SPX: Collaborative Research: Intelligent Communication Fabrics to Facilitate Extreme Scale Computing

SPX:协作研究:促进超大规模计算的智能通信结构

基本信息

  • 批准号:
    1919130
  • 负责人:
  • 金额:
    $ 44.05万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2019
  • 资助国家:
    美国
  • 起止时间:
    2019-10-01 至 2024-09-30
  • 项目状态:
    已结题

项目摘要

Advances in High-Performance Computing over the last several decades have enabled various important applications throughout science and engineering. Semiconductor technology increasingly faces fundamental physical limits. New approaches to hardware/software co-design are now achieving higher performance at extreme scales. This project will explore two new approaches: configurability and integration. Configurability enables hardware to map better to applications. Integration enables system components that have generally been single function to gain additional functionality. An example is a network for the transport of data. Such a network might also operate on that data as it is being transported. Integration enables compute everywhere in the architecture and network. Configurability and integration will lead to much more efficient use of computing resources. Such resources include high-performance computers. Another focus of this project will be in the use of Field Programmable Gate Arrays (FPGAs). FPGAs are a widely used type of integrated circuit. In FPGAs the hardware can be configured to match the application. FPGAs will allow the deployment of the research products into production systems. They will also allow evaluation of changes in system design. This work will advance extreme-scale computing applications. Such applications include medicine and new drug discovery. This project will also include training of graduate and undergraduate students. It will focus on students belonging to groups underrepresented in STEM disciplinesThe central theme of this project is that emerging centrality of hardware configurability and integration means that at least two old systems abstractions must be broken. First, this project will demonstrate that computation and communication should no longer comprise separate silos. Second, it will demonstrate that applications should no longer be mapped to fixed hardware. In their place, new hardware-software abstractions must necessarily be built around the idea of the application-centric system. This project will identify new computing modes -- computation distribution/offload and configurable hardware -- that will be application aware with tight mapping of applications to computer systems. This improved mapping, in turn, will require deploying a number of mechanisms, starting with improved compilers and high-quality application libraries but extending to lighter-weight yet intelligent middleware (for instance, improved Message Passing Interface middleware), automated application modification, dynamic autotuning, and machine learning assisting in all of these components. The project will consider all of these concerns in due course.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.
在过去的几十年里,高性能计算的进步使科学和工程中的各种重要应用成为可能。半导体技术日益面临基本的物理限制。硬件/软件协同设计的新方法现在在极端规模下实现了更高的性能。这个项目将探索两种新的方法:可配置性和集成。可配置性使硬件能够更好地映射到应用程序。集成使通常具有单一功能的系统组件能够获得额外的功能。一个例子是用于数据传输的网络。这样的网络还可以在传输数据时对数据进行操作。集成使计算在体系结构和网络中无处不在。可配置性和集成将导致更有效地使用计算资源。这些资源包括高性能计算机。该项目的另一个重点将是现场可编程门阵列(fpga)的使用。fpga是一种应用广泛的集成电路。在fpga中,硬件可以配置为与应用相匹配。fpga将允许将研究产品部署到生产系统中。它们还将允许对系统设计中的变化进行评估。这项工作将推进极端规模计算的应用。这些应用包括医药和新药发现。该项目还将包括研究生和本科生的培训。该项目的中心主题是硬件可配置性和集成的中心地位,这意味着必须打破至少两个旧的系统抽象。首先,这个项目将证明计算和通信不应再由各自的竖井组成。其次,它将演示应用程序不应该再映射到固定的硬件。在它们的位置上,必须围绕以应用程序为中心的系统的思想构建新的软硬件抽象。该项目将确定新的计算模式——计算分配/卸载和可配置硬件——这将是应用程序感知的,应用程序与计算机系统的紧密映射。这种改进的映射反过来又需要部署许多机制,从改进的编译器和高质量的应用程序库开始,扩展到轻量级的智能中间件(例如,改进的消息传递接口中间件)、自动应用程序修改、动态自动调优,以及在所有这些组件中辅助的机器学习。该项目将在适当的时候考虑所有这些问题。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。

项目成果

期刊论文数量(34)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
FCsN: A FPGA-Centric SmartNIC Framework for Neural Networks
FCsN:以 FPGA 为中心的神经网络 SmartNIC 框架
FLASH: FPGA-Accelerated Smart Switches with GCN Case Study
FLASH:采用 GCN 的 FPGA 加速智能开关案例研究
  • DOI:
    10.1145/3577193.3593739
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Haghi, Pouya;Krska, William;Tan, Cheng;Geng, Tong;Chen, Po Hao;Greenwood, Connor;Guo, Anqi;Hines, Thomas;Wu, Chunshu;Li, Ang
  • 通讯作者:
    Li, Ang
FPDeep: Scalable Acceleration of CNN Training on Deeply-Pipelined FPGA Clusters
  • DOI:
    10.1109/tc.2020.3000118
  • 发表时间:
    2020-05
  • 期刊:
  • 影响因子:
    3.7
  • 作者:
    Tianqi Wang;Tong Geng;Ang Li;Xi Jin;Martin C. Herbordt
  • 通讯作者:
    Tianqi Wang;Tong Geng;Ang Li;Xi Jin;Martin C. Herbordt
COPA Use Case: Distributed Secure Joint Computation
COPA 用例:分布式安全联合计算
The Future of FPGA Acceleration in Datacenters and the Cloud
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Martin Herbordt其他文献

