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

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

The sparse triangular solve kernel, SpTRSV, is an important building block for a number of
numerical linear algebra routines. Parallelizing SpTRSV on today's manycore platforms …

Sparse triangular solvers are typically parallelized using level-scheduling techniques, but
parallel efficiency is poor on high-throughput architectures like GPUs. We propose using an …

We study and evaluate performance optimization techniques for the HPCG benchmark on
the newest generation Sunway supercomputer. Specifically, a two-level blocking scheme is …

In this paper, we propose a new preconditioning method that can be seen as a
generalization of block-Jacobi methods, or as a simplification of the sparse approximate …

Sparse triangular solve (SpTRSV) is one of the most important kernels in many real-world
applications. Currently, much research on parallel SpTRSV focuses on level-set construction …

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

In this paper, we present single-and multi-node optimizations of SU2, a widely-used, open-
source Computational Fluid Dynamics application, aimed at improving performance and …

The sparse triangular solve kernels, SpTRSV and SpTRSM, are important building blocks for
a number of numerical linear algebra routines. Parallelizing SpTRSV and SpTRSM on …