SWEEP: Adaptive Task Scheduling for Exploring Energy Performance Trade-offs

Energy efficiency is becoming a major concern when running parallel computing systems owing to its impact on system reliability and operating cost. Recent works, that focus on energy efficient execution of task-based parallel applications on multi-core systems, leverage a subset of architectural features (core asymmetry, CPU DVFS and memory DVFS) and application attributes (inter-task parallelism, intra-task parallelism and task characteristics) to achieve this goal. More importantly, they have a fixed target metric and do not provide the flexibility to explore energy performance trade-offs (EPTO). We propose SWEEP, a task scheduler that leverages all the aforementioned architectural knobs and application attributes to facilitate EPTO exploration. SWEEP, at a high level, uses a combination of models and heuristics and works by splitting application execution into high parallelism and low parallelism phases. It then uses an adaptive task distribution algorithm, specific to the phase type, that leverages model-based predictions to determine the best task schedule and the DVFS settings for the phase. Moreover, SWEEP is able to flexibly target various EPTO metrics, a feature that is not supported by other proposals. Our evaluation shows that SWEEP achieves 19.9%, 36.4% and 9.5% reduction on average in terms of EDP, ED
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P and E
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DP compared to the best performing state-of-the-art.