NDLI: Efficient search for inputs causing high floating-point errors

Content Provider	ACM Digital Library
Author	Gopalakrishnan, Ganesh Chiang, Wei-Fan Rakamaric, Zvonimir Solovyev, Alexey
Abstract	Tools for floating-point error estimation are fundamental to program understanding and optimization. In this paper, we focus on tools for determining the input settings to a floating point routine that maximizes its result error. Such tools can help support activities such as precision allocation, performance optimization, and auto-tuning. We benchmark current abstraction-based precision analysis methods, and show that they often do not work at scale, or generate highly pessimistic error estimates, often caused by non-linear operators or complex input constraints that define the set of legal inputs. We show that while concrete-testing-based error estimation methods based on maintaining shadow values at higher precision can search out higher error-inducing inputs, suit able heuristic search guidance is key to finding higher errors. We develop a heuristic search algorithm called Binary Guided Random Testing (BGRT). In 45 of the 48 total benchmarks, including many real-world routines, BGRT returns higher guaranteed errors. We also evaluate BGRT against two other heuristic search methods called ILS and PSO, obtaining better results.
Starting Page	43
Ending Page	52
Page Count	10
File Format	PDF
ISSN	03621340 15581160
DOI	10.1145/2692916.2555265
Journal	ACM SIGPLAN Notices (SIGP)
Volume Number	49
Issue Number	8
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	1983-05-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Floating-point error estimation methods Sequential and parallel programming Guided search
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Software

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