PowerStar: Improving Power Efficiency in Heterogenous Processors for Bursty Workloads with Approximate Computing
PowerStar: Improving Power Efficiency in Heterogenous Processors for Bursty Workloads with Approximate Computing
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DOI：
10.1109/cloudcom.2019.00035
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发表时间：
2019-12
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影响因子：
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通讯作者：
Sai Santosh Dayapule;Fan Yao;Guru Venkataramani
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作者：
Sai Santosh Dayapule;Fan Yao;Guru Venkataramani
现代数据中心在其服务器节点中越来越多地采用异构处理器，以最大限度地提高能效。然而，如何正确配置这些处理器，以便在波动的工作负载下最大化吞吐量，同时优化系统功耗，仍然存在挑战。在本文中，我们提出了PowerStar，一个框架，最大限度地提高电源效率，并减少在异构处理器中所需的重新配置的数量在工作到达模式的波动期间，同时处理延迟关键的工作负载。PowerStar是基于以下两个关键观察而构建的：（i）异构处理器的重新配置以添加更多核心并实现更高性能和/或计算核心的重新分配可能是昂贵的，这是由于所涉及的额外延迟和相关联的能量开销;（ii）保持该系统的大部分功率可节省大量能源─有效的配置，能够吸收短突发的工作到达，而不需要重新配置系统。PowerStar通过仔细选择最节能的配置（状态）并在可行时通过控制使用近似计算明智地最大化状态驻留来运行。我们在一个6核的ARM big.LITTLE异构平台上实现了PowerStar作为原型，并在各种工作负载下对其进行了评估。我们的研究结果表明，与性能驱动的电源管理策略的基线相比，我们的电源效率感知的PowerStar可以在严格的QoS下降低平均功耗高达11（第95百分位延迟低于3×作业执行延迟），并且在宽松的QoS下可以节省甚至高达32%的平均功率（10×作业执行延迟下的第95百分位延迟）约束。
Modern Data Centers have increasingly adopted heterogeneous processors in their server nodes to maximize power efficiency. However, there are still challenges in how to properly configure these processors such that throughput can be maximized under fluctuating workload while optimizing system power consumption. In this paper, we propose PowerStar, a framework that maximizes power efficiency and reduces the number of reconfigurations needed in heterogeneous processors during periods of fluctuations in job arrival patterns while handling latency-critical workloads. PowerStar is built based on the following two key observations: (i) reconfiguration of heterogeneous processors to add more cores and enable higher performance and/or re-allocation of computing cores can be costly due to the extra latency involved and the associated energy overheads; (ii) a considerable amount of energy savings can be achieved by keeping the system in most power-efficient configurations capable of absorbing short bursts in job arrivals without needing to reconfigure the system. PowerStar operates by carefully choosing the most power-efficient configurations (states) and judiciously maximizing the state residency through the controlled use of approximate computing, when feasible. We implement PowerStar as a prototype on a 6-core ARM big.LITTLE heterogeneous platform and evaluate it with a variety of workloads. Our results show that, compared to a baseline of performance-driven power management policy, our power efficiency-aware PowerStar can reduce the average power by up to 11% under tight QoS (95th percentile latency under 3× job execution latency), and can save even higher average power of up to 32% under relaxed QoS (95th percentile latency under 10× job execution latency) constraints when compared to the baseline.