An approach to resource-aware co-scheduling for CMPs

We develop real-time scheduling techniques for improving performance and energy for multiprogrammed workloads that scale non-uniformly with increasing thread counts. Multithreaded programs generally deliver higher throughput than single-threaded programs on chip multiprocessors, but performance gains from increasing threads decrease when there is contention for shared resources. We use analytic metrics to derive local search heuristics for creating efficient multiprogrammed, multithreaded workload schedules. Programs are allocated fewer cores than requested, and scheduled to space-share the CMP to improve global throughput. Our holistic approach attempts to co-schedule programs that complement each other with respect to shared resource consumption. We find application co-scheduling for performance and energy in a resource-aware manner achieves better results than solely targeting total throughput or concurrently co-scheduling all programs. Our schedulers improve overall energy delay (E*D) by a factor of 1.5 over time-multiplexed gang scheduling. \ua9 2010 ACM