Automatically adapting programs for mixed-precision floating-point computation

Authors:
Michael O. Lam;Jeffrey K. Hollingsworth;Bronis R. de Supinski;Matthew P. Legendre
Affiliations:
University of Maryland, College Park, MD, USA;University of Maryland, College Park, MD, USA;Lawrence Livermore National Laboratory, Livermore, CA, USA;Lawrence Livermore National Laboratory, Livermore, CA, USA
Venue:
Proceedings of the 27th international ACM conference on International conference on supercomputing
Year:
2013

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Abstract

As scientific computation continues to scale, efficient use of floating-point arithmetic processors is critical. Lower precision allows streaming architectures to perform more operations per second and can reduce memory bandwidth pressure on all architectures. However, using a precision that is too low for a given algorithm and data set leads to inaccurate results. In this paper, we present a framework that uses binary instrumentation and modification to build mixed-precision configurations of existing binaries that were originally developed to use only double-precision. This framework allows developers to explore mixed-precision configurations without modifying their source code, and it permits autotuning of floating-point precision. We include a simple search algorithm to automate identification of code regions that can use lower precision. Our results for several benchmarks show that our framework is effective and incurs low overhead (less than 10X in most cases). In addition, we demonstrate that our tool can replicate manual conversions and suggest further optimization; in one case, we achieve a speedup of 2X.