Collaborative Research: OAC Core: Small: Efficient and Policy-driven Burst Buffer Sharing

合作研究：OAC Core：小型：高效且策略驱动的突发缓冲区共享

• 批准号: 2008388
• 负责人: Zhao Zhang
• 金额: $ 29.29万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008388&HistoricalAwards=false
• 关键词: Collaborative; Research; OAC; Core; Small

Modern scientific research heavily relies on supercomputers. Supercomputing applications, such as traditional numerical simulations (HPC), data intensive applications (Big Data), and most recently, deep learning (DL) applications, are increasingly run on supercomputers to obtain timely results and explore new research methods that combine multiple application types. However, a bottleneck in their design reduces the potential performance of modern supercomputers. This project, bbThemis, addresses this problem by enabling efficient and policy-driven sharing of an intermediate storage layer known as a "burst buffer", so that more scientists and applications can leverage state-of-the-art storage techniques to significantly reduce their runtime and enhance the productivity of their research. This project will deliver substantial gains to almost every research area that uses HPC resources, leading to improved science and engineering methods and products in all fields. This research will have an immediate and significant impact on existing scientific applications and on deriving guidelines for next-generation HPC system design, deployment, and utilization. The project will also contribute to educational outcomes. In addition to students working directly on project goals, results developed in the project will be used in tutorial and training sessions at Texas Advanced Computing Center’s summer institute in deep learning and other major conferences, and in University of Illinois Urbana-Champaign student projects. The project is aligned with the National Strategic Computing Initiative (NSCI) to advance US leadership in HPC.This project, bbThemis (https://github.com/bbThemis), leverages a suite of technologies, such as disassociation of I/O processing from control logic, time-sliced intra I/O node sharing, function interception for low overhead POSIX I/O, and metadata and data placement for optimal individual application performance. It is investigating how to best apply these technologies, by: 1) Identifying optimal burst buffer configurations for a suite of representative supercomputing applications; 2) Proposing, prototyping, and verifying different design options to address intra-node and inter-node I/O performance sharing; and 3) Designing and evaluating a set of sharing policies, such as fair sharing and priority sharing, with real applications and I/O traces. This project will dramatically increase the sharing capacity of existing burst buffers and enhance domain scientists’ productivity at a large scale. It explores various sharing policies that permit efficient sharing of I/O resources and that meet the requirements of computing centers. The results will enable the provisioning of I/O resources, where users can request specific IOPS or bandwidth for a period of time. The prototype burst buffer sharing framework will immediately increase the capacity of existing supercomputers with enhanced I/O performance. The lessons learned will guide next-generation I/O system design for large scale systems. The general improvement of HPC, Big Data, and DL applications will also increase the coherence of the hardware and software used for data analytics computing and modeling and simulation.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.

现代科学研究严重依赖超级计算机应用，例如传统的数值模拟（HPC）、数据密集型应用（大数据）以及最近的深度学习（DL）应用，越来越多地在超级计算机上运行以获得及时的结果和探索。然而，其设计中的瓶颈降低了现代超级计算机的潜在性能，该项目 bbThemis 通过实现高效且策略驱动的中间存储层共享来解决这个问题。 “突发缓冲区”，以便更多的科学家和应用程序能够利用最先进的存储技术来显着减少运行时间并提高研究生产力。该项目将为几乎所有使用 HPC 资源的研究领域带来巨大收益。 ，从而改进所有领域的科学和工程方法和产品，该研究将对现有的科学应用以及下一代 HPC 系统设计、部署和利用的指导方针产生直接和重大的影响。除了直接从事项目的学生之外。为了实现目标，该项目开发的成果将用于德克萨斯州高级计算中心的深度学习暑期学院和其他主要会议的教程和培训课程，以及伊利诺伊大学厄巴纳-香槟分校的学生项目。该项目与国家战略计算保持一致。旨在提升美国在 HPC 领域领导地位的倡议 (NSCI)。该项目 bbThemis (https://github.com/bbThemis) 利用了一套技术，例如将 I/O 处理与控制逻辑分离，时间片内 I/O 节点共享、低开销 POSIX I/O 的函数拦截以及最佳单个应用程序性能的元数据和数据放置正在研究如何最好地应用这些技术，方法是：1）确定最佳突发。一套代表性超级计算应用程序的缓冲区配置；2) 提出、原型设计和验证不同的设计选项，以解决节点内和节点间 I/O 性能共享问题；以及 3) 设计和评估一组共享策略，例如作为公平共享和优先级共享，通过实际应用程序和 I/O 跟踪，该项目将极大地提高现有突发缓冲区的共享容量，并大规模提高领域科学家的生产力。它探索了允许有效共享 I 的各种共享策略。 /O 资源并且满足计算中心的要求，结果将启用 I/O 资源的配置，用户可以在一段时间内请求特定的 IOPS 或带宽，原型突发缓冲区共享框架将立即增加计算中心的容量。现有超级计算机具有增强的 I/O 性能。汲取的经验教训将指导大规模系统的下一代 I/O 系统设计。 HPC、大数据和深度学习应用的总体改进也将提高用于数据分析计算、建​​模和仿真的硬件和软件的一致性。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Fine-grained Policy-driven I/O Sharing for Burst Buffers

细粒度策略驱动的突发缓冲区 I/O 共享

• DOI: 10.1145/3581784.3607041
• 发表时间: 2023-11
• 期刊: ACM
• 作者: Karrels, Ed;Huang, Lei;Kan, Yuhong;Arora, Ishank;Wang, Yinzhi;Katz, Daniel S.;Gropp, William;Zhang, Zhao
• 通讯作者: Zhang, Zhao