Evaluating Stochastic Floating-Point Superoptimization with STOKE

Bachelor thesis (2022)

Authors

J. Schaap Electrical Engineering, Mathematics and Computer Science

Contributors

D.G. Sprokholt Programming Languages - EEMCS (mentor)
S.S. Chakraborty Programming Languages - EEMCS (mentor)
E. Demirović Algorithmics - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Jop Schaap
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Published Date

23-06-2022

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

The superoptimizer STOKE has previously been shown to be effective at optimizing programs containing floating-point numbers. The STOKE optimizer obtains these results by running a stochastic search over the set of all programs and selecting the best-optimized one. This study aims to find more clearly what floating-point programs STOKE optimizes particularly well and for which ones it fails to find significant rewrites. To answer the research question, STOKE and GCC optimized multiple small programs, and I compared these on execution speed. The results showed numerous cases where STOKE failed to obtain a better optimization than GCC. The results suggest that for specific floating-point functions, there exist limitations in both the test case generator and the STOKE search algorithm that prevent it from finding good optimizations. The findings of this paper suggest further research on the STOKEs test case generator to improve its performance.

Files

Research_paper.pdf
(.pdf | 0.779 Mb)