By the same authors

From the same journal

On the acceleration of wavefront applications using distributed many-core architectures

Research output: Contribution to journalArticlepeer-review


  • S. J. Pennycook
  • S. D. Hammond
  • G. R. Mudalige
  • S. A. Wright
  • S. A. Jarvis


Publication details

JournalComputer Journal
DateE-pub ahead of print - 31 Jul 2011
DatePublished (current) - 1 Feb 2012
Issue number2
Number of pages16
Pages (from-to)138-153
Early online date31/07/11
Original languageEnglish


In this paper we investigate the use of distributed graphics processing unit (GPU)-based architectures to accelerate pipelined wavefront applications - a ubiquitous class of parallel algorithms used for the solution of a number of scientific and engineering applications. Specifically, we employ a recently developed port of the LU solver (from the NAS Parallel Benchmark suite) to investigate the performance of these algorithms on high-performance computing solutions from NVIDIA (Tesla C1060 and C2050) as well as on traditional clusters (AMD/InfiniBand and IBM BlueGene/P). Benchmark results are presented for problem classes A to C and a recently developed performance model is used to provide projections for problem classes D and E, the latter of which represents a billion-cell problem. Our results demonstrate that while the theoretical performance of GPU solutions will far exceed those of many traditional technologies, the sustained application performance is currently comparable for scientific wavefront applications. Finally, a breakdown of the GPU solution is conducted, exposing PCIe overheads and decomposition constraints. A new k-blocking strategy is proposed to improve the future performance of this class of algorithm on GPU-based architectures.

Bibliographical note

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    Research areas

  • CUDA, GPU, many-core computing, optimization, performance modelling, wavefront

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