XPS: FP: Collaborative Research: Parallel Irregular Programs: From High-Level Specifications to Run-time Optimizations

XPS：FP：协作研究：并行不规则程序：从高级规范到运行时优化

• 批准号: 1337281
• 负责人: Keshav Pingali
• 金额: $ 37.51万
• 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: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1337281&HistoricalAwards=false
• 关键词: XPS; FP; Collaborative; Research; Parallel

The high performance super-computers of today and the ordinary computers of tomorrow have an ever-increasing number of cores. Utilizing these computers efficiently will allow research advancements in every field of science, from understanding the brain to understanding the fundamental particles to understanding the cosmos. These new computers are increasingly complex and difficult to program. At the same time, standard algorithms used in science and engineering are evolving and are increasingly hard to map to these machines. Advances in programming models, tools, and implementations which make implementing complex algorithms simpler, while achieving high performance, are essential to making high performance computing a standard tool of all scientists.Regular algorithms, usually expressed with matrices, have driven high performance computing. Increasingly there is considerable interest in using large-scale computers for irregular algorithms. Irregular algorithms arise in manipulating graphs, sparse-matrices, trees, adaptive meshes, etc and are increasingly a standard tool used by computational scientists. Expressing such algorithms at a high-level has allowed high-performance run-times to achieve performance comparable to the best hand-coded implementations of these algorithms on shared-memory machines. A high level description frees the programmer from the complexities of parallel programming. The PIs are building run-times and compilers to allow the execution of complex, irregular algorithms on distributed-memory, large-scale computers. A high-level representation allows the system to exploit considerable knowledge about the semantics of the algorithm to optimize communication, mask latency, and achieve high-performance.

当今的高性能超级计算机和明天的普通计算机的核心数量不断增加。 从了解大脑到了解基本颗粒到理解宇宙的每个科学领域，有效地利用这些计算机将允许研究进步。 这些新计算机越来越复杂且难以编程。 同时，科学和工程中使用的标准算法正在发展，并且越来越难以将这些机器映射到这些机器上。 编程模型，工具和实现的进步，使实施复杂算法变得更简单，同时实现高性能，对于使高性能计算成为所有科学家的标准工具至关重要。通常用矩阵表达的规范算法驱动了高性能计算。 越来越多的兴趣将大型计算机用于不规则算法。不规则的算法在操纵图，稀疏缩写，树木，自适应网格等方面出现，并且越来越多地是计算科学家使用的标准工具。 在高级表达这种算法已经允许高性能运行时间实现与共享内存机器上这些算法的最佳手动编码实现相当的性能。 高级描述使程序员摆脱了并行编程的复杂性。 PI正在构建运行时间和编译器，以允许在分布式内存，大型计算机上执行复杂的，不规则的算法。 高级表示使系统可以利用有关算法语义的大量知识，以优化通信，掩盖延迟和实现高性能。