Evaluation of a semi-static approach to mapping dynamic iterative tasks onto heterogeneous computing systems

To minimize the execution time of an iterative application in a heterogeneous parallel computing environment, an appropriate mapping scheme is needed for matching and scheduling the subtasks of the application onto the processors. When some of the characteristics of the application subtasks are unknown a priori and will change from iteration to iteration during execution time, a semi-static methodology can be employed, that starts with an initial mapping but dynamically decides whether to perform a remapping between iterations of the application, by observing the effects of these dynamic parameters on the application's execution time. The objective of the study is to implement and evaluate such a semi-static methodology. For analyzing the effectiveness of the proposed scheme, it is compared with two extreme approaches: a completely dynamic approach using a fast mapping heuristic and an ideal approach that uses a genetic algorithm online but ignores the time for remapping. Experimental results indicate that the semi-static approach outperforms the dynamic approach and is reasonably close to the ideal but infeasible approach