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

现代科学研究严重依赖于超级计算机。超级计算应用，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，并探索结合多种应用类型的新研究方法。然而，它们设计中的瓶颈降低了现代超级计算机的潜在性能。bbThemis项目通过实现被称为“突发缓冲区”的高效和策略驱动的中间存储层共享来解决这一问题，因此更多的科学家和应用程序可以利用最先进的存储技术来显着减少其运行时间并提高其研究效率。该项目将为几乎所有使用高性能计算资源的研究领域带来可观的收益，从而改进所有领域的科学和工程方法和产品。这项研究将对现有的科学应用产生直接而重大的影响，并为下一代高性能计算系统的设计、部署和利用提供指导。该项目还将促进教育成果。除了学生直接为项目目标工作外，项目中开发的结果将用于德克萨斯高级计算中心暑期学院的深度学习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计划（NSCI）保持一致，以推进美国在高性能计算领域的领导地位。这个名为bbThemis （https://github.com/bbThemis）的项目利用了一套技术，比如I/O处理与控制逻辑的分离、分时I/O节点共享、用于低开销POSIX I/O的功能拦截，以及用于优化单个应用程序性能的元数据和数据放置。它正在研究如何最好地应用这些技术，通过：1)为一套具有代表性的超级计算应用程序确定最佳突发缓冲配置；2)提出，原型设计，并验证不同的设计方案，以解决节点内和节点间的I/O性能共享；3)设计和评估一组共享策略，如公平共享和优先级共享，并结合实际应用和I/O跟踪。该项目将极大地提高现有突发缓冲区的共享能力，并大规模地提高领域科学家的生产力。本文探讨了各种共享策略，这些策略允许有效地共享I/O资源并满足计算中心的需求。结果将支持I/O资源的分配，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现有超级计算机的容量，增强I/O性能。吸取的经验教训将指导大规模系统的下一代I/O系统设计。高性能计算、大数据和深度学习应用的普遍改进也将增加用于数据分析、计算、建模和仿真的硬件和软件的一致性。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.