Deep neural networks have achieved remarkable performance in both image classification
and object detection problems, at the cost of a large number of parameters and …

Sparse matrix-vector multiplication (SpMV) is of singular importance in sparse linear
algebra. In contrast to the uniform regularity of dense linear algebra, sparse operations …

The recent switch to parallel microprocessors is a milestone in the history of computing.
Industry has laid out a roadmap for multicore designs that preserves the programming …

Determining the best set of optimizations to apply to a kernel to be executed on the graphics
processing unit (GPU) is a challenging problem. There are large sets of possible …

The massive parallelism of graphics processing units (GPUs) offers tremendous
performance in many high-performance computing applications. While dense linear algebra …

We are witnessing a dramatic change in computer architecture due to the multicore
paradigm shift, as every electronic device from cell phones to supercomputers confronts …

This paper introduces a storage format for sparse matrices, called compressed sparse
blocks (CSB), which allows both Ax and A, x to be computed efficiently in parallel, where A is …

Fast changing, increasingly complex, and diverse computing platforms pose central
problems in scientific computing: How to achieve, with reasonable effort, portable optimal …

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

We present a performance model-driven framework for automated performance tuning
(autotuning) of sparse matrix-vector multiply (SpMV) on systems accelerated by graphics …