Framework: Software: NSCI: Collaborative Research: Hermes: Extending the HDF Library to Support Intelligent I/O Buffering for Deep Memory and Storage Hierarchy Systems

现代高性能计算（HPC）应用程序产生大量数据。但是，基于磁盘的存储系统的性能改进比内存的性能改进慢得多，造成了显著的输入/输出（I/O）性能差距。为了缩小性能差距，存储子系统正在进行广泛的更改，采用新技术并在内存/存储层次结构中添加更多的层。有了更深的内存层次结构，内存系统的数据移动复杂性就会显著增加，从而更难利用深度内存和存储层次结构（DMSH）设计的潜力。随着我们走向百亿亿次时代，I/O瓶颈是解决高性能计算社区面临的性能瓶颈所必须解决的问题。具有多层内存/存储层的DMSHs提供了一种可行的解决方案，但要有效使用非常复杂。理想情况下，多层存储的存在对应用程序应该是透明的，而不必牺牲I/O性能。需要加强和扩展当前的软件系统，以便在DMSHs下透明和有效地支持数据访问和移动。分层数据格式（HDF）技术是一组当前的I/O解决方案，用于解决组织、访问、分析和保存数据方面的问题。HDF5库在科学界广泛流行。在DOE实验室中使用的高级I/O库中，HDF5是无可否认的领导者，占有99%的份额。HDF5通过隐藏对单个共享文件执行协调I/O的复杂性以及封装通用优化来解决I/O瓶颈。虽然HDF技术和其他现有的I/O中间件一样，并不是为支持DMSHs而设计的，但HDF5的广泛流行及其中间件特性使其成为DMSHs下启用、管理和监督I/O缓冲的理想选择。该项目提出开发Hermes，这是一个异构感知、多层、动态和分布式的I/O缓冲系统，将显著提高I/O性能。该项目提出用Hermes设计扩展HDF技术。Hermes是新的，HDF5的增强也是新的。本研究的交付包括一个增强的HDF5库，一组扩展的HDF技术，以及一组通用I/O缓冲和内存系统优化机制和方法。我们相信，DMSH I/O缓冲和HDF技术的结合是一种可行的实用解决方案，可以有效地支持科学发现。Hermes将通过开发新的缓冲算法和机制来推进HDF5核心技术，以支持1)DMSHs中的垂直和水平缓冲：垂直意味着在本地访问不同级别的数据，水平意味着跨远程计算节点传播/收集数据；2)通过HDF5进行选择性缓冲：这里的选择性是指某些内存层，例如NVMe，仅用于选定的数据；3)通过在线系统分析进行动态缓冲：缓冲模式可以根据消息流量动态更改；4)通过强化学习的自适应缓冲：通过学习应用程序的访问模式，我们可以在运行时适应预取算法和缓存替换策略。开发Hermes将被翻译成高质量可靠的软件，并将与核心HDF5库一起发布。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 high performance computing (HPC) applications generate massive amounts of data. However, the performance improvement of disk based storage systems has been much slower than that of memory, creating a significant Input/Output (I/O) performance gap. To reduce the performance gap, storage subsystems are under extensive changes, adopting new technologies and adding more layers into the memory/storage hierarchy. With a deeper memory hierarchy, the data movement complexity of memory systems is increased significantly, making it harder to utilize the potential of the deep memory and storage hierarchy (DMSH) design. As we move towards the exascale era, I/O bottleneck is a must to solve performance bottleneck facing the HPC community. DMSHs with multiple levels of memory/storage layers offer a feasible solution but are very complex to use effectively. Ideally, the presence of multiple layers of storage should be transparent to applications without having to sacrifice I/O performance. There is a need to enhance and extend current software systems to support data access and movement transparently and effectively under DMSHs. Hierarchical Data Format (HDF) technologies are a set of current I/O solutions addressing the problems in organizing, accessing, analyzing, and preserving data. HDF5 library is widely popular within the scientific community. Among the high level I/O libraries used in DOE labs, HDF5 is the undeniable leader with 99% of the share. HDF5 addresses the I/O bottleneck by hiding the complexity of performing coordinated I/O to single, shared files, and by encapsulating general purpose optimizations. While HDF technologies, like other existing I/O middleware, are not designed to support DMSHs, its wide popularity and its middleware nature make HDF5 an ideal candidate to enable, manage, and supervise I/O buffering under DMSHs. This project proposes the development of Hermes, a heterogeneous aware, multi tiered, dynamic, and distributed I/O buffering system that will significantly accelerate I/O performance. This project proposes to extend HDF technologies with the Hermes design. Hermes is new, and the enhancement of HDF5 is new. The deliveries of this research include an enhanced HDF5 library, a set of extended HDF technologies, and a group of general I/O buffering and memory system optimization mechanisms and methods. We believe that the combination of DMSH I/O buffering and HDF technologies is a reachable practical solution that can efficiently support scientific discovery. Hermes will advance HDF5 core technology by developing new buffering algorithms and mechanisms to support 1) vertical and horizontal buffering in DMSHs: here vertical means access data to/from different levels locally and horizontal means spread/gather data across remote compute nodes; 2) selective buffering via HDF5: here selective means some memory layer, e.g. NVMe, only for selected data; 3) dynamic buffering via online system profiling: the buffering schema can be changed dynamically based on messaging traffic; 4) adaptive buffering via Reinforcement Learning: by learning the application's access pattern, we can adapt prefetching algorithms and cache replacement policies at runtime. The development Hermes will be translated into high quality dependable software and will be released with the core HDF5 library.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.