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

在过去的几十年里，计算机速度和性能的巨大进步使精确模拟日益复杂的现象成为可能。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。随着存储层次结构的深化，每片多核并行程度的显著提高，编程计算密集型工程应用程序以在大规模集群系统上获得高性能的任务变得越来越困难。通常的情况是，开发有效和高效的软件所需的时间和精力已经成为推动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化。为了解决这一挑战，该项目正在开发一个新的框架，用于在全球地址空间框架中转换非常规的科学/工程应用程序。这项研究的基础是一个非常不同的和补充的研究方向，以应对提高程序员生产率、保持可移植性和在可伸缩的分布式存储并行系统上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，由我们的系统自动转换，以便在分布式内存并行系统上高效执行。这种方法的动机是流行的基于OpenMP和Openacc杂注的方法的成功，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。针对一类重要的科学/工程程序，采用检查器/执行器范式，提出了一种基于注解的OpenAPP(APP-ASynchronous Partited Parallism)框架，用于在分布式内存并行系统上执行。该项目旨在显著降低与有效使用可扩展分布式存储计算机相关的进入门槛，如果寻求比顺序代码提高100倍以上的性能，则这是至关重要的。该项目的成功成果将对寻求使用下一代并行系统进行模拟和建模的计算和领域科学家和工程师产生革命性的影响。开发的工具将在开放源码许可下向社区公开提供。该项目还将组织研讨会，将编译器/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聚集在一起。

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.