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Accelerating S3D: A GPGPU Case Study

  • Kyle Spafford
  • Jeremy Meredith
  • Jeffrey Vetter
  • Jacqueline Chen
  • Ray Grout
  • Ramanan Sankaran
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6043)

Abstract

The graphics processor (GPU) has evolved into an appealing choice for high performance computing due to its superior memory bandwidth, raw processing power, and flexible programmability. As such, GPUs represent an excellent platform for accelerating scientific applications. This paper explores a methodology for identifying applications which present significant potential for acceleration. In particular, this work focuses on experiences from accelerating S3D, a high-fidelity turbulent reacting flow solver. The acceleration process is examined from a holistic viewpoint, and includes details that arise from different phases of the conversion. This paper also addresses the issue of floating point accuracy and precision on the GPU, a topic of immense importance to scientific computing. Several performance experiments are conducted, and results are presented from the NVIDIA Tesla C1060 GPU. We generalize from our experiences to provide a roadmap for deploying existing scientific applications on heterogeneous GPU platforms.

Keywords

Single Precision Graphic Processor Memory Access Pattern Precision Version Timestep Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Kyle Spafford
    • 1
  • Jeremy Meredith
    • 1
  • Jeffrey Vetter
    • 1
  • Jacqueline Chen
    • 2
  • Ray Grout
    • 2
  • Ramanan Sankaran
    • 1
  1. 1.Oak Ridge National Laboratory 
  2. 2.Sandia National Laboratories 

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