Modeling and predicting application performance on hardware accelerators

Authors:
Mitesh R. Meswani;Laura Carrington;Didem Unat;Allan Snavely;Scott Baden;Stephen Poole
Affiliations:
San Diego Supercomputer Center, USA;San Diego Supercomputer Center, USA;University of California at San Diego, USA;San Diego Supercomputer Center, USA;University of California at San Diego, USA;Oak Ridge National Laboratory, USA
Venue:
IISWC '11 Proceedings of the 2011 IEEE International Symposium on Workload Characterization
Year:
2011

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Abstract

Systems with hardware accelerators speedup applications by offloading certain compute operations that can run faster on accelerators. Thus, it is not surprising that many of top500 supercomputers use accelerators. However, in addition to procurement cost, significant programming and porting effort is required to realize the potential benefit of such accelerators. Hence, before building such a system it is prudent to answer the question 'what is the projected performance benefit from accelerators for workloads of interest?' We address this question by way of a performance-modeling framework, which predicts realizable application performance on accelerators speedily and accurately without going to the considerable effort of porting and tuning.