Abstract: Automatically Adapting Programs for Mixed-Precision Floating-Point Computation

Abstract

As scientific computation continues to scale, it is crucial to use floating-point arithmetic processors as efficiently as possible. 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 will result in inaccurate results. In this poster, 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 allows developers to easily experiment with mixed-precision configurations without modifying their source code, and it permits auto-tuning of floating-point precision. We also implemented a simple search algorithm to automatically identify which code regions can use lower precision. We include results for several benchmarks that show both the efficacy and overhead of our tool.