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

现代科学研究非常依赖于超级计算机。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，并探索联合收割机结合多种应用类型的新研究方法。然而，其设计中的瓶颈降低了现代超级计算机的潜在性能。这个项目bbThemis通过实现被称为“突发缓冲区”的中间存储层的高效和策略驱动共享来解决这个问题，以便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他们的运行时间并提高他们的研究生产力。该项目将为几乎所有使用HPC资源的研究领域带来实质性收益，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HPC系统的设计，部署和利用制定指导方针。该项目还将促进教育成果。除了学生直接致力于项目目标外，该项目开发的成果还将用于德克萨斯州高级计算中心深度学习暑期研究所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，以提升美国在HPC领域的领导地位。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、时间切片内部I/O节点共享、低开销POSIX I/O的函数拦截以及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）确定一套代表性的超级计算应用程序的最佳突发缓冲器配置; 2）提出，原型设计，并验证不同的设计选项，以解决节点内和节点间的I/O性能共享; 3）根据真实的应用和I/O轨迹，设计和评估一组共享策略，如公平共享和优先级共享。该项目将大大增加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。 它探讨了各种共享策略，允许有效地共享I/O资源，并满足计算中心的要求。结果将支持I/O资源的调配，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。这些经验教训将指导下一代大型系统的I/O系统设计。高性能计算、大数据和深度学习应用程序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.