SMaT: (S)parse (Ma)trix Matrix (T)ensor Core-accelerated library

Requirements

Hardware

We run our experiments on the Swiss National Computing Center’s Ault compute cluster. Each node is equipped with a single NVIDIA A100-SXM4-40GB GPU, and AMD EPYC 7742 @ 2.25GHz CPU. The A100 driver version is 530.30.02.

Software

All experiments were executed using the GCC 12.3.0 compiler, NVIDIA nvcc v12.0, NVIDIA cuSPARSE v12.0, NVIDIA CUDA Toolkit v12.0, Python 3.9, and the following Python libraries: Pandas, Matplotlib, Numpy, Scipy, and Seaborn

To create a conda environment:

conda env create -f smat_env.yml
conda activate smat

Datasets

For preparing the matrices run the following:

• SuiteSparse Collection:

python download_suitesparse.py

• Synthetic band matrices:

python generate_matrices.py

Compiling

In order to compile the library:

cd src/cuda_hgemm
source compile.sh

Running The Code

Further details and scripts for reproducing experiments can be found: here.