Tuning floating-point precision using dynamic program information and temporal locality

We present a methodology for precision tuning of full applications. These techniques must select a search space composed of either variables or instructions and provide a scalable search strategy. In full application settings one cannot assume compiler support for practical reasons. Thus, an additional important challenge is enabling code refactoring. We argue for an instruction-based search space and we show: 1) how to exploit dynamic program information based on call stacks; and 2) how to exploit the iterative nature of scientific codes, combined with temporal locality. We applied the methodology to tune the implementation of scientific codes written in a combination of Python, CUDA, C++ and Fortran, tuning calls to math exp library functions. The iterative search refinement always reduces the search complexity and the number of steps to solution. Dynamic program information increases search efficacy. Using this approach, we obtain application runtime performance improvements up to 27%.