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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >Fine-Grained Powercap Allocation for Power-Constrained Systems Based on Multi-Objective Machine Learning
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Fine-Grained Powercap Allocation for Power-Constrained Systems Based on Multi-Objective Machine Learning

机译：基于多目标机器学习的功率约束系统进行细粒度PowerCAP分配

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Power capping is an important solution to keep the system within a fixed power constraint. However, for the over-provisioned and power-constrained systems, especially the future exascale supercomputers, powercap needs to be reasonably allocated according to the workloads of compute nodes to achieve trade-offs among performance, energy and powercap. Thus it is necessary to model performance and energy and to predict the optimal powercap allocation strategies. Existing power allocation approaches have insufficient granularity within nodes. Modeling approaches usually model performance and energy separately, ignoring the correlation between objectives, and do not expose the Pareto-optimal powercap configurations. Therefore, this article combines the powercap with uncore frequency scaling and proposes an approach to predict the Pareto-optimal powercap configurations on the power-constrained system for input MPI and OpenMP parallel applications. Our approach first uses the elaborately designed micro-benchmarks and a small number of existing benchmarks to build the training set, and then applies a multi-objective machine learning algorithm which combines the stacked single-target method with extreme gradient boosting to build multi-objective models of performance and energy. The models can be used to predict the optimal processor and memory powercap settings, helping compute nodes perform fine-grained powercap allocation. When the optimal powercap configuration is determined, the uncore frequency scaling is used to further optimize the energy consumption. Compared with the reference powercap configuration, the predicted optimal configurations predicted by our method can achieve an average powercap reduction of 31.35 percent, an average energy reduction of 12.32 percent, and average performance degradation of only 2.43 percent.
机译：电源覆盖是保持系统在固定功率约束中的重要解决方案。然而，对于过度配置和功率约束的系统，特别是未来的ExaScale超级计算机，PowerCAP需要根据计算节点的工作量合理地分配，以实现性能，能量和PowerCAP之间的权衡。因此，有必要模拟性能和能量，并预测最佳PowerCAP分配策略。现有的电力分配方法在节点内具有足够的粒度。建模方法通常分别模拟性能和能量，忽略目标之间的相关性，并且不公开帕累托最佳PowerCAP配置。因此，本文将PowerCAP与未频率缩放结合起来，提出了一种方法来预测用于输入MPI和OpenMP并行应用的功率受限系统上的Pareto-Optimal PowerCAP配置。我们的方法首先使用精心设计的微基准和少量现有的基准来构建培训集，然后应用一个多目标机器学习算法，将堆叠的单目标方法与极端渐变提升相结合以构建多目标性能和能量模型。该模型可用于预测最佳处理器和内存PowerCAP设置，帮助计算节点执行细粒度的PowerCAP分配。当确定最佳PowerCAP配置时，未频率缩放用于进一步优化能量消耗。与参考PowerCAP配置相比，我们方法预测的预测最佳配置可以实现平均PowerCAP减少31.35％，平均能量降低12.32％，平均性能降低仅为2.43％。

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