Faster Math Functions, Soundly

TL;DR

OpTuner is an automatic method that optimizes mathematical function implementations at each use site, significantly speeding up numerical code while maintaining acceptable accuracy levels.

Contribution

The paper introduces OpTuner, a novel tool that automatically selects optimal function implementations across speed-accuracy trade-offs for improved performance.

Findings

Achieved a 9% speedup in the POV-Ray ray tracer without output change.

Matched 216 implementations to 89 use sites across benchmarks.

Demonstrated up to 438% speed-up with minimal accuracy loss.

Abstract

Standard library implementations of functions like sin and exp optimize for accuracy, not speed, because they are intended for general-purpose use. But applications tolerate inaccuracy from cancellation, rounding error, and singularities-sometimes even very high error-and many application could tolerate error in function implementations as well. This raises an intriguing possibility: speeding up numerical code by tuning standard function implementations. This paper thus introduces OpTuner, an automatic method for selecting the best implementation of mathematical functions at each use site. OpTuner assembles dozens of implementations for the standard mathematical functions from across the speed-accuracy spectrum. OpTuner then uses error Taylor series and integer linear programming to compute optimal assignments of function implementation to use site and presents the user with a speed-accuracy Pareto curve they can use to speed up their code. In a case study on the POV-Ray ray tracer, OpTuner speeds up a critical computation, leading to a whole program speedup of 9% with no change in the program output (whereas human efforts result in slower code and lower-quality output). On a broader study of 37 standard benchmarks, OpTuner matches 216 implementations to 89 use sites and demonstrates speed-ups of 107% for negligible decreases in accuracy and of up to 438% for error-tolerant applications.