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The emergence of power as a first-class design constraint has fueled the proposal of a growing number of run-time power optimizations. Many of these optimizations trade-off power saving opportunity for a variable performance loss which depends on application characteristics and program phase. Furthermore, the potential benefits of these optimizations are sometimes non-additive, and it can be difficult to identify which combinations of these optimizations to apply. Trial-and-error approaches have been proposed to adaptively tune a processor. However, in a chip multiprocessor, the cost of individually configuring each core under a wide range of optimizations would be prohibitive under simple trial-and-error approaches.In this work, we introduce an adaptive, multi-optimization power saving strategy for multi-core power management. Specifically, we solve the problem of meeting a global chip-wide power budget through run-time adaptation of highly configurable processor cores. Our approach applies analytic modeling to reduce exploration time and decrease the reliance on trial-and-error methods. We also introduce risk evaluation to balance the benefit of various power saving optimizations versus the potential performance loss. Overall, we find that our approach can significantly reduce processor power consumption compared to alternative optimization strategies.

In a few technology generations, limitations of fabrication processes will make accurate design time power estimates a daunting challenge. Static leakage current which comprises a significant fraction of total power due to large on-chip caches, is exponentially dependent on widely varying physical parameters such as gate length, gate oxide thickness, and dopant ion concentration. In large structures like onchip caches, this may mean that one portion of a cache may consume an order of magnitude larger static power than equivalently sized regions.Under this climate, egalitarian management of physical resources is clearly untenable. In this paper, we analyze the effects of within-die and die-to-die leakage variation for onchip caches. We then propose way prioritization, a manufacturing variation aware scheme that minimizes cache leakage energy. Our results show that significant average power reductions are possible without undue hardware complexity or performance compromise.

Abstract