Sparse matrix computations are prevalent in many scientific and technical applications. In many simulation applications, the solving of the sparse matrix-vector multiplication (SpMV) is critical for ...

Which solver to choose depends on the nature of your matrix. SuiteSparse is built on top of LAPACK and BLAS, which are pretty low level and FORTRAN-y.

A novel AI-acceleration paper presents a method to optimize sparse matrix multiplication for machine learning models, particularly focusing on structured sparsity. Structured sparsity involves a ...

Sparse matrix computations are pivotal to advancing high-performance scientific applications, particularly as modern numerical simulations and data analyses demand efficient management of large ...

Real PIM systems can provide high levels of parallelism, large aggregate memory bandwidth and low memory access latency, thereby being a good fit to accelerate the widely-used, memory-bound Sparse ...

This newly developed data processing utilizes computing and communications technologies that leverage “sparse matrix” data structures in order to significantly accelerate the performance of vector ...

JACOB BIEN, ROBERT J. TIBSHIRANI, Sparse estimation of a covariance matrix, Biometrika, Vol. 98, No. 4 (DECEMBER 2011), pp. 807-820 ...