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2019 | OriginalPaper | Buchkapitel

GPUMixer: Performance-Driven Floating-Point Tuning for GPU Scientific Applications

verfasst von : Ignacio Laguna, Paul C. Wood, Ranvijay Singh, Saurabh Bagchi

Erschienen in: High Performance Computing

Verlag: Springer International Publishing

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Abstract

We present GPUMixer, a tool to perform mixed-precision floating-point tuning on scientific GPU applications. While precision tuning techniques are available, they are designed for serial programs and are accuracy-driven, i.e., they consider configurations that satisfy accuracy constraints, but these configurations may degrade performance. GPUMixer, in contrast, presents a performance-driven approach for tuning. We introduce a novel static analysis that finds Fast Imprecise Sets (FISets), sets of operations on low precision that minimize type conversions, which often yield performance speedups. To estimate the relative error introduced by GPU mixed-precision, we propose shadow computations analysis for GPUs, the first of this class for multi-threaded applications. GPUMixer obtains performance improvements of up to \(46.4\%\) of the ideal speedup in comparison to only \(20.7\%\) found by state-of-the-art methods.

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Titel: GPUMixer: Performance-Driven Floating-Point Tuning for GPU Scientific Applications
verfasst von: Ignacio Laguna
Paul C. Wood
Ranvijay Singh
Saurabh Bagchi
Verlag: Springer International Publishing
Buch: High Performance Computing
Print ISBN: 978-3-030-20655-0

Electronic ISBN: 978-3-030-20656-7

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-20656-7_12

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