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各种机器的混合。随着这些系统在能力上继续快速扩展，它们的计算能源消耗飙升，需要复杂的冷却设施，而这些设施本身就消耗了大量能源。因此，对高能效资源管理的需要是最重要的。此外，由于不可预测的环境变化，例如负载不平衡或机器突然故障导致的热热点，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加，针对这些不确定性使系统运行健壮的重要性也越来越大。该奖项的目标是研究基于随机的模型、指标和算法策略，以获得节能和健壮的资源分配。研究的重点是从来自不同计算机的真实世界数据导出随机稳健性和能量模型；将随机模型应用于资源管理策略，以共同优化性能、稳健性、计算能量和冷却能量；开发用于实时热建模的新方案；并使用从真实世界的千万亿级系统(国家大气研究中心的黄石公园和橡树岭国家实验室的美洲豹)和千兆级系统(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。研究有望实现在运行时对各种不确定源具有弹性的资源管理策略，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多的研究人员和科学问题能够获得高性能计算。从长远来看，这项研究的理论基础和工具将在实现在现实电力预算内实现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。这项研究的更广泛影响包括：将研究成果纳入所有级别的教学，包括研究生、本科、中学，甚至基础教育；增加代表不足的群体的参与；以及促进与行业和政府实验室的密切联系，以将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.