SHF:Medium: Energy Efficient and Stochastically Robust Resource Allocation for Heterogeneous Computing

并行和分布式计算系统通常是机器的异质混合。随着这些系统的能力不断迅速扩大，它们的计算能量消耗也急剧增加，需要复杂的冷却设施，而这些设施本身就消耗大量的能量。因此，对能源效率高的资源管理的需要是至关重要的。此外，由于不可预测的变化，例如负载不平衡或突然机器故障引起的热热点，这些系统经常经历性能下降和高功耗。随着系统复杂性的增长，使系统运行对这些不确定性具有鲁棒性的重要性也在增加。该奖项的目标是研究基于随机的模型、指标和算法策略，以获得节能和鲁棒的资源分配。研究重点是从异构计算机器的实际数据中导出随机鲁棒性和能量模型；将随机模型应用于协同优化性能、鲁棒性、计算能量和冷却能量的资源管理策略；开发新的实时热建模方案；并通过从现实世界的千万亿级系统（国家大气研究中心的黄石和橡树岭国家实验室的美洲豹）和太万亿级系统（科罗拉多州立大学的ISTeC集群和橡树岭国家实验室的集群）收集的反馈来推动和验证研究。该研究预计将实现对运行时各种不确定性源具有弹性的资源管理策略，同时还考虑到温度变化和冷却能力的动态变化，以满足高性能计算环境中系统能效前所未有的性能保证。通过降低与计算相关的能源成本和不确定性的影响，这项研究最终将使高性能计算能够为更广泛的研究人员和科学问题提供便利。从长远来看，从这项研究中产生的理论基础和工具将在实现在现实电力预算的极端规模下实现可持续计算的宏伟承诺方面发挥至关重要的作用。研究的更广泛影响包括：将研究成果纳入各级教学，包括研究生、本科、中学甚至小学教育；增加代表性不足群体的参与；促进与工业和政府实验室的密切联系，将已开发的知识迅速转化为现实世界的部署。

Parallel and distributed computing systems are often a heterogeneous mix of machines. As these systems continue to expand rapidly in capability, their computational energy expenditure has skyrocketed, requiring elaborate cooling facilities, which themselves consume significant energy. The need for energy-efficient resource management is thus paramount. Moreover, these systems frequently experience degraded performance and high power consumption due to circumstances that change unpredictably, such as thermal hotspots caused by load imbalances or sudden machine failures. As the complexity of systems grows, so does the importance of making system operation robust against these uncertainties. The goal of this award is to study stochastic-based models, metrics, and algorithmic strategies for deriving resource allocations that are energy-efficient and robust. The research focus is on deriving stochastic robustness and energy models from real-world data from heterogeneous computing machines; applying stochastic models for resource management strategies that co-optimize performance, robustness, computation energy, and cooling energy; developing novel schemes for real-time thermal modeling; and driving and validating the research with feedback collected from real-world petascale systems (Yellowstone at National Center of Atmospheric Research and Jaguar at Oak Ridge National Lab) and terascale systems (Colorado State University's ISTeC cluster and clusters at Oak Ridge National Lab).The research is expected to realize resource management strategies that are resilient to various sources of uncertainty at run-time while also considering the dynamics of temperature variations and cooling capacity to meet performance guarantees with unprecedented gains in system energy-efficiency in high performance computing environments. By lowering the energy costs and impact of uncertainties associated with computing, this research will ultimately render high performance computing accessible to a wider population of researchers and scientific problems. In the long term, the theoretical foundations and tools that emerge from this research will play a vital role in achieving the grand promise of sustainable computing at extreme scales within realistic power budgets. The broader impacts of the research include: incorporate research results into all levels of teaching, including graduate, undergraduate, secondary, and even elementary education; increase participation by underrepresented groups; and foster close ties with industry and government labs to transfer the developed knowledge quickly into real-world deployments.