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.
This work was performed when P. C. Wood and R. Singh wereat Purdue University.
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Acknowledgments
We thank the anonymous reviewers for their suggestions and comments on the paper. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract DEAC52-07NA27344 (LLNL-CONF-748618).
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Laguna, I., Wood, P.C., Singh, R., Bagchi, S. (2019). GPUMixer: Performance-Driven Floating-Point Tuning for GPU Scientific Applications. In: Weiland, M., Juckeland, G., Trinitis, C., Sadayappan, P. (eds) High Performance Computing. ISC High Performance 2019. Lecture Notes in Computer Science(), vol 11501. Springer, Cham. https://doi.org/10.1007/978-3-030-20656-7_12
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