Sparse matrix-vector multiplication (SpMV) plays a key role in computational science and
engineering, graph processing, and machine learning applications. Much work on SpMV …

Sparse general matrix-matrix multiplication (SpGEMM) is an important kernel in
computational science and engineering, and has been widely studied on homogeneous …

Algebraic multigrid (AMG) methods are particularly efficient to solve a wide range of sparse
linear systems, due to their good flexibility and adaptability. Even though modern parallel …

Conjugate gradient (CG) and biconjugate gradient stabilized (BiCGSTAB) are effective
methods used for solving sparse linear systems. We in this paper propose Mille-feuille, a …

Sparse matrix dense matrix multiplication (SpMM) is commonly used in applications ranging
from scientific computing to graph neural networks. Typically, when SpMM is executed in a …

Sparse matrix-vector multiplication (SpMV) is a crucial computing kernel with widespread
applications in iterative algorithms. Over the past decades, research on SpMV optimization …

Exploiting matrix symmetry to halve memory footprint offers an opportunity for accelerating
memory-bound computations like Sparse Matrix-Vector Multiplication (SpMV). However …

Sparse Matrix-Vector Multiplication (SpMV) plays a crucial role in scientific computing, but
severe load imbalance among threads restricts its performance. Previous load-balancing …

The sparse matrix-vector multiplication (SpMV) is widely used in large-scale scientific
computing and engineering. However, optimizing SpMV for high-performance digital signal …

Sparse matrix dense matrix multiplication (SpMM) is commonly used in applications ranging
from scienti c computing to graph neural networks. Typically, when SpMM is executed in a …