Abstract Sparse Matrix Vector Multiplication (SpMV) is one of the most basic problems in scientific and en-gineering computations. It is the basic operation in many realms, such as solving linear …

KPM can be broadly classified as a polynomial-based expansion scheme, with the corresponding simple iterative structure of the basic algorithm that addresses the large sparse matrix from the …

modi ed sliced ELLPACK format. Blocking a set of vectors and processing them simultaneously accelerates the computation of a set of consecutive SpMVs signi -cantly.

Serpens features (1) a general-purpose design, (2) memory-centric processing engines, and (3) index coalescing to support the efi-cient processing of arbitrary SpMVs. From the evaluation of …

KPM can be broadly classified as a polynomial-based expansion scheme, with the corresponding simple iterative structure of the basic algorithm that addresses the large sparse matrix from the …

Scientific computing applications require efficient multi-plication of one or more dense vectors by a sparse matrix. These kernels are referred to as SpMV or SpMM respectively, and often arise …

The marker-and-cell (MAC) method [1] and material-point method (MPM) [2] have been widely used in the geodynamics community because of their ability to accurately track post-failure …