Travel: Workshop on Clusters, Clouds, and Data Analytics for Scientific Computing 2024

旅行：2024 年科学计算集群、云和数据分析研讨会

• 批准号: 2336813
• 负责人: Jack Dongarra
• 金额: $ 2.5万
• 依托单位: University of Tennessee Knoxville
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-15 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2336813&HistoricalAwards=false
• 关键词: Travel; Workshop; Clusters; Clouds; Data

The research areas of cluster, cloud, and data-analytics computing are undergoing significant transformation due to two major trends. The first trend is the emergence of multicore and hybrid microprocessor designs, which require system designers to prioritize energy usage and application designers to leverage parallelism and data locality. However, the traditional high-performance computing (HPC) software stack is not well-suited for these new architectures with numerous nodes, cores, accelerators, and reduced memory per core. The second trend is the exponential growth in data generated and analyzed by cutting-edge scientific applications and their user communities. This poses challenges not only in processing and managing vast amounts of data but also in making the data accessible to participants in large national and international collaborations, spread across various administrative domains and utilizing different resources like clusters, clouds, and data analysis tools. These new conditions present several complex design and deployment challenges for the cyberinfrastructure research community. They must address issues related to scalability, programmability, performance, interoperability, resilience, resource virtualization, data logistics, and system management. The Workshop on Clusters, Clouds, and Data Analytics in Scientific Computing (CCDASC) will be held to tackle these challenges and facilitate fruitful discussions. This workshop aims to bring together leading-edge researchers to collaboratively review, analyze, and gain new insights into these developments, and translate these advancements into significant benefits for the scientific community. The workshop aims to achieve its goal by focusing on the commonalities and interactions between clusters, clouds, and data analytics and how they can support data and compute-intensive collaborations. These computational infrastructures share architectural similarities, and addressing issues related to cloud computing encompasses those concerning clusters. The growing importance of data-driven research and cross-domain collaboration underscores the need for solutions to data interoperability and logistics challenges. Key topics to be explored in the workshop include overcoming scalability challenges for high performance and computational efficiency, developing portable software for various platforms (clusters, grids, clouds), creating common protocols and APIs for seamless data movement and analysis across resource domains, designing easier programming paradigms for parallel systems, and solving system management problems arising from multicore architectures, resource virtualization, and data-intensive applications. The workshop format fosters synergistic effects among researchers from different focus areas, facilitating coordination for high-impact research that spans multiple domains. The workshop's tasks include surveying and analyzing deployment, operational, and usage issues for clusters, clouds, and data analytics, documenting the current state-of-the-art in each area, sharing experiences of research communities and science domains benefiting from these technologies, exploring interoperability among clouds and grids, and identifying future research directions in light of disruptive trends and technological gaps.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

由于两个主要趋势，集群、云和数据分析计算的研究领域正在经历重大转变。第一个趋势是多核和混合微处理器设计的出现，这要求系统设计人员优先考虑能源使用，应用程序设计人员利用并行性和数据局部性。然而，传统的高性能计算（HPC）软件堆栈并不适合这些具有众多节点、内核、加速器和每个内核内存减少的新架构。第二个趋势是尖端科学应用及其用户社区生成和分析的数据呈指数级增长。这不仅在处理和管理大量数据方面提出了挑战，而且在使大型国家和国际合作的参与者能够访问数据方面也提出了挑战，这些数据分布在各个行政领域，并利用不同的资源，如集群，云和数据分析工具。这些新条件为网络基础设施研究界带来了几个复杂的设计和部署挑战。它们必须解决与可扩展性、可编程性、性能、互操作性、弹性、资源虚拟化、数据物流和系统管理相关的问题。科学计算中的集群、云和数据分析研讨会（CCDASC）将应对这些挑战，并促进富有成效的讨论。本次研讨会旨在汇集前沿研究人员，共同回顾、分析这些发展，并获得新的见解，将这些进展转化为科学界的重大利益。研讨会旨在通过关注集群、云和数据分析之间的共性和相互作用，以及它们如何支持数据和计算密集型协作来实现其目标。这些计算基础设施共享架构相似性，并且解决与云计算相关的问题包括与集群相关的问题。数据驱动的研究和跨领域协作的重要性日益增长，这凸显了对数据互操作性和物流挑战的解决方案的需求。研讨会将探讨的主要主题包括克服可扩展性挑战以实现高性能和计算效率，开发适用于各种平台的可移植软件（集群、网格、云），为跨资源域的无缝数据移动和分析创建通用协议和API，为并行系统设计更简单的编程范例，并解决多核架构、资源虚拟化、和数据密集型应用程序。研讨会的形式促进了来自不同重点领域的研究人员之间的协同效应，促进了跨多个领域的高影响力研究的协调。研讨会的任务包括调查和分析集群、云和数据分析的部署、运营和使用问题，记录每个领域的当前最新技术水平，分享研究社区和科学领域从这些技术中受益的经验，探索云和网格之间的互操作性，并根据颠覆性趋势和技术差距确定未来的研究方向。该奖项反映了NSF的法定使命，并通过使用基金会的学术价值和更广泛的影响审查标准。