CSR-AES: Thermal Conductors: Runtime software support for proactive heat management in advanced execution systems

为了满足分布式仿真的计算需求，需要peta级系统。尽管在功率和热感知计算方面进行了广泛的研究，但这些系统中组件产生的热量仍在继续增长。成千上万个这样的组件紧密耦合会产生足够的热量，从而增加故障率并触发系统减速。散热需要昂贵的专用冷却系统。需要技术来减少大型系统产生的热量。我们正在构建一个基础设施，以便在先进的执行系统中实现自动化热管理。具体来说，我们有两个研究目标：1)创建一个分布式运行时热分析框架；2)创建主动控制技术，以减少单个高端组件上的分布式应用程序和系统热量。我们正在将我们的研究整合到新课程中，通过VT MAOP计划瞄准和招募少数民族学生进行研究活动，并开展外展活动，鼓励学生研究高端计算。我们正在创造能够提高分布式模拟效率和可靠性的技术。我们所有的软件工具和技术都将是开源的，并在我们的网站存储库中向公众提供。这影响了执行基于模拟的实验的广泛学科，包括计算物理，生物学和化学。此外，减少大规模应用的散热将降低计算中心的运营成本，提高系统可靠性，并通过节能间接影响环境。

Peta-scale systems are needed to meet the computational demands of distributed simulations. The heat produced by the components in these systems continues to grow despite extensive research in power- and thermal-aware computing. Tight coupling of tens of thousands of these components produces enough heat to increase failure rates and trigger system slowdowns. Dissipating the heat requires costly, dedicated cooling systems. Techniques are needed to reduce the heat produced by large-scale systems.We are building an infrastructure to enable automated thermal management in advanced execution systems. Specifically, we have two research goals: 1) create a framework for distributed runtime thermal profiling; and 2) create proactive control techniques to reduce distributed application and system thermals on individual high-end components. We are integrating our research in new courses, targeting and recruiting minority students for research activities through the VT MAOP program, and conducting outreach activities to encourage student research in high-end computing.We are creating technologies that will improve the efficiency and reliability of distributed simulations generally. All of our software tools and techniques will be open source and made available to the public in our website repository. This impacts a broad range of disciplines that perform simulation-based experimentation including computational physics, biology, and chemistry. Additionally, reducing the heat dissipation of large-scale applications will reduce operational costs for computational centers, increase system reliability, and impact the environment indirectly through energy conservation.