Graphics processing units (GPUs) have delivered a remarkable performance for a variety of
high performance computing (HPC) applications through massive parallelism. One such …

The massive parallelism provided by the graphics processing units (GPUs) offers
tremendous performance in many high-performance computing applications. One such …

The sparse matrix‐vector multiplication (SpMV) is of great importance in scientific
computations. Graphics processing unit (GPU)‐accelerated SpMVs for large‐sized problems …

A wide range of applications in engineering and scientific computing are based on the
sparse matrix computation. There exist a variety of data representations to keep the non …

Sparse matrix–vector multiplication (SpMV) kernel dominates the computing cost in
numerous applications. Most of the existing studies dedicated to improving this kernel have …

Sparse matrix-vector multiplication (SpMV) is a fundamental building block for various
numerical computing applications. However, most existing GPU-SpMV approaches may …

Sparse matrix–vector multiplication (SpMV) is one of the most important kernels in high-
performance computing (HPC), yet SpMV normally suffers from ill performance on many …

Sparse matrix vector multiplication (SpMV) is the dominant kernel in scientific simulations.
Many-core processors such as GPUs accelerate SpMV computations with high parallelism …

The sparse matrix-vector multiplication (SpMV) is a fundamental computational kernel used
in science and engineering. As a result, the performance of a large number of applications …

The rising popularity of the graphics processing unit (GPU) across various numerical
computing applications triggered a breakneck race to optimize key numerical kernels and in …