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• W2122738744 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." @default.
• W2122738744 created "2016-06-24" @default.
• W2122738744 creator A5021217615 @default.
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• W2122738744 date "2014-02-06" @default.
• W2122738744 modified "2023-09-24" @default.
• W2122738744 title "Efficient search for inputs causing high floating-point errors" @default.
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• W2122738744 doi "https://doi.org/10.1145/2555243.2555265" @default.
• W2122738744 hasPublicationYear "2014" @default.
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