Smart, Adaptive Mapping of Parallelism in the Presence of External Workload

Given the wide scale adoption of multi-cores in main stream computing, parallel programs rarely execute in isolation and have to share the platform with other applications that com-pete for resources. If the external workload is not consid-ered when mapping a program, it leads to a significant drop in performance. This paper describes an automatic approach that combines compile-time knowledge of the program with dynamic runtime workload information to determine the best adaptive mapping of programs to available resources. This approach delivers increased performance for the target ap-plication without penalizing the existing workload. This ap-proach is evaluated on NAS and SpecOMP parallel bench-mark programs across a wide range of workload scenarios. On average, our approach achieves performance gain of 1.5x over a state-of-art scheme on a 12 core machine