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 compete for resources. If the external workload is not considered 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 application without penalizing the existing workload. This approach 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.
|Title of host publication||Proceedings of the 2013 IEEE/ACM International Symposium on Code Generation and Optimization (CGO)|
|Place of Publication||Washington, DC, USA|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||10|
|Publication status||Published - 2013|
- Machine Learning,Parallelism Mapping,Runtime adaptation