Efficient and Scalable Communication and System Software for Exascale Computing

用于百亿亿次计算的高效且可扩展的通信和系统软件

• 批准号: RGPIN-2016-05389
• 负责人: Afsahi, Ahmad
• 金额: $ 2.26万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=708929
• 关键词: Efficient; Scalable; Communication; System; Software

High-Performance Computing (HPC) is used to tackle computationally-intensive problems in fields as diverse as green energy and biofuels, cancer research and drug discovery, weather forecasting and climate change, seismic processing for oil and gas, genomics and bioinformatics, astrophysics, material science, automotive, defense, data mining and analytics, and financial computing. It is the key to many scientific discoveries and engineering innovations. However, the demand for more computational power is never ending. Far more computational power on much larger-scale machines is required to unravel the scientific mysteries. The Message-Passing Interface (MPI) is the de facto standard for communication in HPC systems, and by far the dominant parallel programming model used by large-scale scientific and engineering applications. Processes in such applications compute on their local data while extensively communicating with each other through the interconnection networks. MPI has proved to be scalable and is smoothly transitioning in the current systems. However, on extreme-scale systems that are characterized by massive parallelism, highly hierarchical architectures and communication channels, smaller memory per core and heterogeneity, there will be immense pressure on the interconnection networks and communication system software to deliver the required performance and scalability. This research seeks to address the challenges for high-performance and scalable communication subsystems on extreme-scale systems. The proposed research is highly original and innovative in the sense that it addresses key questions in high-performance communications and system software in MPI and hybrid MPI+X programming models. Such research will pave the way for adoption by industry. The outcome of this research will be relevant to various sectors in Canada, including Environment Canada, Compute Canada, Canada Genome Sciences Centre, automotive and oil/gas industries, and ultimately the Canadian public at large. It is expected that the findings from this research will have significant impact on the target community, and that it will lead to new directions for future research. In such a key and fast-paced field, the results of this research will keep Canada at the forefront of science and technology. The proposed research is ideal for training HQP in that it has a strong foundation that translates immediately into practical applications and implementations. There is a high demand for graduates in HPC and networking, and the HQP trained will be well positioned to compete for jobs in academia and industry.

高性能计算 (HPC) 用于解决绿色能源和生物燃料、癌症研究和药物发现、天气预报和气候变化、石油和天然气地震处理、基因组学和生物信息学、天体物理学等领域的计算密集型问题，材料科学、汽车、国防、数据挖掘和分析以及金融计算。它是许多科学发现和工程创新的关键。然而，对更多计算能力的需求永无止境。要解开科学之谜，需要在更大规模的机器上拥有更多的计算能力。 消息传递接口 (MPI) 是 HPC 系统中通信的事实上的标准，也是迄今为止大规模科学和工程应用程序使用的主要并行编程模型。此类应用程序中的进程对其本地数据进行计算，同时通过互连网络相互广泛通信。 MPI 已被证明具有可扩展性，并且正在当前系统中顺利过渡。然而，在以大规模并行性、高度分层架构和通信通道、每核内存较小和异构性为特征的超大规模系统上，互连网络和通信系统软件将面临巨大的压力，无法提供所需的性能和可扩展性。这项研究旨在解决超大规模系统上高性能和可扩展通信子系统的挑战。拟议的研究具有高度原创性和创新性，因为它解决了 MPI 和混合 MPI+X 编程模型中高性能通信和系统软件的关键问题。此类研究将为行业采用铺平道路。 这项研究的成果将与加拿大的各个部门相关，包括加拿大环境部、加拿大计算部、加拿大基因组科学中心、汽车和石油/天然气行业，并最终与加拿大公众相关。预计这项研究的结果将对目标群体产生重大影响，并将为未来的研究带来新的方向。在这样一个关键且快节奏的领域，这项研究成果将使加拿大保持在科学技术的前沿。拟议的研究非常适合培训 HQP，因为它具有可立即转化为实际应用和实施的坚实基础。 HPC 和网络领域对毕业生的需求很高，受过培训的 HQP 将能够很好地竞争学术界和工业界的职位。