Live Webcast 15th Annual Charm++ Workshop

The recent trend of rapid increase in the number of cores per chip has resulted in vast amounts of on-node parallelism. These high core counts result in hardware variability that introduces imbalance. Applications are also becoming more complex themselves, result- ing in dynamic load imbalance. Load imbalance of any kind can result in loss of performance and decrease in system utilization. In this paper, we propose a new integrated runtime system that adds OpenMP shared-memory parallelism to the Charm++ distributed programming model to improve load balancing on distributed sys- tems. Our proposal utilizes an infrequent periodic assignment of work to cores based on load measurement, in combination with tasks created via OpenMP’s parallel loop construct from each core to handle load imbalance. We demonstrate the bene ts of using this integrated runtime system on the LLNL ASC proxy application Lassen, achieving speedups of 50% over runs without any load bal- ancing and 10% over existing distributed-memory-only balancing schemes in Charm++.