Online work maximization under a peak temperature constraint

摘要

Increasing power densities and the high cost of low thermal resistance packages and cooling solutions make it impractical to design processors for worst-case temperature scenarios. As a result, packages and cooling solutions are designed for less than worst-case power densities and dynamic voltage and frequency scaling (DVFS) is used to prevent dangerous on-chip temperatures at run time. Unfortunately, DVFS can cause unpredicted drops in performance (e.g., long response times). We propose and optimally solve the problem of thermally-constrained online work maximization for general-purpose computing systems on uniprocessors with discrete speed levels and non-negligible transition overheads. Simulation results show that our approach completes 47.7% on average and up to 68.0% more cycles than a naive policy.