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; 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）用于解决诸如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，基因组学和生物信息学，天文学，材料科学，自身抗体，辩护，数据计算和财务计算的癌症研究和药物的发现，天气预报和气候变化的多样性，癌症研究和药物的发现，天气预报和气候变化。这是许多科学发现和工程创新的关键。但是，对更多计算能力的需求永远不会结束。需要更大规模机器上的计算能力才能揭示科学的奥秘。 消息通讯接口（MPI）是HPC系统中通信的事实上的标准，迄今为止，大型科学和工程应用程序使用的主要并行编程模型。此类应用程序中的过程计算其本地数据，同时通过互连网络进行广泛通信。事实证明，MPI是可扩展的，并且在当前系统中正常过渡。但是，在以大规模平行性，高度分层体系结构和通信渠道，每个核心和异质性的记忆较小的极端尺度系统上，将对互连网络和通信系统软件产生巨大压力，以提供所需的性能和可伸缩性。这项研究旨在应对极限系统上高性能和可扩展的通信子系统的挑战。拟议的研究在MPI和Hybrid MPI+X编程模型中解决了高性能通信和系统软件中的关键问题，这是高度原始和创新的。这样的研究将为行业采用铺平道路。 这项研究的结果将与加拿大的各个部门有关，包括加拿大环境部，加拿大，加拿大基因组科学中心，汽车和石油/天然气行业，以及最终是整个加拿大公众。预计这项研究的发现将对目标社区产生重大影响，并将导致未来研究的新方向。在这样的关键和快节奏的领域中，这项研究的结果将使加拿大处于科学技术的最前沿。拟议的研究是培训HQP的理想选择，因为它具有很强的基础，可以立即转化为实际的应用和实施。对HPC和网络的毕业生的需求很高，受过HQP培训的人将有能力竞争学术界和行业的工作。