CPA-CSA-T: Power-Performance Optimization Strategies for Highly Uncertain Multi-core Systems

该项目涉及在未来高度不确定、容易发生故障的大规模（100个处理器）芯片多处理器（cmp）中，最大限度地减少功率性能效率（PPE）的损失，在cmp中，单个处理器以各种方式配置以保持运行。在这种动态异构的场景下，实现的PPE将根据应用程序在哪些降级的内核上运行而有很大差异。对于某些将线程分配给内核的任务，降级可能非常严重，以至于导致系统无法使用。然而，大的问题规模妨碍了直接的搜索技术。这个问题可以通过设计时分析和运行时测量的明智组合以及基于分层规则的搜索算法来解决。该算法在两个级别上运行，最低级别被划分为核的子组。关于不同退化状态下内核特征的设计时信息有助于减少搜索空间，而运行时测量则提供关于解决方案“好”的反馈。结果将是未来大规模，高度不确定的cmp的可行解决方案，允许尽可能保持接近峰值的PPE。这项研究的更广泛影响包括综合研究和教育、扩大代表性不足群体的参与、加强研究基础设施、广泛传播结果以及潜在的社会影响。

This project involves minimizing the power-performance efficiency(PPE) loss in future highly uncertain, failure-prone, large-scale (100 processor) Chip Multi-Processors (CMPs) in which the individual processors are deconfigured in various ways in order to remain operational. Under this scenario of dynamic heterogeneity, the achieved PPE will vary significantly depending on what applications run on which degraded cores. For some assignments of threads to cores, the degradation may be so severe as to render the system unusable. However, the large problem size precludes straightforward search techniques.This problem is addressed through a judicious combination of design-time analysis and runtime measurements coupled with a hierarchical rules-based search algorithm. The algorithm operates at two levels, with the lowest level divided among subgroups of cores. Design time information regarding the characteristics of cores in different degraded states helps prune the search space, while runtime measurements provide feedback about the ``goodness'' of the solution. The result will be a workable solution for future large-scale, highly uncertain CMPs that permits maintaining as close to the peak PPE as possible. The broader impacts of this research involve integrated research and education, broadening the participation of under-represented groups, enhanced infrastructure for research, broad dissemination of results, and potential societal impact.