Modeling and predicting performance of high performance computing applications on hardware accelerators

  • Authors:

  • Mitesh R. Meswani;Laura Carrington;Didem Unat;Allan Snavely;Scott Baden;Stephen Poole

  • Affiliations:

  • San Diego Supercomputer Center, La Jolla, CA, USA;San Diego Supercomputer Center, La Jolla, CA, USA;University of California at San Diego, San Diego, CA, USA;San Diego Supercomputer Center, La Jolla, CA, USA;University of California at San Diego, San Diego, CA, USA;Oak Ridge National Laboratory, Oak Ridge, TN, USA

  • Venue:
  • International Journal of High Performance Computing Applications
  • Year:
  • 2013

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Abstract

Hybrid-core systems speedup applications by offloading certain compute operations that can run faster on hardware accelerators. However, such systems require significant programming and porting effort to gain a performance benefit from the accelerators. Therefore, prior to porting it is prudent to investigate the predicted performance benefit of accelerators for a given workload. To address this problem we present a performance-modeling framework that predicts the application performance rapidly and accurately for hybrid-core systems. We present predictions for two full-scale HPC applications-HYCOM and Milc. Our results for two accelerators (GPU and FPGA) show that gather/scatter and stream operations can speedup by as much as a factor of 15 and overall compute time of Milc and HYCOM improve by 3.4% and 20%, respectively. We also show that in order to benefit from the accelerators, 70% of the latency of data transfer time between the CPU and the accelerators needs to be overcome.