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

该建议旨在通过开发自适应软件工具来应对这两个主要挑战，以托管质量质量 - 性能权力的权衡。首先，我们将开发组合和统计自适应技术，以动态选择方法，在产生满足应用程序质量要求的解决方案的同时提供改进的性能。接下来，使用注释的计算和通信成本模型以及稀疏的数据访问模式，我们将开发降低功率的技术，而无需表现障碍。例如，即使利用了处理器之间相对较小的负载型，节能也可能是重要的。这些失衡很容易按照数万亿个循环的顺序进行，并且可以通过动态电压缩放（DVS，theclock频率和电源电压调谐）来调整功耗，以轻度/重载的处理器。更重要的是，产生的见解可能会导致未来的系统，在这些系统中，电力预算是有效地针对处理器内存互连子系统以提高应用程序性能的。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