Evaluating Stochastic Floating-Point Superoptimization with STOKE

Evaluating Stochastic Floating-Point Superoptimization with STOKE

Schaap, Jop (TU Delft Electrical Engineering, Mathematics and Computer Science)

Sprokholt, D.G. (mentor)
Chakraborty, S.S. (mentor)
Demirović, E. (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2022-06-23

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.

superoptimization
STOKE
floating-point
Markov Chain Monte Carlo
stochastic search

http://resolver.tudelft.nl/uuid:8fc5ced7-ecc8-4ad9-8b3d-8a854a27578e

Student theses

bachelor thesis

© 2022 Jop Schaap