The significant presence that many-core devices like GPUs have these days, and their
enormous computational power, motivates the study of sparse matrix operations in this …

The sparse matrix multiplication (SpGeMM) increased its importance in the last years due to
its data science and machine learning applications. Consequently, considerable research …

Sparse linear algebra kernels are memory-bound routines, and their performance varies
significantly according to the non-null pattern of the sparse matrix operands. The impressive …

Sparse general matrix-matrix multiplication (SpGEMM) is challenging especially on graphic
accelerators. Existing solutions do not fully utilize the shared memory of the graphics …

Historically, the sparse matrix-vector product (SpMV) acceleration concentrated the central
part of the research efforts devoted to sparse linear algebra kernels. Lately, other …

Kernels in linear algebra are memory-bounded routines and their performance is dependent
on the sparsity pattern of the matrix operands. Since memory is many times slower than …

The significant presence that many-core devices like GPUs have these days, and their
enormous computational power, motivates the study of sparse matrix operations in this …

The significant presence that many-core devices like GPUs have these days, and their
enormous computational power, motivates the study of sparse matrix operations in this …