CAREER: A Unified Framework for Multilevel Parallelization on Deep Computing Systems

职业：深度计算系统多级并行化的统一框架

• 批准号: 0346867
• 负责人: Dimitrios Nikolopoulos
• 金额: $ 41.98万
• 依托单位: College of William and Mary
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-02-15 至 2007-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0346867&HistoricalAwards=false
• 关键词: CAREER; Unified; Framework; Multilevel; Parallelization

Nikolopoulos, DimitriosCollege of William and MaryCCF-0346867This research activity involves the design, development and deploymentof a programming framework for explicit multilevel parallelization using a global address space model. The framework targets the upcoming generation of Petaflop-class supercomputers, which are based on architectural substrates with multiple levels of on-chip and off-chip parallelism and deep memory hierarchies. The research addresses the need for increased productivity and utilization of the National supercomputing power, by attempting to close the gap between computer architecture innovation and parallel programming practices. The main goal of this activity is to reduce the programming effort required for mapping algorithmic parallelism to hierarchical hardware components with heterogeneous means for parallel execution and assist programmers in deriving balanced designs of layered parallel applications. The programming framework investigated in this research unifies parallel programming models and methodologies and enables faster adaptation of parallel code to new hardware platforms. Concurrently, it forms a basis for education and training of interdisciplinary student audiences in high performance programming. The parallel programming component of this research is designed around standard C++ templates with notation for nested threads and iterators. The notations for parallelism are coupled with a templated representation of data, which allows for arbitrary partitioning, sharing, and coherence control at multiple levels of parallel execution constructs. While the programmer highlights nested parallelism, the orchestration and management of multigrain threads and data are delegated to the compiler and the runtime system. The research investigates novel methods for controlling the granularity of multilevel parallelism via vertical analysis of the program. Periodicity analysis and selective runtime tracing are used as means to derive effective data distribution and layout schemes without userintervention. Alongside runtime analysis, new resource-driven scheduling strategies and novel microprocessor features, including on-chip multithreading, on-chip SIMD parallelism and speculative execution, are incorporated into the parallelization and program optimization processes.

Nikolopoulos，Dimitrios 威廉和玛丽学院CCF-0346867 这项研究活动涉及使用全局地址空间模型来设计、开发和部署用于显式多级并行化的编程框架。该框架面向下一代千万亿次浮点级超级计算机，该计算机基于具有多级片上和片外并行性以及深度存储器层次结构的架构基板。该研究通过尝试缩小计算机体系结构创新和并行编程实践之间的差距，解决了提高生产力和国家超级计算能力利用率的需求。这项活动的主要目标是减少将算法并行性映射到具有异构并行执行方式的分层硬件组件所需的编程工作，并帮助程序员导出分层并行应用程序的平衡设计。 本研究中研究的编程框架统一了并行编程模型和方法，并使并行代码能够更快地适应新的硬件平台。 同时，它为跨学科学生观众的高性能编程教育和培训奠定了基础。本研究的并行编程组件是围绕标准 C++ 模板设计的，并带有嵌套线程和迭代器的表示法。并行性的符号与数据的模板化表示相结合，允许在并行执行结构的多个级别上进行任意分区、共享和一致性控制。 虽然程序员强调嵌套并行性，但多粒度线程和数据的编排和管理被委托给编译器和运行时系统。该研究研究了通过程序的垂直分析来控制多级并行粒度的新方法。 周期性分析和选择性运行时跟踪被用作无需用户干预即可得出有效数据分布和布局方案的手段。除了运行时分析之外，新的资源驱动调度策略和新颖的微处理器功能（包括片上多线程、片上 SIMD 并行性和推测执行）也被纳入并行化和程序优化过程中。