Power-Performance Implications of Thread-level Parallelism on Chip Multiprocessors

Abstract

We discuss power-performance implications of running parallel applications on chip multiprocessors (CMPs). First, we develop an analytical model that, for the first time, puts together parallel efficiency, granularity, and voltage/frequency scaling, to quantify the performance and power consumption, delivered by a CMP running a parallel code. Then, we conduct detailed simulations of parallel applications running on a power-performance CMP model. Our experiments confirm that our analytical model predicts power-performance behavior reasonably well. Both analytical and experimental models show that parallel computing can bring significant power savings and still meet a given performance target, by choosing granularity and voltage/frequency levels judiciously. The particular choice, however, is dependent on the application's parallel efficiency curve and the process technology utilized, which our model captures. Likewise, analytical model and experiments show the effect of a limited power budget on the application's scalability curve. In particular, we show that a limited power budget can cause a rapid performance degradation beyond a number of cores, even in the case of applications with excellent scalability properties. On the other hand, our experiments show that power-thrifty memory-bound applications can actually enjoy better scalability than more "nominally scalable" applications (i.e., without regard to power) when a limited power budget is in place