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Achieving high performance for GPU codes requires developers to havesignificant knowledge in parallel programming and GPU architectures, andin-depth understanding of the application. This combination makes itchallenging to find performance optimizations for GPU-based applications,especially in scientific computing. This paper shows that significant speedupscan be achieved on two quite different scientific workloads using the tool,GEVO, to improve performance over human-optimized GPU code. GEVO usesevolutionary computation to find code edits that improve the runtime of amultiple sequence alignment kernel and a SARS-CoV-2 simulation by 28.9% and 29%respectively. Further, when GEVO begins with an early, unoptimized version ofthe sequence alignment program, it finds an impressive 30 times speedup -- aperformance improvement similar to that of the hand-tuned version. This workpresents an in-depth analysis of the discovered optimizations, revealing thatthe primary sources of improvement vary across applications; that most of theoptimizations generalize across GPU architectures; and that several of the mostimportant optimizations involve significant code interdependencies. The resultsshowcase the potential of automated program optimization tools to help reducethe optimization burden for scientific computing developers and enhanceperformance portability for domain-specific accelerators.

Understanding the Power of Evolutionary Computation for GPU Code Optimization