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

SHF：Medium：异构计算的节能和随机鲁棒资源分配

• 批准号: 1302693
• 负责人: Sudeep Pasricha
• 金额: $ 85万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-05-15 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1302693&HistoricalAwards=false
• 关键词: SHF; Medium; Energy; Efficient; Stochastically

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.

并行和分布式计算系统通常是机器的异构组合。随着这些系统的能力不断快速扩展，它们的计算能源消耗猛增，需要复杂的冷却设施，而这些设施本身会消耗大量能源。因此，对节能资源管理的需求至关重要。此外，由于环境不可预测的变化，例如负载不平衡或突然的机器故障引起的热点，这些系统经常会出现性能下降和功耗高的情况。随着系统复杂性的增加，使系统运行稳健以应对这些不确定性的重要性也随之增加。该奖项的目标是研究基于随机的模型、指标和算法策略，以得出节能且稳健的资源分配。研究重点是从异构计算机的真实数据中导出随机鲁棒性和能量模型；将随机模型应用于资源管理策略，共同优化性能、鲁棒性、计算能量和冷却能量；开发实时热建模的新颖方案；并利用从现实世界千万亿级系统（国家大气研究中心的 Yellowstone 和橡树岭国家实验室的 Jaguar）和万亿级系统（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。研究预计将实现能够适应运行时各种不确定性来源的资源管理策略，同时还考虑温度变化和冷却能力的动态，以满足性能保证，并在系统中获得前所未有的收益高性能计算环境中的能源效率。通过降低能源成本和与计算相关的不确定性的影响，这项研究最终将使更广泛的研究人员和科学问题能够使用高性能计算。从长远来看，这项研究产生的理论基础和工具将在实现现实功率预算内极端规模可持续计算的宏伟承诺方面发挥至关重要的作用。该研究更广泛的影响包括： 将研究成果纳入各级教学，包括研究生、本科生、中学，甚至初等教育；增加代表性不足群体的参与；并与行业和政府实验室建立密切联系，将开发的知识快速转移到现实世界的部署中。