SHF: Small: The Loop Chain Abstraction for Balancing Locality and Parallelism

SHF：小：平衡局部性和并行性的循环链抽象

• 批准号: 1700723
• 负责人: Catherine Olschanowsky
• 金额: $ 26万
• 依托单位: Boise State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-16 至 2018-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1700723&HistoricalAwards=false
• 关键词: SHF; Small; Loop; Chain; Abstraction

Title: SHF: Small: The Loop Chain Abstraction for Balancing Locality and ParallelismComputational science, which involves modeling and simulation of phenomena such as combustion in engines, has become the third pillar of science and engineering research. Computer simulations test design parameters much more cheaply than physical experiments. Also, computer simulations participate in a fortuitous cycle with theory by enabling inexpensive experimentation of theoretical models. Mapping computer simulations to high performance computer architectures is a challenging computer science problem; constraints include achieving high performance and effective use of computing resources while not overburdening scientific programmers. This challenge is becoming more severe as architectures continue to evolve in ways that make them ever more difficult to use. In this project, the PIs will remove programmer burden by developing a programming abstraction called loop chaining, which enables architecture-specific program optimizations by compilers. This work enables scientists to spend less time dealing with annoying performance programming details and more time evolving their scientific models that help push science and engineering forward.Exposing opportunities for parallelization while explicitly managing data locality is the primary challenge to porting and optimizing existing computational science simulation codes. The most popular programming models used in these codes such as MPI (Message Passing Interface) require that programmers explicitly determine the data and computation distribution. This has led to good scaling between compute nodes, but parallelism and locality are needed within a node as well. There are many approaches for implementing shared memory parallelism, but with most of them it is the programmer's responsibility to group computations to improve data locality. This project focuses on the development of the loop chain abstraction to provide compilers with sufficient information to automate the parallelism versus data locality tradeoff. Preliminary results show that using the loop chain abstraction can significantly improve parallel scalability. The intellectual merits are that the loop chain abstraction will enable existing codes to maintain their software modularity while exposing information critical to performance optimizations that improve parallel scalability. Some important contributions of this research are the re-casting of existing program optimizations to use the loop chain abstraction as input and the eventual incorporation of the loop chain abstraction into parallel programming languages. The broader impacts include reducing the burden on scientists developing computational simulations, sharing the developed compiler prototypes as open-source software, and providing tutorials for doing source-to-source loop chain-based tiling transformations in C++ and Fortran code. The testbed for loop chaining will include atmospheric science, materials, and combustion codes, therefore tunable versions of these applications will be released. Additionally, a new course module will be developed, through which students will be trained in computational science and specifically, on how to expose loop chains within simulation software.

职务名称：SHF：小：平衡局部性和非局部性的环链抽象计算科学涉及对发动机燃烧等现象的建模和模拟，已成为科学和工程研究的第三大支柱。计算机模拟测试设计参数比物理实验便宜得多。 此外，计算机模拟参与了一个偶然的循环与理论，使廉价的实验理论模型。 将计算机模拟映射到高性能计算机架构是一个具有挑战性的计算机科学问题;约束包括实现高性能和有效使用计算资源，同时不给科学程序员带来过重负担。 随着体系结构的不断发展，使它们变得越来越难以使用，这一挑战变得越来越严峻。 在这个项目中，PI将通过开发一种称为循环链的编程抽象来减轻程序员的负担，这种抽象允许编译器对特定于体系结构的程序进行优化。 这项工作使科学家能够花更少的时间来处理恼人的性能编程细节，并将更多的时间用于发展他们的科学模型，以帮助推动科学和工程向前发展。在显式管理数据局部性的同时暴露并行化的机会是移植和优化现有计算科学仿真代码的主要挑战。在这些代码中使用的最流行的编程模型，如MPI（消息传递接口），要求程序员显式地确定数据和计算分布。这导致了计算节点之间的良好伸缩性，但节点内也需要并行性和局部性。有许多实现共享内存并行的方法，但大多数方法都是程序员负责对计算进行分组以提高数据局部性。这个项目的重点是开发循环链抽象，为编译器提供足够的信息来自动化并行性与数据局部性的权衡。 初步结果表明，使用循环链抽象可以显着提高并行可扩展性。 智能的优点是，循环链抽象将使现有的代码，以保持其软件的模块化，同时暴露的信息，提高并行可扩展性的性能优化的关键。 这项研究的一些重要贡献是重新铸造现有的程序优化使用循环链抽象作为输入，并最终纳入到并行编程语言的循环链抽象。 更广泛的影响包括减轻科学家开发计算模拟的负担，将开发的编译器原型作为开源软件共享，并提供在C++和Fortran代码中进行基于源到源循环链的平铺转换的教程。 循环链的测试平台将包括大气科学，材料和燃烧代码，因此这些应用程序的可调版本将被发布。 此外，还将开发一个新的课程模块，通过该模块，学生将接受计算科学培训，特别是如何在仿真软件中暴露循环链。