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得精确地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。随着存储器层次结构的加深，每个芯片的多核并行度显著提高，对计算密集型工程应用程序进行编程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。通常情况下，开发有效和高效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发展的瓶颈。这一挑战可以通过编译时/运行时系统的进步来克服，该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。这项研究是基于一个非常不同的和互补的研究方向，目前的努力，在解决提高程序员的生产力，保持可移植性，并实现良好的性能可扩展的分布式内存并行系统的挑战。该项目将推进编译器/运行时技术，使用户可以开发带注释的顺序程序，由我们的系统自动转换为分布式内存并行系统上的高效执行。这种方法的动机是流行的OpenMP和OpenACC pragma为基础的方法转换注释顺序程序的多核和GPU/加速器系统上的并行执行，分别成功。提出了一种基于注释的OpenAPP（APP - Asynchronous Partitioned Objectelism）框架，用于一类重要的科学/工程程序的源到源转换，该框架使用检查器/执行器范式在分布式存储器并行系统上执行。建议的框架将使用几个中型到大型的应用程序进行验证。该项目旨在显着降低与有效使用可扩展的分布式内存计算机相关的进入壁垒，这对于寻求比顺序代码性能提高100倍以上至关重要。该项目的成功成果将为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带来变革。开发的工具将在开源许可证下向社区公开提供。该项目还将组织讲习班，汇集编译器/运行时专家和计算科学家，开发大规模并行科学/工程应用程序。

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.