EPROF: An energy/performance/reliability optimization framework for streaming applications

Abstract

Computer systems face increasing challenges in simultaneously meeting an application's energy, performance, and reliability goals. While energy and performance tradeoffs have been studied through different dynamic voltage and frequency scaling (DVFS) policies and power management schemes, tradeoffs of energy and performance with reliability have not been studied for general purpose computing. This is particularly relevant for application domains such as multimedia, where some limited application error tolerance can be exploited to reduce energy [7]. In this paper, we present EPROF, an optimization framework based on Mixed-Integer Linear Programming (MILP) that selects possible schedules for running tasks on multiprocessors in order to minimize energy while meeting constraints on application performance and reliability. We consider parallel applications that express (on task graphs) the performance and reliability goals they need to achieve, and that run on chip multiprocessors made up of heterogeneous processor cores that offer different energy/performance/reliability tradeoffs. For the StreamIt benchmarks [16], EPROF can identify schedules that offer up to 34% energy reduction over a baseline method while achieving the targeted performance and reliability. More broadly, EPROF demonstrates how these three degrees of freedom (energy, performance and reliability) can be flexibly exploited as needed for different applications.