OAC Core: LABIOS: Storage Acceleration via Data Labeling and Asynchronous I/O

OAC 核心：LABIOS：通过数据标签和异步 I/O 进行存储加速

• 批准号: 2313154
• 负责人: Xian-He Sun
• 金额: $ 60万
• 依托单位: Illinois Institute of Technology
• 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: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2313154&HistoricalAwards=false
• 关键词: OAC; Core; LABIOS; Storage; Acceleration

Data is everywhere, and it is growing exponentially. Data-intensive computing is a field that deals with processing and analyzing massive amounts of data for various purposes, such as scientific discovery, business intelligence, social media, and health care. However, storing and accessing data efficiently and effectively is a major challenge for data-intensive computing. Current storage systems are not designed to handle the diverse and complex data needs of different applications. They are slow, rigid, and wasteful. This project introduces a new way of managing data that overcomes these limitations. It develops a new data representation and a companion storage system that are faster, more flexible, and more scalable than existing solutions. This project also shows how this new approach improves the performance and functionality of various applications in different application domains, such as scientific computing, deep learning, and microservices. It advances the state-of-the-art in data-intensive computing and serves the national interest by enhancing scientific productivity and output, increasing the scalability and reliability of scientific applications that handle massive and complex data sets, and facilitating the collaboration and communication among scientists from different disciplines by improving the compatibility of scientific applications that use different data formats, platforms, and protocols. This project can foster interdisciplinary research and innovation. It also supports education and diversity in computing and data science by creating a well-trained workforce and enhancing the existing curricula and research programs.The goal of this project is to develop and evaluate a new input/output (I/O) paradigm for data-intensive computing based on the concept of a Label and a system called LABIOS. A Label is a data unit that encapsulates an operation and a pointer to its input data. LABIOS is a distributed storage system that executes Labels asynchronously and independently on servers that support tiered storage (e.g., non-volatile memory express (NVMe), solid state drive (SSD), hard disk drive (HDD), etc.). This project investigates how to design and implement LABIOS to provide active storage semantics, software defined storage semantics, and in-situ and in-transit analytics. It explores how to use LABIOS to support elastic and dynamic resource provisioning, integrated storage access, and programmable storage application programming interfaces (APIs). The performance and functionality of LABIOS are evaluated using various applications and workloads from scientific and Big Data domains, such as scientific computing, deep learning, and microservices; and how LABIOS improves the I/O efficiency and effectiveness of these applications and workloads are demonstrated. This project also analyzes the implications of the new I/O paradigm for the existing high-performance computing (HPC) ecosystem. It contributes to the advancement of data-intensive computing by providing a novel data representation and a scalable storage system that can address the diverse and complex data requirements of large-scale applications.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.

数据无处不在，并且呈指数级增长。数据密集型计算是一个处理和分析大量数据的领域，用于各种目的，如科学发现，商业智能，社交媒体和医疗保健。然而，高效地存储和访问数据是数据密集型计算的主要挑战。当前的存储系统并不是设计来处理不同应用程序的多样化和复杂的数据需求。他们是缓慢的，僵化的，浪费的。该项目引入了一种新的管理数据的方式，克服了这些限制。它开发了一种新的数据表示和配套存储系统，比现有解决方案更快，更灵活，更可扩展。该项目还展示了这种新方法如何提高不同应用领域（如科学计算、深度学习和微服务）中各种应用程序的性能和功能。它通过提高科学生产力和产出，提高处理大量复杂数据集的科学应用程序的可扩展性和可靠性，并通过提高使用不同数据格式，平台和协议的科学应用程序的兼容性来促进不同学科科学家之间的合作和交流，从而推动了数据密集型计算的发展，并为国家利益服务。该项目可以促进跨学科研究和创新。它还通过创建训练有素的劳动力和加强现有的课程和研究计划来支持计算和数据科学的教育和多样性。该项目的目标是开发和评估一种新的输入/输出（I/O）范式，用于基于标签和LABIOS系统的数据密集型计算。标签是一个数据单元，它封装了一个操作和一个指向其输入数据的指针。LABIOS是一个分布式存储系统，它在支持分层存储的服务器上异步和独立地执行标签（例如，快速非易失性存储器（NVMe）、固态驱动器（SSD）、硬盘驱动器（HDD）等）。本项目研究如何设计和实现LABIOS，以提供主动存储语义，软件定义的存储语义，以及原位和在途分析。它探讨了如何使用LABIOS来支持弹性和动态资源调配、集成存储访问和可编程存储应用程序编程接口（API）。LABIOS的性能和功能使用来自科学和大数据领域的各种应用程序和工作负载进行评估，例如科学计算，深度学习和微服务;以及LABIOS如何提高这些应用程序和工作负载的I/O效率和有效性。该项目还分析了新I/O范式对现有高性能计算（HPC）生态系统的影响。它通过提供新颖的数据表示和可扩展的存储系统，为数据密集型计算的发展做出了贡献。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。

项目成果

LuxIO: Intelligent Resource Provisioning and Auto-Configuration for Storage Services

• DOI: 10.1109/hipc56025.2022.00041
• 发表时间: 2022-12
• 期刊: 2022 IEEE 29th International Conference on High Performance Computing, Data, and Analytics (HiPC)
• 作者: Keith Bateman;N. Rajesh;Jaime Cernuda Garcia;Luke Logan;Jie Ye;Stephen Herbein;Anthony Kougkas;Xian-He Sun

An Evaluation of DAOS for Simulation and Deep Learning HPC Workloads

• DOI: 10.1145/3578353.3589542
• 发表时间: 2023-05
• 期刊: Proceedings of the 3rd Workshop on Challenges and Opportunities of Efficient and Performant Storage Systems
• 作者: Luke Logan;J. Lofstead;Xian-He Sun;Anthony Kougkas