CDS&E: Compiler/Runtime Support for Developing Scalable Parallel Multi-Scale Multi-Physics

CDS

• 批准号: 1404995
• 负责人: Ponnuswamy Sadayappan
• 金额: $ 54.43万
• 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: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2019-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1404995&HistoricalAwards=false
• 关键词: CDS& Compiler; Runtime; Support; Developing

The dramatic strides in computer speed and performance over the last few decades make it feasible to accurately model increasingly complex phenomena. However, achieving high performance on massively parallel supercomputers is an extremely challenging task. With deepening memory hierarchies, significantly higher degrees of per-chip multi-core parallelism, the task of programming compute-intensive engineering applications to attain high performance on a large scale cluster system has become increasingly difficult. It is often the case that the time and effort required to develop effective and efficient software has become the bottleneck in advancing many areas of science and engineering. This challenge can be overcome by advances in compile-time/runtime systems that can ease the burden on the programmer while delivering a high performance portable instantiation of the particular application on modern and emerging high performance platforms.To address this challenge, this project is developing a novel framework for transforming irregular scientific/engineering applications in a global address space framework. The research is grounded in a very different and complementary research direction to most current efforts in addressing the challenge of enhancing programmer productivity, maintaining portability, and achieving good performance on scalable distributed-memory parallel systems. The project will advance compiler/runtime techniques so that users can develop annotated sequential programs, to be automatically transformed by our system for efficient execution on distributed-memory parallel systems. This approach is motivated by the success of the popular OpenMP and OpenACC pragma based approaches to transforming annotated sequential programs for parallel execution on multicore and GPU/accelerator systems, respectively. An annotation based OpenAPP (APP - Asynchronous Partitioned Parallelism) framework is proposed for source-to-source transformation of an important class of scientific/engineering programs using the inspector/executor paradigm for execution on distributed-memory parallel systems. The proposed framework will be validated using several medium to large scale applications.The project seeks to significantly lower the entry barrier associated with effective use of scalable distributed-memory computers, which are essential if more than 100x performance improvement over sequential codes is sought. A successful outcome of this project will be transformative for computational and domain scientists and engineers who seek to use next generation parallel systems for their simulation and modeling. The developed tools will be made publicly available to the community under an open source license. The project will also organize workshops that bring together compiler/runtime experts and computational scientists developing massively parallel scientific/engineering applications.

在过去的几十年里，计算机速度和性能的巨大进步使得对日益复杂的现象进行精确建模成为可能。然而，在大规模并行超级计算机上实现高性能是一项极具挑战性的任务。随着内存层次的加深，每片多核并行度的显著提高，在大规模集群系统上编程计算密集型工程应用程序以获得高性能的任务变得越来越困难。通常情况下，开发有效和高效的软件所需的时间和精力已经成为推进许多科学和工程领域的瓶颈。这一挑战可以通过编译时/运行时系统的进步来克服，这些系统可以减轻程序员的负担，同时在现代和新兴的高性能平台上提供特定应用程序的高性能可移植实例。为了应对这一挑战，该项目正在开发一种新的框架，用于在全球地址空间框架中转换不规则的科学/工程应用程序。这项研究的基础是一个非常不同的和互补的研究方向，以解决在可扩展的分布式内存并行系统上提高程序员生产力、维护可移植性和实现良好性能的挑战。该项目将推进编译器/运行时技术，以便用户可以开发带注释的顺序程序，这些程序由我们的系统自动转换，以便在分布式内存并行系统上有效执行。这种方法的动机是受流行的OpenMP和OpenACC基于pragma的方法的成功推动，这些方法分别将带注释的顺序程序转换为在多核和GPU/加速器系统上并行执行。本文提出了一种基于标注的OpenAPP（异步分区并行）框架，用于在分布式内存并行系统上使用检查员/执行者范式对一类重要的科学/工程程序进行源到源转换。提出的框架将使用几个中型到大型应用程序进行验证。该项目旨在显著降低与有效使用可扩展分布式内存计算机相关的入门门槛，如果寻求比顺序代码提高100倍以上的性能，这是必不可少的。这个项目的成功成果将对那些寻求使用下一代并行系统进行仿真和建模的计算和领域科学家和工程师产生革命性的影响。开发的工具将在开源许可下公开提供给社区。该项目还将组织研讨会，汇集编译器/运行时专家和开发大规模并行科学/工程应用程序的计算科学家。