SDCI: HPC: Improvement: Infrastructure for Multi-Node Manycore Computing

SDCI：HPC：改进：多节点众核计算基础设施

• 批准号: 1032859
• 负责人: John Owens
• 金额: $ 39.19万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1032859&HistoricalAwards=false
• 关键词: SDCI; HPC; Improvement; Infrastructure; Multi

One of the major challenges facing manycore computing today is to make parallelism accessible to the mainstream programmer. In today?s era, where nearly every computer contains a manycore processor in the form of the GPU, we have not yet successfully built the primitives and techniques that we require to make manycore processing readily available to the entire computing community.One solution to this challenge is the construction of libraries that encapsulate common programming patterns and idioms. Libraries are self-contained and thus easily added to existing projects; can easily be upgraded; and are well-suited for development and maintenance by academic groups such as ours.High-performance computing is extensively supported by such libraries on the CPU side, but there are few in the manycore world. Our CUDPP (CUDA Data-Parallel Primitives) library is widely used in the GPU computing community, albeit only on single-node (largely non-HPC) systems. We believe the next-generation OpenCL programming environment is the future of manycore programming and thus target our future work toward this standard.Our plan is to extend and support CUDPP for use in the high-performance computing community, which is increasingly adopting manycore processors as core computational engines. We will add single- and many-node primitives to CUDPP and the OpenCL-based CLDPP library, primitives we consider to be vital to the growth of the manycore HPC community. We also plan to define best practices for manycore libraries and to build not just a library but also tools to help others build libraries.The intellectual merit of this proposal lies in the development and improvement of core data structures and algorithms targeted to manycore computing environments in general and HPC computing environments specifically. Other interesting outcomes from this project include multi-node data structures and algorithms and the software engineering of tools to build manycore libraries.Like its predecessor, CUDPP, the library that will result from this work will be open-sourced and widely used in the manycore programming community. The optimization techniques and tools will also find widespread use. We will work with the OpenCL consortium and our industry partners to bring libraries into the OpenCL standard, directly impacting the entire manycore community. The PI will also continue to introduce undergraduates to research through manycore computing, including collaborating with our industrial partners to host Google Summer of Code engineers on this project.

当今许多核心计算面临的主要挑战之一是让主流程序员能够访问并行。在今天的S时代，几乎每台计算机都包含一个图形处理器形式的多核处理器，我们还没有成功地构建出使多核处理随时可供整个计算社区使用所需的原语和技术。解决这一挑战的一个方案是构建封装通用编程模式和习惯用法的库。库是自包含的，因此可以很容易地添加到现有项目中；可以很容易地升级；并且非常适合我们这样的学术团体的开发和维护。高性能计算在CPU方面得到了这样的库的广泛支持，但在许多核心世界中几乎没有。我们的CUDPP(CUDA数据并行原语)库在GPU计算社区中广泛使用，尽管仅在单节点(主要是非HPC)系统上使用。我们相信下一代OpenCL编程环境是多核编程的未来，因此我们未来的工作目标是实现这一标准。我们的计划是扩展和支持CUDPP用于高性能计算社区，高性能计算社区正越来越多地采用多核处理器作为核心计算引擎。我们将在CUDPP和基于OpenCL的CLDPP库中添加单节点和多节点基元，我们认为这些基元对许多核心HPC社区的发展至关重要。我们还计划为多核心库定义最佳实践，并不仅构建一个库，还构建帮助其他人构建库的工具。这项建议的智力优势在于开发和改进针对一般多核计算环境和特定于HPC计算环境的核心数据结构和算法。该项目的其他有趣成果包括多节点数据结构和算法，以及构建多核心库的工具的软件工程。与其前身CUDPP一样，这项工作产生的库将是开源的，并在多核编程社区中广泛使用。优化技术和工具也将得到广泛应用。我们将与OpenCL联盟和我们的行业合作伙伴合作，将库引入OpenCL标准，直接影响整个许多核心社区。PI还将继续通过许多核心计算向本科生介绍研究，包括与我们的工业合作伙伴合作，在这个项目上接待Google Summer of Code工程师。