Migration is a known technique to offer process rescheduling, being especially pertinent for Bulk Synchronous Parallel (BSP) programs. Such programs are organized in a set of supersteps, in which the slowest process always determines the synchronization time. This approach motivated us to develop a first model called MigBSP, which combines computation, communication, and migration costs metrics for process rescheduling decisions. In this paper, a new model named MigPF enhances our previous work in three aspects: (i) a different algorithm for detecting imbalance situations, which considers the performance of each processor, including its enclosed processes, instead of individual times of each process; (ii) an improvement on the rescheduling reactivity through shortening the interval for the next migration call when imbalance situations arise; (iii) a new algorithm for self-organizing the migratable processes and their destinations. Particularly, this third item represents our main scientific contribution, not only in terms of the MigBSP context, but also in a broader one that covers the entire BSP landscape. The algorithm assembles possible rescheduling plans (where n is the number of processes) and provides a prediction function (pf) that chooses the most profitable rescheduling plan when compared to the current mapping. We developed a MigPF prototype with the Adaptive MPI (AMPI) library, which offers a standard framework for implementing migration-based load balancing policies. We tested this prototype against other built-in AMPI rescheduling policies with two scientific applications: shear sorting and fractal image compression. The results revealed performance gains up to 41% and an overhead limited to 5% when migrations do not take place.

Future Generation Computer Systems