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

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. 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. Initial results with the Algebraic MultiGrid kernel demonstrate a nearly 2χ speedup.