparseScsrmv(hipsparseHandle_t handle,
hipsparseOperation_t transA,
int m, int n, int nnz, const float *alpha,
const hipsparseMatDescr_t descrA,
const float *csrValA,
const int *csrRowPtrA, const int *csrColIndA,
const float *x, const float *beta,
float *y);
hipSPARSE assumes matrix 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.