Target-Aware Implementation of Real Expressions
Brett Saiki, Jackson Brough, Jonas Regehr, Jes\'us Ponce, Varun, Pradeep, Aditya Akhileshwaran, Zachary Tatlock, Pavel Panchekha

TL;DR
Chassis is a target-aware numerical compiler that optimizes mathematical expressions for speed and accuracy by combining traditional and numerical compilation techniques, demonstrating superior trade-offs across various hardware targets.
Contribution
It introduces a novel target-aware compilation approach that integrates target descriptions with iterative optimization, improving accuracy and performance over existing methods.
Findings
Chassis outperforms Clang by 3.5x in accuracy-performance trade-offs.
Chassis achieves up to 2.0x better results than Herbie.
Demonstrated effectiveness on 9 diverse hardware targets.
Abstract
New low-precision accelerators, vector instruction sets, and library functions make maximizing accuracy and performance of numerical code increasingly challenging. Two lines of worktraditional compilers and numerical compilersattack this problem from opposite directions. Traditional compiler backends optimize for specific target environments but are limited in their ability to balance performance and accuracy. Numerical compilers trade off accuracy and performance, or even improve both, but ignore the target environment. We join aspects of both to produce Chassis, a target-aware numerical compiler. Chassis compiles mathematical expressions to operators from a target description, which lists the real expressions each operator approximates and estimates its cost and accuracy. Chassis then uses an iterative improvement loop to optimize for speed and…
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Taxonomy
TopicsNatural Language Processing Techniques
