Collaborative Research: SHF: Medium: A New Direction of Research and Development to Fulfill the Promise of Computational Storage

合作研究：SHF：Medium：实现计算存储承诺的研发新方向

• 批准号: 2210754
• 负责人: Tong Zhang
• 金额: $ 40万
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2210754&HistoricalAwards=false
• 关键词: Collaborative; Research; SHF; Medium; New

As the speed gap between processor and storage keeps widening, moving computation closer to data is a promising direction for high-speed data processing. However, multiple challenging issues, such as the limited resources on device hardware, stringent cost and power constraints, difficulties in software development, and the lack of systematic solutions, are unfortunately hindering a widespread adoption of computational storage in reality. This project addresses these critical issues by using a cohesive approach to turn computational storage into a cooperative component in the whole system. This project also aims to make a broader impact by training students at different levels with research activities, enriching curriculum and classroom teaching with new research results, and contributing to educational and outreach activities.This project makes an effort to address the challenging research issues, aiming to provide a new direction to fulfill the promise of computational storage. By using a holistic and system-oriented methodology, the project investigates critical research issues across multiple layers in the system hierarchy and develops effective solutions. Specifically, the project studies multiple important aspects in order to systematically integrate computational storage into existing computing ecosystems, such as designing a service-oriented abstraction for applications, optimizing system-level resource utilization, leveraging proximity and mitigating memory resource contention in device hardware, adapting core data structures and algorithms of applications to fully exploit heterogeneous computing resources, etc. A set of representative application cases is also studied for effectively leveraging computational storage. The success of this project will make broad and significant contributions to enable computational storage to address critical challenges in increasingly more data-centric 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.

随着处理器和存储之间的速度差距不断扩大，使计算更接近数据是高速数据处理的一个有前途的方向。然而，设备硬件资源有限、成本和功耗严格限制、软件开发困难以及缺乏系统解决方案等多种挑战性问题阻碍了计算存储在现实中的广泛采用。该项目通过使用内聚方法将计算存储转变为整个系统中的协作组件来解决这些关键问题。该项目还旨在通过研究活动培训不同级别的学生，用新的研究成果丰富课程和课堂教学，并为教育和推广活动做出贡献，从而产生更广泛的影响。该项目致力于解决具有挑战性的研究问题，旨在提供一个新的方向来实现计算存储的承诺。通过使用整体和面向系统的方法，该项目跨系统层次结构的多个层面调查关键研究问题，并开发有效的解决方案。具体来说，该项目研究了多个重要方面，以便系统地将计算存储集成到现有的计算生态系统中，例如为应用程序设计面向服务的抽象、优化系统级资源利用率、利用邻近性并减轻设备硬件中的内存资源争用、适应应用的核心数据结构和算法，充分利用异构计算资源等。还研究了一组代表性应用案例，以有效利用计算存储。该项目的成功将为计算存储做出广泛而重大的贡献，以应对日益以数据为中心的应用中的关键挑战。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和更广泛的影响进行评估，被认为值得支持审查标准。