Language and System Support for Petascale Irregular Applications

对 Petascale 不规则应用程序的语言和系统支持

• 批准号: 0833162
• 负责人: Keshav Pingali
• 金额: $ 80万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0833162&HistoricalAwards=false
• 关键词: Language; System; Support; Petascale; Irregular

Until recently, most high-performance computing applications involved matrix computations. However, new application areas such as computational biology and machine learning involve constructing, computing with, and modifying large sparse graphs. The Galois team at the University of Texas, Austin has been building a system that exploits optimistic parallelization and compiler analysis to raise the level of abstraction at which these applications can be coded without a substantial performance penalty. The programming model is sequential Java augmented with two constructs called optimistic set iterators. This project brings together Galois project team members, computational biologists and machine learning experts to extend and evaluate the Galois system on high-end shared-memory machines and large-scale distributed memory for applications that involve computations on large sparse graphs. The main thrusts of the project are (i) design and implementation of modest language extensions for supporting nested data-parallelism, (ii) porting the existing system to distributed-memory machines, (iii) implementation of adaptive feedback-driven parallel execution, and (iv) development of compiler analyses to optimize execution.

直到最近，大多数高性能计算应用都涉及矩阵计算。然而，新的应用领域，如计算生物学和机器学习涉及构建、计算和修改大型稀疏图。德克萨斯大学奥斯汀分校的Galois团队一直在构建一个系统，该系统利用乐观并行化和编译器分析来提高抽象级别，在抽象级别上，这些应用程序可以在没有实质性性能损失的情况下进行编码。编程模型是顺序Java，增强了两个称为乐观集迭代器的结构。该项目汇集了Galois项目团队成员、计算生物学家和机器学习专家，在高端共享内存机器和大规模分布式内存上扩展和评估Galois系统，用于涉及大型稀疏图计算的应用程序。该项目的主要重点是(i)设计和实现适度的语言扩展，以支持嵌套数据并行；（ii）将现有系统移植到分布式内存机器上；（iii）实现自适应反馈驱动的并行执行；（iv）开发编译器分析以优化执行。