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。它被用于解决药物发现、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在计算其本地数据的同时，通过MPI库彼此进行广泛的通信。通信会对运行在HPC集群上的MPI应用程序的性能和可伸缩性产生不利影响。随着多核和即将到来的众核架构的可用性，在所有级别上提供越来越多的并行性，由数十万个节点组成的HPC集群具有数百万个核心和复杂的网络拓扑，有望在未来几年内打破Exaflops（每秒10^18次浮点运算）的障碍。随着这种高度层次化集群的出现，MPI必须针对性能和可扩展性进行优化，以科普不断增长的大规模仿真需求。拟议的研究是高度原创性和创新的意义上，它解决了MPI中的关键问题，包括拓扑感知的进程映射，通过将专用队列的合作伙伴进程，通过服务质量供应合作伙伴/非合作伙伴的流量，并通过开发基于光纤的异步进展技术。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、加拿大计算部、加拿大基因组科学中心、石油和天然气工业，最终与加拿大公众有关。预计本研究的结果将对目标社区产生重大影响，并为未来的研究提供新的方向。拟议的研究是理想的培训HQP，因为它有一个强大的基础，立即转化为实际应用和实施。HPC和网络专业的毕业生需求量很大，经过培训的HQP将能够很好地竞争学术界和工业界的工作。