Power-aware MPI task aggregation prediction for high-end computing systems

Emerging large-scale systems have many nodes with several processors per node and multiple cores per processor. These systems require effective task distribution between cores, processors and nodes to achieve high levels of performance and utilization. Current scheduling strategies distribute tasks between cores according to a count of available cores, b ut ignore the execution time and energy implications of task aggregation (i.e., grouping multiple tasks within the same node or the same multicore processor). Task aggregation can save significant energy while sustaining or even improving performance. However, choosing an effective task aggregation becomes more difficult as the core count and the options available for task placement increase. We present a framework to predict the performance effect of task aggregation in both computation and communication phases and its impact in terms of execution time and energy of MPI programs. Our results for the N PB 3.2 MPI benchmark suite show that our framework provides accurate predictions leading to substantial energy saving through aggregation (64.87% on average and up to 70.03 %) with tolerable performance loss (under 5%).