Power Awareness through Selective Dynamically Optimized Traces

Abstract

We present the PARROT concept that seeks to achievehigher performance with reduced energy consumptionthrough gradual optimization of frequently executed codetraces. The PARROT microarchitectural framework integratestrace caching, dynamic optimizations and pipelinedecoupling. We employ a selective approach for applyingcomplex mechanisms only upon the most frequently usedtraces to maximize the performance gain at any givenpower constraint, thus attaining finer control of tradeoffsbetween performance and power awareness.We show that the PARROT based microarchitecture canimprove the performance of aggressively designed processorsby providing the means to improve the utilizationof their more elaborate resources. At the same time, rigorousselection of traces prior to storage and optimizationprovides the key to attenuating increases in thepower budget.For resource-constrained designs, PARROT based architecturesdeliver better performance (up to an average16% increase in IPC) at a comparable energy level,whereas the conventional path to a similar performanceimprovement consumes an average 70% more energy.Meanwhile, for those designs which can tolerate a higherpower budget, PARROT gracefully scales up to use additionalexecution resources in a uniformly efficient manner.In particular, a PARROT-style doubly-wide machinedelivers an average 45% IPC improvement while actuallyimproving the cubic-MIPS-per-WATT power awarenessmetric by over 50%.