SPX: Collaborative Research: Parallel Algorithm by Blocks - A Data-centric Compiler/runtime System for Productive Programming of Scalable Parallel Systems

SPX：协作研究：块并行算法 - 用于可扩展并行系统的高效编程的以数据为中心的编译器/运行时系统

• 批准号: 1919211
• 负责人: Ponnuswamy Sadayappan
• 金额: $ 44万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2019-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1919211&HistoricalAwards=false
• 关键词: SPX; Collaborative; Research; Parallel; Algorithm

Achieving both high productivity and high performance on scalable parallel and heterogeneous computer systems is a challenging goal for application developers. Parallel programming with Message Passing Interface (MPI) is currently the most widely used and effective means of developing scalable parallel applications; however the productivity of application developers is lower than with programming models that offer a global shared view of data structures. In comparison, achieving high performance and scalability with global-address-space programming models is challenging. This project focuses on the development of a data-centric compiler/runtime framework, "Parallel Algorithms by Blocks" (PAbB), aimed at offering users the combined positive attributes of multiple parallel programming models without the disadvantages. The main novelty of this project is that it uses a combination of user insights, new compiler optimizations, and advanced runtime support to achieve both productivity and performance for an important class of computations that operate on matrices, tensors, and graphs. The main broader impact of the work is that it can significantly lower the barrier to entry for scientists from various domains who wish to develop new high-performance applications on large scale parallel systems, but presently find it too difficult with currently available parallel programming models. This project brings together a team of investigators, with expertise across the software stack, to develop compiler tools and runtime systems for PAbB and demonstrate its use across a number of applications from computational science and data science. The PAbB model is intended to work in concert with MPI; that is, PAbB programs can execute in any standard MPI environment, interoperating with other native MPI code. The key idea behind the proposed approach is to offer the user a global-address view of the targeted data structures, requiring only (optionally in some cases) that they specify how data should be partitioned, but have the compiler/runtime handle the tedious aspects of the global-to-local re-indexing and inter-node data movement. In addition to the productivity benefit, a second significant benefit is in enabling system support for dynamic load balancing. The approach is being designed and demonstrated in the context of applications operating on dense and sparse matrices and tensors, and graphs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在可扩展的平行和异质计算机系统上，实现高生产率和高性能是应用程序开发人员的挑战性目标。并行编程与消息传递接口（MPI）是当前是开发可扩展并行应用程序的最广泛使用和有效手段。但是，应用程序开发人员的生产率低于提供全球数据结构共享视图的编程模型。相比之下，通过全球地址空间编程模型实现高性能和可伸缩性是一项挑战。该项目的重点是开发以数据为中心的编译器/运行时框架“ By Blocks By Blocks”（PABB）的开发，旨在为用户提供多个没有缺点的多个并行编程模型的正面属性。该项目的主要新颖性是，它结合了用户洞察力，新编译器优化和高级运行时支持，以实现在矩阵，张量和图形上运行的重要一类计算类别的生产力和性能。这项工作的主要影响是，它可以显着降低来自希望在大型平行系统上开发新的高性能应用程序的科学家的进入的障碍，但目前发现当前可用的并行编程模型这太困难了。该项目汇集了一组研究人员，在软件堆栈中具有专业知识，以开发用于PABB的编译器工具和运行时系统，并在计算科学和数据科学的许多应用程序中证明了其在许多应用程序中的使用。 PABB模型旨在与MPI协同合作；也就是说，PABB程序可以在任何标准的MPI环境中执行，并与其他本机MPI代码互操作。拟议方法背后的关键思想是向用户提供目标数据结构的全球地址视图，仅需要（在某些情况下选择）它们指定了应如何分区数据，但具有编译器/运行时处理全球到局部到局部重新索引和节点数据移动的乏味方面。除了生产力益处外，第二个重要的好处是为动态负载平衡提供系统支持。该方法是在以密集和稀疏的矩阵和张量进行的应用程序的背景下进行设计和证明的。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来支持。