Irregular applications such as big graph analysis, material simulations, molecular dynamics
simulations, and finite element analysis have performance problems due to their use of …

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

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

Sparse matrix-vector multiplication (SpMV) is an important kernel in many scientific
applications and is known to be memory bandwidth limited. On modern processors with …

Abstract The Optimized Sparse Kernel Interface (OSKI) is a collection of low-level primitives
that provide automatically tuned computational kernels on sparse matrices, for use by solver …

Several manufacturers have already started to commercialize near-bank Processing-In-
Memory (PIM) architectures, after decades of research efforts. Near-bank PIM architectures …

Sparse general matrix-matrix multiplication (SpGEMM) is one of the most fundamental
building blocks in sparse linear solvers, graph processing frameworks and machine learning …

Algebraic multigrid methods for large, sparse linear systems are a necessity in many
computational simulations, yet parallel algorithms for such solvers are generally …

Several manufacturers have already started to commercialize near-bank Processing-In-
Memory (PIM) architectures, after decades of research efforts. Near-bank PIM architectures …

Important workloads, such as machine learning and graph analytics applications, heavily
involve sparse linear algebra operations. These operations use sparse matrix compression …