Optimization of sparse matrix-vector multiplication on emerging multicore platforms

Conference · Mon Apr 16 04:00:00 EDT 2007

DOI:https://doi.org/10.1145/1362622.1362674· OSTI ID:920852

Williams, S; Oliker, L; Vuduc, R; Shalf, J; Yelick, K; Demmel, J

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 parallelism of unprecedented scale. To fully unleash the potential of these systems, the HPC community must develop multicore specific optimization methodologies for important scientific computations. In this work, we examine sparse matrix-vector multiply (SpMV)--one of the most heavily used kernels in scientific computing--across a broad spectrum of multicore designs. Our experimental platform includes the homogeneous AMD dual-core and Intel quad-core designs, as well as the highly multithreaded Sun Niagara and heterogeneous STI Cell. We present several optimization strategies especially effective for the multicore environment, and demonstrate significant performance improvements compared to existing state-of-the-art serial and parallel SpMV implementations. Additionally, we present key insights into the architectural tradeoffs of leading multicore design strategies, in the context of demanding memory-bound numerical algorithms.

• Research Organization: Lawrence Livermore National Laboratory (LLNL), Livermore, CA
• Sponsoring Organization: USDOE
• DOE Contract Number: W-7405-ENG-48
• OSTI ID: 920852
• Report Number(s): UCRL-CONF-230160
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS
ALGORITHMS
COMPUTER ARCHITECTURE
DESIGN
KERNELS
OPTIMIZATION
PERFORMANCE
SUPERCOMPUTERS