Sparse-sparse matrix multiplication (SpGEMM) is a computation kernel widely used in
numerous application domains such as data analytics, graph processing, and scientific …

Sparse matrices are widely used in graph and data analytics, machine learning, engineering
and scientific applications. This paper describes and analyzes OuterSPACE, an accelerator …

Sparse matrix-vector multiplication (spMVM) is the most time-consuming kernel in many
numerical algorithms and has been studied extensively on all modern processor and …

General sparse matrix-matrix multiplication (SpGEMM) is a fundamental building block for
numerous applications such as algebraic multigrid method, breadth first search and shortest …

When authors present new storage formats for sparse matrices, they usually focus mainly on
a single evaluation criterion, which is the performance of sparse matrix-vector multiplication …

CUDA, OpenCL, and OpenMP are popular programming models for the multicore
architectures of CPUs and many-core architectures of GPUs or Xeon Phis. At the same time …

General sparse matrix–matrix multiplication (SpGEMM) is a fundamental building block for
numerous applications such as algebraic multigrid method (AMG), breadth first search and …

Sparse problems that dominate a wide range of applications fail to effectively benefit from
high memory bandwidth and concurrent computations in modern high-performance …

As today's organizations are capturing exponentially larger amounts of data than ever, now
is the time for organizations to rethink how they digest that data. Through advanced …

This work describes the challenges presented by porting code to the Intel Xeon Phi
coprocessor, as well as opportunities for optimization and tuning. We use micro …