CAREER: Harnessing Hybrid Computing Resources in PetaScale Computing and Beyond

职业：利用千万亿级计算及其他领域的混合计算资源

• 批准号: 1149644
• 负责人: Melissa Smith
• 金额: $ 46.82万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-15 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1149644&HistoricalAwards=false
• 关键词: CAREER; Harnessing; Hybrid; Computing; Resources

Future ExaScale computing systems are expected to exceed a hundred-thousand nodes and contain a rich mix of heterogeneous computing resources created by coupling multi-core processors with accelerator processors. However, current applications are not yet able to exploit more than a few tens of thousands of these nodes, or effectively manage this heterogeneous mix of resources to balance workloads. Moreover, this heterogeneous mixture of resources in hybrid or multi-paradigm systems presents programmers with new, but challenging opportunities for exploiting the various types of parallelism within algorithms. This CAREER research will formulate an inclusive hierarchical framework for performance modeling and analysis of hybrid computing systems that include multi-core processors and accelerators. The framework includes a coarse-grained model for rapid assessment of the fitness match between the application and the hybrid system and a fine-grained model for predicting execution time and workload balance analysis. These models will be useful for researchers and scientists developing, optimizing, and maintaining scientific codes. The research project includes forming a taxonomy of application and architecture characteristics through a rigorous study of application optimization and execution on multi-core and accelerator processors. The study focuses on the inter-node parallelism of the applications across a mix of heterogeneous nodes and the intra-node parallelism on the multi-paradigm nodes. These hybrid system studies elucidate the application and architecture characteristics included in the coarse and fine-grained models of the hierarchical framework. The optimization methods (resource ratio optimization, load balancing and memory management) and the model framework are qualitatively assessed for other application domains not included in this study. The project also transitions this knowledge into educational modules suitable for classroom use in the undergraduate and graduate curriculum.

未来的ExaScale计算系统预计将超过10万个节点，并包含通过将多核处理器与加速器处理器耦合而创建的异构计算资源的丰富混合。然而，目前的应用程序还不能利用超过数万个这样的节点，或者有效地管理这种异构的资源混合来平衡工作负载。此外，这种混合或多范式系统中的异构混合资源为程序员提供了新的，但具有挑战性的机会，利用算法中的各种类型的并行性。这项职业生涯的研究将制定一个包容性的分层框架，包括多核处理器和加速器的混合计算系统的性能建模和分析。该框架包括一个粗粒度的模型，用于快速评估应用程序和混合系统之间的适应性匹配和细粒度的模型，用于预测执行时间和工作负载平衡分析。这些模型将有助于研究人员和科学家开发，优化和维护科学代码。该研究项目包括通过对多核和加速器处理器上的应用程序优化和执行的严格研究，形成应用程序和架构特征的分类。该研究的重点是跨混合异构节点的应用程序的节点间并行性和多范式节点上的节点内并行性。这些混合系统的研究阐明了应用程序和架构的特点，包括在粗粒度和细粒度模型的分层框架。优化方法（资源比例优化，负载平衡和内存管理）和模型框架进行了定性评估，其他应用领域不包括在这项研究中。该项目还将这些知识转化为适合本科生和研究生课程课堂使用的教育模块。