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Evaluating adaptive and predictive power management strategies for optimizing visualization performance on supercomputers
Parallel Computing ( IF 1.4 ) Pub Date : 2021-04-15 , DOI: 10.1016/j.parco.2021.102782
Stephanie Brink , Matthew Larsen , Hank Childs , Barry Rountree

Power is becoming an increasingly scarce resource on the next generation of supercomputers, and should be used wisely to improve overall performance. One strategy for improving power usage is hardware overprovisioning, i.e., systems with more nodes than can be run at full power simultaneously without exceeding the system-wide power limit. With this study, we compare two strategies for allocating power throughout an overprovisioned system – adaptation and prediction – in the context of visualization workloads. While adaptation has been suitable for workloads with more regular execution behaviors, it may not be as suitable on visualization workloads, since they can have variable execution behaviors. Our study considers a total of 104 experiments, which vary the rendering workload, power budget, allocation strategy, and node concurrency, including tests processing data sets up to 1 billion cells and using up to 18,432 cores across 512 nodes. Overall, we find that prediction is a superior strategy for this use case, improving performance up to 27% compared to an adaptive strategy.



中文翻译:

评估自适应和预测电源管理策略,以优化超级计算机上的可视化性能

电源已成为下一代超级计算机上越来越稀缺的资源,应明智地使用它来提高整体性能。改善功耗的一种策略是硬件过度配置, 节点数量超过可以同时以全功率运行的系统,而不会超出系统范围的功率限制。通过这项研究,我们在可视化工作负载的情况下,比较了在整个超额配置的系统中分配功率的两种策略-自适应和预测。尽管适应已适用于具有更多常规执行行为的工作负载,但它可能不适用于可视化工作负载,因为它们可能具有可变的执行行为。我们的研究考虑了总共104个实验,这些实验会改变渲染工作量,功耗预算,分配策略和节点并发性,包括测试处理多达10亿个单元的数据集以及在512个节点上使用多达18,432个核心的测试。总体而言,我们发现对于此用例而言,预测是一种出色的策略,

更新日期:2021-05-25
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