Performance of Runtime Optimization on BLAST

  • Authors:
  • Abhinav Das;Jiwei Lu;Howard Chen;Jinpyo Kim;Pen-Chung Yew;Wei-Chung Hsu;Dong-Yuan Chen

  • Affiliations:
  • University of Minnesota;University of Minnesota;University of Minnesota;University of Minnesota;University of Minnesota;University of Minnesota;Intel Corporation

  • Venue:
  • Proceedings of the international symposium on Code generation and optimization
  • Year:
  • 2005

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Abstract

Optimization of a real world application BLAST is used to demonstrate the limitations of static and profile-guided optimizations and to highlight the potential of runtime optimization systems. We analyze the performance profile of this application to determine performance bottlenecks and evaluate the effect of aggressive compiler optimizations on BLAST. We find that applying common optimizations (e.g. O3) can degrade performance. Profile guided optimizations do not show much improvement across the board, as current implementations do not address critical performance bottlenecks in BLAST. In some cases, these optimizations lower performance significantly due to unexpected secondary effects of aggressive optimizations. We also apply runtime optimization to BLAST using the ADORE framework. ADORE is able to detect performance bottlenecks and deploy optimizations resulting in performance gains up to 58% on some queries using data cache prefetching.