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，它正在成倍增长。数据密集型计算是一个领域，涉及用于各种目的的处理和分析大量数据，例如科学发现，商业智能，社交媒体和医疗保健。但是，对于数据密集型计算，有效，有效地存储和访问数据是一个主要挑战。当前的存储系统并非旨在满足不同应用程序的多样化和复杂的数据需求。他们很慢，僵硬且浪费。该项目介绍了一种克服这些限制的数据的新方法。它开发了一个新的数据表示形式和一个比现有解决方案更快，更灵活且更可扩展的伴随存储系统。该项目还显示了这种新方法如何改善不同应用领域中各种应用的性能和功能，例如科学计算，深度学习和微服务。它可以通过提高科学生产力和产出来提高数据密集型计算的最先进，并通过提高科学生产力和产出，从而提高处理大规模和复杂数据集的科学应用的可扩展性和可靠性，并通过使用不同的数据使用不同数据的科学应用程序来改善不同学科的科学家之间的协作和交流，以利用不同的学科，平台，平台，平台和协议，平台，和协议，平台，和协议，和协议，和协议，和协议，和协议，和协议。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并增强现有的课程和研究计划来支持计算和数据科学方面的教育和多样性。该项目的目的是根据基于数据密集型计算的新输入/输出（I/O）基于标签和名为LABIOS的系统来开发和评估一个新的输入/输出（I/O）范式。标签是一个数据单元，可封装操作和指向其输入数据的指针。 Labios是一个分布式存储系统，可在支持分层存储的服务器上独立执行标签（例如非挥发性内存Express（NVME），固态驱动器（SSD），硬盘驱动器（HDD）等）。该项目调查了如何设计和实施Labios，以提供主动存储语义，软件定义的存储语义以及原位和原地分析。它探讨了如何使用LABIOS支持弹性和动态资源提供，集成的存储访问以及可编程的存储应用程序编程接口（API）。 Labios的性能和功能是使用科学和大数据领域（例如科学计算，深度学习和微服务）的各种应用和工作量评估的； Labios如何改善这些应用和工作量的I/O效率和有效性。该项目还分析了新的I/O范式对现有高性能计算（HPC）生态系统的含义。它通过提供新颖的数据表示和可扩展的存储系统来促进数据密集型计算的进步，该系统可以解决大规模应用程序的多样化和复杂的数据要求。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响审查标准来通过评估来获得支持的。

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