Mapping Medley: Adaptive Parallelism Mapping with Varying Optimization Goals

Abstract

In modern day computing, the performance of parallel programs is bound by the dynamic execution context that includes inherent program behavior, resource requirements, co-scheduled programs sharing the system resources, hardware failures and input data. Besides this dynamic context, the optimization goals are increasingly becoming multi-objective and dynamic such as minimizing execution time while maximizing energy efficiency. Efficiently mapping the parallel threads on to the hardware cores is crucial to achieve these goals. This paper proposes a novel approach to judiciously map parallel programs to hardware in dynamic contexts and goals. It uses a simple, yet novel technique by collecting a set of mapping policies to determine best number of threads that are optimal for specific contexts. It then binds threads to cores for increased affinity. Besides, this approach also determines the optimal DVFS levels for these cores to achieve higher energy efficiency. On extensive evaluation with state-of-art techniques, this scheme outperforms them in the range 1.08x up to 1.21x and 1.39x over OpenMP default.

This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-CONF-696003.