High-performance and scalable communication subsystems for exascale computing

用于百亿亿次计算的高性能和可扩展通信子系统

• 批准号: 238964-2011
• 负责人: Afsahi, Ahmad
• 金额: $ 1.6万
• 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: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=547153
• 关键词: performance; scalable; communication; subsystems; exascale

High-Performance Computing (HPC) is the key to many scientific discoveries and engineering innovations. It is used to tackle computationally-intensive problems in fields as diverse as drug discovery, modeling of global climate system, seismic processing for oil and gas, green energy, genomics and bioinformatics, and astrophysics. Scientific/engineering simulations are mainly written with the Message-Passing Interface (MPI) library. Parallel processes in these simulations compute on their local data while extensively communicating with each other through the MPI library. Communication adversely affects the performance and scalability of MPI applications running on HPC clusters. With the availability of multi-core and soon many-core architectures offering increasing parallelism at all levels, HPC clusters consisting of hundreds of thousands of nodes with millions of cores and complex network topologies are poised to break the Exaflops (10^18 floating point operations per second) barrier in the coming years. With the emergence of such highly hierarchical clusters, MPI has to be optimized for performance and scalability in order to cope with the ever-increasing demands of large-scale simulations. The proposed research is highly original and innovative in the sense that it addresses key issues in MPI, by including topology-awareness for process mapping, by incorporating dedicated queues for partner processes, by quality of service provisioning partner/non-partner traffic, and by developing fiber-based asynchronous progression techniques. The outcome of this research will be relevant to various sectors in Canada, including Environment Canada, Compute Canada, Canada Genome Sciences Centre, oil and 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. 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) 是许多科学发现和工程创新的关键。它用于解决药物发现、全球气候系统建模、石油和天然气地震处理、绿色能源、基因组学和生物信息学以及天体物理学等领域的计算密集型问题。科学/工程模拟主要使用消息传递接口（MPI）库编写。这些模拟中的并行进程计算本地数据，同时通过 MPI 库相互广泛通信。通信会对 HPC 集群上运行的 MPI 应用程序的性能和可扩展性产生不利影响。随着多核和即将推出的众核架构的出现，在各个级别上提供越来越多的并行性，由数十万个节点、数百万个核心和复杂的网络拓扑组成的 HPC 集群有望突破 Exaflops（10^18 浮点运算）每秒）未来几年的障碍。随着这种高度分层集群的出现，MPI 必须针对性能和可扩展性进行优化，以应对大规模模拟不断增长的需求。拟议的研究具有高度原创性和创新性，因为它解决了 MPI 中的关键问题，包括流程映射的拓扑感知、合并合作伙伴流程的专用队列、提供合作伙伴/非合作伙伴流量的服务质量以及开发基于光纤的异步进展技术。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、石油和天然气行业，并最终与加拿大广大公众相关。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。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。