Dynamic power-performance adaptation of parallel computation on chip multiprocessors

Previous proposals for power-aware thread-level parallelism on chip multiprocessors (CMPs) mostly focus on multiprogrammed workloads. Nonetheless, parallel computation of a single application is critical in light of the expanding performance demands of important future workloads. This work addresses the problem of dynamically optimizing power consumption of a parallel application that executes on a many-core CMP under a given performance constraint. The optimization space is two-dimensional, allowing changes in the number of active processors and applying dynamic voltage/frequency scaling. We demonstrate that the particular optimum operating point depends nontrivially on the power-performance characteristics of the CMP, the application´s behavior, and the particular performance target. We present simple, low-overhead heuristics for dynamic optimization that significantly cut down on the search effort along both dimensions of the optimization space. In our evaluation of several parallel applications with different performance targets, these heuristics quickly lock on a configuration that yields optimal power savings in virtually all cases.