Energy-efficient computing for HPC workloads on heterogeneous manycore chips.

ABSTRACTPower and energy efficiency is one of the major challenges to achieve exascale computing in the next several years. While chips operating at low voltages have been studied to be highly energy-efficient, low voltage operations lead to heterogeneity across cores within the microprocessor chip. In this work, we study chips with low voltage operation and discuss programming systems, and performance modeling in the presence of heterogeneity. We propose an integer linear programming based approach for selecting optimal configuration of a chip that minimizes its energy consumption. We obtain an average of 26% and 10.7% savings in energy consumption of the chip for two HPC mini-applications - miniMD and Jacobi, respectively. We also evaluate the energy savings with execution time constraints, using the proposed approach. These energy savings are significantly more than the savings by sub-optimal configurations obtained from heuristics.