When authors present new storage formats for sparse matrices, they usually focus mainly on
a single evaluation criterion, which is the performance of sparse matrix-vector multiplication …

Sparse Matrix-Vector multiplication (SpMV) is an essential computational kernel in many
application scenarios. Tens of sparse matrix formats and implementations have been …

This work optimizes tensor-times-dense matrix multiply (Ttm) for general sparse and semi-
sparse tensors on CPU and NVIDIA GPU platforms. Ttm is a computational kernel in tensor …

In earlier work we have introduced the “Recursive Sparse Blocks”(RSB) sparse matrix
storage scheme oriented towards cache efficient matrix–vector multiplication (SpMV) and …

Iterative solutions of sparse linear systems and sparse eigenvalue problems have a
fundamental role in vital fields of scientific research and engineering. The crucial computing …

When there is a need to store a sparse matrix into a file system, is it worth to convert it first
into some space-efficient storage format? This paper tries to answer such question for the …

Finding an optimal block size for a given sparse matrix forms an important problem for
storage formats that partition matrices into uniformly-sized blocks. Finding a solution to this …

A new multi-threaded variant of the quicksort algorithm called AQsort and its C++/OpenMP
implementation are presented. AQsort operates in place and was primarily designed for high …

Very large sparse matrices are often processed on massively parallel computer systems with
distributed memory architectures consisting of tens or hundreds of thousands of processor …

Sparse matrix-vector multiplication (shortly spMV) and transposed spMV (shortly spMTV) are
the most common routines in the numerical linear algebra. Sparse storage formats describe …