AMPT-GA: automatic mixed precision floating point tuning for GPU applications

Pradeep V. Kotipalli, Ranvijay Singh, Paul Wood, Ignacio Laguna, Saurabh Bagchi

Abstract: Mixed precision computations improve high performance computing throughput for applications that can tolerate decreased mathematical precision in their computations. Native mixed precision computation is commonplace in today's GPGPU accelerators where it is applied to applications with well-known tolerances for reduced mathematical precision. Applications with stricter accuracy needs lack support for selecting precisions that both improve performance and satisfy these accuracy requirements. Prior works have focused primarily on accuracy, leaving performance concerns such as the overhead of casting unanswered in GPGPU contexts. In this paper, we present a system called AMPT-GA that selects application-level data precisions to maximize performance while satisfying accuracy constraints. We combine static analysis for casting-aware performance modeling with dynamic analysis for modeling and enforcing precision constraints. We further improve our optimizations with application-aware mutations in our genetic algorithm-based search function. AMPT-GA improves the performance efficiency of our target applications more than the prior state-of-the-art approach called Precimonious. AMPT-GA outperforms Precimonious in efficiency by 14--63%.