Maximizing Throughput on Power-Bounded HPC Systems

Future extreme scale computing systems will be imposed with definite power budgets that are less than the sum of overprovisioned hardwares' peak power. For such systems, maximizing system throughput for the given power is a necessity that requires innovative technologies. In this work, we investigate power-judicious job scheduling coupled with multilevel power coordination to maximize system throughput on power-bounded computer clusters. The job scheduling co-locates and co-runs complementary jobs with respect to performance and power attributes within and across nodes, while the power coordination accordingly distributes power among nodes, sockets, and memory based on the jobs' demands. Experimental results on a multicore based cluster show that the proposed scheduler improves system throughput by over 20% compared with counterparts at the node and cluster levels.