<span class="var-sub_title">Floating-Point Autotuner for CPU-Based Mixed-Precision Applications</span> SC18 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

Floating-Point Autotuner for CPU-Based Mixed-Precision Applications

Authors: Ruidong Gu (North Carolina State University), Paul A. Beata (North Carolina State University), Michela Becchi (North Carolina State University)

Abstract: In this poster, we present the design and development of an autotuning tool for floating-point code. The goal is to balance accuracy and performance in order to produce an efficient and accurate mixed-precision program. The tuner starts by maximizing accuracy through the use of a high-precision library called CAMPARY and then achieves performance gains under a given error bound by tuning down groups of variables and operations from the higher precision down to double precision. We tested our tuning strategy on a computational fluid dynamics benchmark where we show a 4x speedup relative to the fully high-precision version during the iterative tuning process and achieve an average absolute error of 2.8E-16 compared with the reference solution computed using the 256-bit GNU MPFR extended precision library.

Best Poster Finalist (BP): no

Poster: pdf
Poster summary: PDF
Reproducibility Description Appendix: PDF

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