Multithreaded sparse matrix-matrix multiplication for many-core and GPU architectures

Multi-threaded Sparse Matrix-Matrix Multiplication for Many-Core and GPU Architectures

Sparse Matrix-Matrix multiplication is a key kernel that has applications in several domains such as scientific computing and graph analysis. Several algorithms have been studied in the past for this foundational kernel. In this paper, we develop parallel algorithms for sparse matrixmatrix multiplication with a focus on performance portability across different high performance computing archite...

Automatically Tuning Sparse Matrix-Vector Multiplication for GPU Architectures

Graphics processors are increasingly used in scientific applications due to their high computational power, which comes from hardware with multiple-level parallelism and memory hierarchy. Sparse matrix computations frequently arise in scientific applications, for example, when solving PDEs on unstructured grids. However, traditional sparse matrix algorithms are difficult to efficiently parallel...

Accelerating Sparse Matrix Vector Multiplication on Many-Core GPUs

Many-core GPUs provide high computing ability and substantial bandwidth; however, optimizing irregular applications like SpMV on GPUs becomes a difficult but meaningful task. In this paper, we propose a novel method to improve the performance of SpMV on GPUs. A new storage format called HYB-R is proposed to exploit GPU architecture more efficiently. The COO portion of the matrix is partitioned ...

Sparse Matrix-Vector Multiplication on a GPU

Sparse Matrix-vector Multiplication on Nvidia Gpu

In this paper, we present our work on developing a new matrix format and a new sparse matrix-vector multiplication algorithm. The matrix format is HEC, which is a hybrid format. This matrix format is efficient for sparse matrix-vector multiplication and is friendly to preconditioner. Numerical experiments show that our sparse matrix-vector multiplication algorithm is efficient on