Fine Tuning Algorithmic Skeletons

Algorithmic skeletons correspond to a high-level programming model that takes advantage of nestable programming patterns to hide the complexity of parallel/distributed applications. Programmers have to: define the nested skeleton structure, and provide the muscle (sequential) portions of code which are specific to a problem. An inadequate structure definition, or inefficient muscle code can lead to performance degradation of the application. Related research has focused on the problem of performing optimization to the skeleton structure. Nevertheless, to our knowledge, no focus has been done on how to aide the programmer to write performance efficient muscle code. We present the Calcium skeleton framework as the environment in which to perform fine tuning of algorithmic skeletons. Calcium provides structured parallelism in Java using ProActive. ProAcitve is a grid middleware implementing the active object programming model, and providing a deployment framework. Finally, using a skeleton solution of the NQueens counting problems in Calcium, we validate the fine tuning approach on a grid environment.