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: 1337217
• 负责人: Marc Snir
• 金额: $ 37.49万
• 依托单位: University of Illinois at Urbana-Champaign
• 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=1337217&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.

今天的高性能超级计算机和明天的普通计算机都有越来越多的核心。 有效地利用这些计算机将使每个科学领域的研究取得进展，从理解大脑到理解基本粒子再到理解宇宙。 这些新的计算机越来越复杂，越来越难编程。 与此同时，科学和工程中使用的标准算法正在不断发展，越来越难以映射到这些机器上。 编程模型、工具和实现的进步使复杂算法的实现变得更简单，同时实现高性能，这对于使高性能计算成为所有科学家的标准工具是必不可少的。 越来越多地有相当大的兴趣在使用大型计算机的不规则算法。不规则算法出现在操作图，稀疏矩阵，树，自适应网格等，并越来越多地被计算科学家使用的标准工具。 在高层次上表达这样的算法允许高性能运行时实现与共享内存机器上这些算法的最佳手工编码实现相媲美的性能。 高级描述使程序员从并行编程的复杂性中解放出来。 PI正在构建运行时和编译器，以允许在分布式内存的大规模计算机上执行复杂的，不规则的算法。 高级表示允许系统利用关于算法语义的大量知识来优化通信、屏蔽延迟并实现高性能。