This article introduces a significance-centric programming model and runtime support that sets the supply voltage in a multicore CPU to sub-nominal values to reduce the energy footprint and provide mechanisms to control output quality. The developers specify the significance of application tasks respecting their contribution to the output quality and provide check and repair functions for handling faults. On a multicore system, we evaluate five benchmarks using an energy model that quantifies the energy reduction. When executing the least-significant tasks unreliably, our approach leads to 20% CPU energy reduction with respect to a reliable execution and has minimal quality degradation.

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We introduce a task-based programming model and runtime system that exploit the observation that not all parts of a program are equally significant for the accuracy of the end-result, in order to trade off the quality of program outputs for increased energyefficiency. This is done in a structured and flexible way, allowing for easy exploitation of different points in the quality/energy space, without adversely affecting application performance. The runtime system can apply a number of different policies to decide whether it will execute less-significant tasks accurately or approximately. The experimental evaluation indicates that our system can achieve an energy reduction of up to 83% compared with a fully accurate execution and up to 35% compared with an approximate version employing loop perforation. At the same time, our approach always results in graceful quality degradation.