Work

Quantifying the effect of matrix structure on multithreaded performance of the SpMV kernel

Year: 2014

Type: conference-paper

Abstract: Sparse matrix-vector multiplication (SpMV) is the core operation in many common network and graph analytics, but poor performance of the SpMV kernel handicaps these applications. This work quantifies the effect of matrix structure on SpMV performance, using Intel's VTune tool for the Sandy Bridge architecture. Two types of sparse matrices are considered: finite difference (FD) matrices, which are structured, and R-MAT matrices, which are unstructured. Analysis of cache behavior and prefetcher activity reveals that the SpMV kernel performs far worse with R-MAT matrices than with FD matrices, due to the difference in matrix structure. To address the problems caused by unstructured matrices, novel architecture improvements are proposed.

Source: IEEE High Performance Extreme Computing Conference

DOI: 10.1109/hpec.2014.7040991

Authors: Daniel Kimball, Elizabeth Michel, Paul Keltcher, Michael M. Wolf

Institutions: MIT Lincoln Laboratory, Massachusetts Institute of Technology

Language: English

Topic: Parallel Computing and Optimization Techniques

Subfield: Hardware and Architecture

Field: Computer Science

Domain: Physical Sciences

Open Access status: green