The symmetric sparse matrix-vector multiplication (SymmSpMV) is an important building
block for many numerical linear algebra kernel operations or graph traversal applications …

The Sunway TaihuLight, equipped with 10 million cores, is currently the world's third fastest
supercomputer. SpMV is one of core algorithms in many high-performance computing …

In this paper, we wish to push the limit of real-time cloth and deformable body simulation to a
higher level with 50K to 500K vertices, based on the development of a novel GPU-based …

Many real-world computations involve sparse data structures in the form of sparse matrices.
A common strategy for optimizing sparse matrix operations is to reorder a matrix to improve …

This paper presents a compiler and runtime framework for parallelizing sparse matrix
computations that have loop-carried dependences. Our approach automatically generates a …

The Sparse Matrix-Vector Multiplication (SpMV) kernel ranks among the most important and
thoroughly studied linear algebra operations, as it lies at the heart of many iterative methods …

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

Exploiting the numeric symmetry in sparse matrices to reduce their memory footprint is very
tempting for optimizing the memory-bound Sparse Matrix-Vector Multiplication (SpMV) …

Sparse matrix multiplication (SpMM) plays a critical role in high-performance computing
applications, such as deep learning, image processing, and physical simulation. Field …

This paper proposes a novel ant colony hyperheuristic approach for reordering the rows and
columns of symmetric positive definite matrices. This ant colony hyperheuristic approach …