Unstructured grid applications on GPU: Performance analysis and improvement

Lizandro Solano-Quinde, Zhi Jian Wang, Brett Bode, Arun K. Somani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Performance of applications running on GPUs is mainly affected by hardware occupancy and global memory latency. Scientific applications that rely on analysis using unstructured grids could benefit from the high performance capabilities provided by GPUs, however, its memory access pattern and algorithm limit the potential benefits. In this paper we analyze the algorithm for unstructured grid analysis on the basis of hardware occupancy and memory access efficiency. In general, the algorithm can be divided into three stages: cell-oriented analysis, edge-oriented analysis and information update, which present different memory access patterns. Based on the analysis we modify the algorithm to make it suitable for GPUs. The proposed algorithm aims for high hardware occupancy and efficient global memory access. Finally, through implementation we show that our design achieves up to 88 times speedup compared to the sequential CPU version.

Original languageEnglish (US)
Title of host publicationProceedings of the 4th Workshop on General Purpose Processing on Graphics Processing Units, GPGPU-4
DOIs
StatePublished - Apr 27 2011
Event4th Workshop on General Purpose Processing on Graphics Processing Units, GPGPU-4 - Newport Beach, CA, United States
Duration: Mar 5 2011Mar 5 2011

Publication series

NameACM International Conference Proceeding Series

Other

Other4th Workshop on General Purpose Processing on Graphics Processing Units, GPGPU-4
Country/TerritoryUnited States
CityNewport Beach, CA
Period3/5/113/5/11

Keywords

  • CUDA
  • GPGPU
  • GPU
  • unstructured grid

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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