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

基本信息

  • 批准号:
    2210755
  • 负责人:
  • 金额:
    $ 40万
  • 依托单位:
  • 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

项目摘要

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.
随着处理器和存储器之间的速度差距不断扩大,将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,诸如设备硬件上的有限资源、严格的成本和功率约束、软件开发中的困难以及缺乏系统的解决方案等多个具有挑战性的问题不幸地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养不同层次的学生,以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,旨在为实现计算存储的承诺提供新的方向。通过使用整体和面向系统的方法,该项目调查了系统层次结构中多个层次的关键研究问题,并制定了有效的解决方案。具体而言,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,调整应用程序的核心数据结构和算法以充分利用异构计算资源,一组代表性的应用案例,也研究了有效地利用计算存储。该项目的成功将为计算存储解决日益以数据为中心的应用中的关键挑战做出广泛而重大的贡献。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

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Feng Chen其他文献

In situ self-transformation synthesis of g-C3N4-modified CdS heterostructure with enhanced photocatalytic activity
原位自转化合成具有增强光催化活性的g-C3N4修饰的CdS异质结构
  • DOI:
    10.1016/j.apsusc.2015.06.074
  • 发表时间:
    2015-12
  • 期刊:
  • 影响因子:
    6.7
  • 作者:
    Huogen Yu;Fengyun Chen;Feng Chen;Xuefei Wang
  • 通讯作者:
    Xuefei Wang
Determination of iodine in seawater: methods and applications
海水中碘的测定:方法和应用
  • DOI:
    10.1016/b978-0-12-374135-6.00001-7
  • 发表时间:
    2009
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Huabin Li;Xiangrong Xu;Feng Chen
  • 通讯作者:
    Feng Chen
A preliminary investigation of metal element profiles in the serum of patients with bloodstream infections using inductively-coupled plasma mass spectrometry (ICP-MS)
使用电感耦合等离子体质谱 (ICP-MS) 对血流感染患者血清中金属元素谱进行初步研究
  • DOI:
    10.1016/j.cca.2018.07.013
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    5
  • 作者:
    Suying Zhao;Shuyuan Cao;Lan Luo;Zhan Zhang;Gehui Yuan;Yanan Zhang;Yanting Yang;Weihui Guo;Li Wang;Feng Chen;Qian Wu;Lei Li
  • 通讯作者:
    Lei Li
Development and Validation of a Novel Predictive Model for the Early Differentiation of Cardiac and Non-Cardiac Syncope
心源性晕厥和非心源性晕厥早期区分的新型预测模型的开发和验证
  • DOI:
    10.2147/ijgm.s454521
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    2.3
  • 作者:
    Sijin Wu;Zhongli Chen;Yuan Gao;S. Shu;Feng Chen;Ying Wu;Yan Dai;Shu Zhang;Keping Chen
  • 通讯作者:
    Keping Chen
Training of Multi-class Linear Classifier with BFGS Method
用BFGS方法训练多类线性分类器

Feng Chen的其他文献

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{{ truncateString('Feng Chen', 18)}}的其他基金

ATD: Sparse and Localized Graph Convolutional Networks for Anomaly Detection and Active Learning
ATD:用于异常检测和主动学习的稀疏和局部图卷积网络
  • 批准号:
    2220574
  • 财政年份:
    2023
  • 资助金额:
    $ 40万
  • 项目类别:
    Standard Grant
Collaborative Research: SHF: Medium: Hardware and Software Support for Memory-Centric Computing Systems
协作研究:SHF:中:以内存为中心的计算系统的硬件和软件支持
  • 批准号:
    2312509
  • 财政年份:
    2023
  • 资助金额:
    $ 40万
  • 项目类别:
    Continuing Grant
FAI: A novel paradigm for fairness-aware deep learning models on data streams
FAI:数据流上具有公平意识的深度学习模型的新颖范式
  • 批准号:
    2147375
  • 财政年份:
    2022
  • 资助金额:
    $ 40万
  • 项目类别:
    Standard Grant
III: Medium: Collaborative Research: MUDL: Multidimensional Uncertainty-Aware Deep Learning Framework
III:媒介:协作研究:MUDL:多维不确定性感知深度学习框架
  • 批准号:
    2107449
  • 财政年份:
    2021
  • 资助金额:
    $ 40万
  • 项目类别:
    Continuing Grant
III: Small: Collaborative Research: A novel paradigm for detecting complex anomalous patterns in multi-modal, heterogeneous, and high-dimensional multi-source data sets
III:小型:协作研究:一种检测多模态、异构和高维多源数据集中复杂异常模式的新范式
  • 批准号:
    1954409
  • 财政年份:
    2019
  • 资助金额:
    $ 40万
  • 项目类别:
    Standard Grant
CAREER: SPARK: A Theoretical Framework for Discovering Complex Patterns in Big Attributed Networks
职业:SPARK:发现大属性网络中复杂模式的理论框架
  • 批准号:
    1954376
  • 财政年份:
    2019
  • 资助金额:
    $ 40万
  • 项目类别:
    Continuing Grant
SHF: Small: Redesigning the System Architecture for Ultra-High Density Data Storage
SHF:小型:重新设计超高密度数据存储的系统架构
  • 批准号:
    1910958
  • 财政年份:
    2019
  • 资助金额:
    $ 40万
  • 项目类别:
    Standard Grant
CAREER: SPARK: A Theoretical Framework for Discovering Complex Patterns in Big Attributed Networks
职业:SPARK:发现大属性网络中复杂模式的理论框架
  • 批准号:
    1750911
  • 财政年份:
    2018
  • 资助金额:
    $ 40万
  • 项目类别:
    Continuing Grant
III: Small: Collaborative Research: A novel paradigm for detecting complex anomalous patterns in multi-modal, heterogeneous, and high-dimensional multi-source data sets
III:小型:协作研究:一种检测多模态、异构和高维多源数据集中复杂异常模式的新范式
  • 批准号:
    1815696
  • 财政年份:
    2018
  • 资助金额:
    $ 40万
  • 项目类别:
    Standard Grant
XPS: FULL: Collaborative Research: Maximizing the Performance Potential and Reliability of Flash-based Solid State Devices for Future Storage Systems
XPS:完整:协作研究:最大限度地提高未来存储系统基于闪存的固态设备的性能潜力和可靠性
  • 批准号:
    1629291
  • 财政年份:
    2016
  • 资助金额:
    $ 40万
  • 项目类别:
    Standard Grant

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合作研究:SHF:媒介:可微分硬件合成
  • 批准号:
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