Applying Logistic Regression Model on HPX Parallel Loops

The performance of many parallel applications depend on the loop-level parallelism. However, manually parallelizing all loops may result in degrading parallelization performance, as some of the loops cannot scale desirably on more number of threads. In addition, the overheads of manually setting chunk sizes might avoid an application to reach its maximum parallel performance. We illustrate how machine learning techniques can be applied to address these challenges. In this research, we develop a framework that is able to automatically capture the static and dynamic information of a loop. Moreover, we advocate a novel method for determining execution policy and chunk size of a loop within an application by considering those captured information implemented within our learning model. Our evaluated execution results show that the proposed technique can speed up the execution process up to 45%.