AutoAnnotate: Reinforcement Learning based Code Annotation for High Level Synthesis
AutoAnnotate:基于强化学习的代码注释,用于高级综合

Martin Herbordt的其他文献

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{{ truncateString('Martin Herbordt', 18)}}的其他基金

Collaborative Research: EAGER: Real-time Strategies and Synchronized Time Distribution Mechanisms for Enhanced Exascale Performance-Portability and Predictability
合作研究:EAGER:实时策略和同步时间分配机制,以增强百亿亿次性能-可移植性和可预测性
  • 批准号:
    2151021
  • 财政年份:
    2022
  • 资助金额:
    $ 44.05万
  • 项目类别:
    Standard Grant
SHF: Small: Collaborative Research: Coupling Computation and Communication in FPGA-Enhanced Clouds and Clusters
SHF:小型:协作研究:FPGA 增强型云和集群中的耦合计算和通信
  • 批准号:
    1618303
  • 财政年份:
    2016
  • 资助金额:
    $ 44.05万
  • 项目类别:
    Standard Grant
II-EN: Collaborative Research: Large-Scale FPGA-Centric Cluster with Direct and Programmable Communication
II-EN:协作研究:具有直接可编程通信功能的以 FPGA 为中心的大规模集群
  • 批准号:
    1405695
  • 财政年份:
    2014
  • 资助金额:
    $ 44.05万
  • 项目类别:
    Standard Grant
CI-P: Collaborative Research: Large-Scale FPGA-Centric Computing with Molecular Dynamics
CI-P:协作研究:以 FPGA 为中心的大规模分子动力学计算
  • 批准号:
    1205593
  • 财政年份:
    2012
  • 资助金额:
    $ 44.05万
  • 项目类别:
    Standard Grant
CAREER: Integrating Architecture-Level Simulation with Industrial CAD Tools for Prototyping High-Performance Coprocessors
职业:将架构级仿真与工业 CAD 工具集成以制作高性能协处理器原型
  • 批准号:
    0228094
  • 财政年份:
    2002
  • 资助金额:
    $ 44.05万
  • 项目类别:
    Continuing Grant
CAREER: Integrating Architecture-Level Simulation with Industrial CAD Tools for Prototyping High-Performance Coprocessors
职业:将架构级仿真与工业 CAD 工具集成以制作高性能协处理器原型
  • 批准号:
    9702483
  • 财政年份:
    1997
  • 资助金额:
    $ 44.05万
  • 项目类别:
    Continuing Grant

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SPX: Collaborative Research: Automated Synthesis of Extreme-Scale Computing Systems Using Non-Volatile Memory
SPX:协作研究:使用非易失性存储器自动合成超大规模计算系统
  • 批准号:
    2408925
  • 财政年份:
    2023
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    $ 44.05万
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SPX: Collaborative Research: Scalable Neural Network Paradigms to Address Variability in Emerging Device based Platforms for Large Scale Neuromorphic Computing
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  • 批准号:
    2401544
  • 财政年份:
    2023
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    $ 44.05万
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SPX: Collaborative Research: Intelligent Communication Fabrics to Facilitate Extreme Scale Computing
SPX:协作研究:促进超大规模计算的智能通信结构
  • 批准号:
    2412182
  • 财政年份:
    2023
  • 资助金额:
    $ 44.05万
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SPX: Collaborative Research: Cross-stack Memory Optimizations for Boosting I/O Performance of Deep Learning HPC Applications
SPX:协作研究:用于提升深度学习 HPC 应用程序 I/O 性能的跨堆栈内存优化
  • 批准号:
    2318628
  • 财政年份:
    2022
  • 资助金额:
    $ 44.05万
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SPX: Collaborative Research: FASTLEAP: FPGA based compact Deep Learning Platform
SPX:协作研究:FASTLEAP:基于 FPGA 的紧凑型深度学习平台
  • 批准号:
    2333009
  • 财政年份:
    2022
  • 资助金额:
    $ 44.05万
  • 项目类别:
    Standard Grant
SPX: Collaborative Research: NG4S: A Next-generation Geo-distributed Scalable Stateful Stream Processing System
SPX:合作研究:NG4S:下一代地理分布式可扩展状态流处理系统
  • 批准号:
    2202859
  • 财政年份:
    2022
  • 资助金额:
    $ 44.05万
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SPX: Collaborative Research: Memory Fabric: Data Management for Large-scale Hybrid Memory Systems
SPX:协作研究:内存结构:大规模混合内存系统的数据管理
  • 批准号:
    2132049
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    $ 44.05万
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SPX: Collaborative Research: Automated Synthesis of Extreme-Scale Computing Systems Using Non-Volatile Memory
SPX:协作研究:使用非易失性存储器自动合成超大规模计算系统
  • 批准号:
    2113307
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    2020
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    $ 44.05万
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    Standard Grant
SPX: Collaborative Research: FASTLEAP: FPGA based compact Deep Learning Platform
SPX:协作研究:FASTLEAP:基于 FPGA 的紧凑型深度学习平台
  • 批准号:
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SPX: Collaborative Research: Intelligent Communication Fabrics to Facilitate Extreme Scale Computing
SPX:协作研究:促进超大规模计算的智能通信结构
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