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. Thus, developers must balance speed and accuracy when choosing the floating-point precision of their subroutines and data structures. I am investigating techniques to help developers learn about the runtime floating-point behavior of their programs, and to help them make decisions concerning the choice of precision in implementation. I propose to develop methods that will generate floating-point precision configurations, automatically testing and validating them using binary instrumentation. The goal is ultimately to make a recommendation to the developer regarding which parts of the program can be reduced to single-precision. The central thesis is that automated analysis techniques can make recommendations regarding the precision levels that each part of a computer program must use to maintain overall accuracy, with the goal of improving performance on scientific codes.
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