Adaptive Software for Extreme-Scale Scientific Computing: Co-Managing Quality-Performance-Power Tradeoffs

用于超大规模科学计算的自适应软件：共同管理质量-性能-功耗权衡

• 批准号: 0444345
• 负责人: Padma Raghavan
• 金额: --
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-15 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0444345&HistoricalAwards=false
• 关键词: Adaptive; Software; Extreme; Scale; Scientific

This proposal seeks to address these two primary challenges by developing adaptive software tools toco-manage quality-performance-power tradeoffs. First, we will develop combinatorial and statistical adaptive techniques to select methods dynamically, delivering the improved performance while producing a solution that meets application quality requirements. Next, using an annotated model of computation and communication costs and sparse data access patterns, we will develop techniques for power reduction withoutperformance impairment. For example, power savings can be significant even when relatively minor loadimbalances among processors are exploited. These imbalances can easily be on the order of trillions ofCPU cycles, and power consumption can be tuned through dynamic voltage scaling (DVS, where both theclock frequency and the supply voltage are tuned) for lightly/heavily loaded processors. More importantly,resulting insights can lead to future systems where the power budget is directed effectively over processor-memory interconnect subsystems to improve application performance. We plan to implement our techniques by developing an adaptive component software system on high-end multiprocessors

该提案旨在通过开发自适应软件工具来共同管理质量-性能-功率权衡来解决这两个主要挑战。首先，我们将开发组合和统计自适应技术，以动态选择方法，提供更好的性能，同时产生满足应用程序质量要求的解决方案。接下来，使用一个注释模型的计算和通信成本和稀疏的数据访问模式，我们将开发技术，降低功耗而不损害性能。例如，即使在利用处理器之间相对较小的负载平衡时，功率节省也可以是显著的。这些不平衡很容易达到数万亿CPU周期的量级，并且可以通过动态电压缩放（DVS，其中时钟频率和电源电压都被调整）来调整轻/重负载处理器的功耗。更重要的是，由此产生的见解可以导致未来的系统，其中功率预算是有效地针对处理器-存储器互连子系统，以提高应用程序的性能。我们计划通过在高端多处理器上开发自适应组件软件系统来实现我们的技术