Energy Saving and Thermal Management Opportunities in a Workload-Aware MPI Runtime for a Scientific HPC Computing Node

With the advent of a new generation of supercomputers characterized by tightly-coupled integration of a large-number of powerful processing cores in the same die, energy and temperature walls are looming threats to the growth in computational power. Scientific computing is characterized by a single application running in parallel on multiple nodes and cores until termination. The message-passing programming model is a widely adopted paradigm for explicitly handling data-sharing between processes of the same application. As an effect of the MPI communication patterns among different processes, the application is characterized by phases which can be exploited by OS power manager. In addition, the large number of cores integrated in the same silicon die introduces large thermal capacitance as well as on-die thermal heterogeneity. Jointly exploiting local workload unbalance and computational node heterogeneity can open interesting opportunities for advanced thermal and energy management. In this paper, we present an exploratory work to assess these opportunities and their limiting factors. We analyze application workload and we identify opportunities to reduce energy consumption and their impact on performance. We test our methodology on a widely-used quantum-chemistry application demonstrating potential benefits of combining the application flow with power and thermal management strategies.