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CCRI: Medium: Rogues Gallery: A Community Research Infrastructure for Post-Moore Computing

CCRI: Medium: Rogues Gallery: A Community Research Infrastructure for Post-Moore Computing
CCRI:媒介:Rogues Gallery:后摩尔计算的社区研究基础设施
批准号:
2016701
负责人:
Jeffrey Young
金额:
$135.17万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-09-01 至 2024-08-31

项目摘要

项目成果

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中文摘要
翻译
即将到来的后摩尔计算时代意味着在不久的将来,戈登摩尔定律的简单和成本效益的晶体管缩放将结束,计算机将不再是更快,只是因为它们可以包含更多,更小的晶体管。未来的计算机需要专门用于某些任务,并进行不同的设计,以实现推动现代经济的性能持续增长。这些专用计算机架构的一些可能的例子包括独特或“流氓”设计,如神经形态或大脑启发的计算机,在量子尺度上运行的计算机和可逆计算机。 这个项目提供了第一个公开的测试平台,用于调查这些新的后摩尔计算机设计,或“流氓”,作为流氓画廊测试平台的一部分。Rogues Gallery提供了一个独特的研究基础设施,包括神经形态、可重构和近记忆平台,以及包括5G在内的新型网络功能。此外,测试平台还辅以培训资源、软件和工具,以及协作社区,以帮助研究人员和开发人员以最佳方式将这些新颖的计算机设计用于日常应用以及具有挑战性的研究问题。这一试验平台的预期成果包括更好地了解后摩尔系统的专用计算机设计,以及提高下一代美国研究人员如何使用这些关键技术的能力和培训。 该项目开发了下一代以社区为中心的硬件和软件测试平台以及相关的硬件设计设施,以评估后摩尔计算的可能架构方向。这些计算机架构和系统与今天的标准处理器模型有很大的不同,在许多情况下可以被认为是未来计算的“流氓”方法。该基础设施或Rogues Gallery将提供一个由格鲁吉亚Tech托管的社区可访问硬件测试平台,该平台将支持对软件工具和编程接口、大型数据集的数据管理技术以及后摩尔硬件的算法策略进行相关研究。这种协作的社区测试平台代表了一种面向未来的投资,目前任何现有的学术或行业资源都无法满足这种投资。IEEE的Rebooting Computing工作已经证明了社区需要一个用于近存储器,神经形态和下一代网络设备的测试平台;热力学,神经形态和量子计算等领域的会议和场地的快速增长;以及研究人员对将深度学习和数据分析等应用映射到这些架构的兴趣越来越大。 为了进一步开发这个测试平台作为社区资源,研究人员正在努力获得独特的硬件(rogues),并正在构建一个通用的基础设施来托管这些硬件和未来的原型,同时还为后摩尔硬件和软件研究开发教程和培训,访问管理,集成和测量技术。Rogues Gallery基础设施的部署和成功将创建一个新的测试平台和相关的研究社区,该社区专注于现有云、学术、国家实验室或NSF资源无法提供的未来硬件原型。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。
英文摘要
The forthcoming post-Moore computing era speaks to a time in the near future when Gordon Moore’s Law of easy and cost-effective transistor scaling will end, and computers will no longer be faster just because they can incorporate more, smaller transistors. Future computers need to be specialized for certain tasks and designed differently to achieve the consistent gains in performance that drive our modern economy. Some possible examples of these specialized computer architectures include unique or “rogue” designs like neuromorphic or brain-inspired computers, computers that operate at a quantum scale, and reversible computers. This project provides the first publicly available testbed for investigating these novel post-Moore computer designs, or “rogues,” as part of the Rogues Gallery testbed. The Rogues Gallery provides a unique research infrastructure with neuromorphic, reconfigurable, and near-memory platforms as well as capabilities for novel networking, including 5G. Additionally, the testbed is supplemented by training resources, software and tools, and a collaborative community to assist researchers and developers in the best ways to use these novel computer designs for everyday applications as well as challenging research problems. The expected outcomes of this testbed effort include a better understanding of specialized computer designs for post-Moore systems as well as improved capabilities and training for the next generation of US researchers in how to use these critical technologies. This project develops a next-generation, community-focused hardware and software testbed and associated hardware design facility to evaluate possible architecture directions for post-Moore computing. These computer architectures and systems differ significantly from today’s standard processor models and in many cases can be considered “rogue” approaches to the future of computing. The infrastructure, or the Rogues Gallery, will provide a community-accessible hardware testbed hosted by Georgia Tech that will enable related research into software tools and programming interfaces, data management techniques for large data sets, and algorithmic strategies for post-Moore hardware. This collaborative, community testbed represents a future-looking investment that is not currently satisfied by any existing academic or industry resources. The community need for a testbed for near-memory, neuromorphic, and next-generation networking devices has already been demonstrated by IEEE’s Rebooting Computing efforts; the rapid growth of conferences and venues for areas like thermodynamic, neuromorphic, and quantum computing; and a growing interest in researchers looking to map applications like deep learning and data analytics to these architectures. To further develop this testbed as a community resource, the investigators are working to acquire unique hardware (rogues) and are building a common infrastructure to host this hardware and future prototypes, while also developing tutorials and training, access management, integration, and measurement techniques for post-Moore hardware and software research. The deployment and success of the Rogues Gallery infrastructure will create a new testbed and associated research community that is focused on future hardware prototypes that are not served by existing cloud, academic, national lab, or NSF resources.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.
期刊论文(9)
专著(0)
科研奖励(0)
会议论文
Coeus: Clustering (A)like Patterns for Practical Machine Intelligent Hybrid Memory Management
Coeus:实用机器智能混合内存管理的类集群 (A) 模式
DOI: 10.1109/ccgrid54584.2022.00071
发表时间: 2022
期刊: IEEE
影响因子: --
作者: [Doudali, Thaleia Dimitra, Gavrilovska, Ada]
通讯作者: Gavrilovska, Ada
Onboarding Users to A64FX via Open OnDemand
通过 Open OnDemand 将用户引入 A64FX
DOI: 10.1145/3503470.3503479
发表时间: 2022
期刊: IWAHPCE22; HPCAsia 2022 Workshop: International Conference on High Performance Computing in Asia-Pacific Region Workshops
影响因子: --
作者: [Jezghani, Aaron, Manalo, Kevin, Powell, Will, Valdez, Jeffrey, Young, Jeffrey]
通讯作者: Young, Jeffrey
Cori: Dancing to the Right Beat of Periodic Data Movements over Hybrid Memory Systems
Cori:在混合内存系统上随着周期性数据移动的正确节奏起舞
DOI: 10.1109/ipdps49936.2021.00043
发表时间: 2021
期刊: 2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS
影响因子: --
作者: [Doudali, Thaleia Dimitra, Zahka, Daniel, Gavrilovska, Ada]
通讯作者: Gavrilovska, Ada
The Case for Optimizing the Frequency of Periodic Data Movements over Hybrid Memory Systems
优化混合内存系统周期性数据移动频率的案例
DOI: 10.1145/3422575.3422788
发表时间: 2020
期刊: MEMSYS 2020
影响因子: --
作者: [Doudali, Thaleia Dimitra, Zahka, Daniel, Gavrilovska, Ada]
通讯作者: Gavrilovska, Ada
9
    CDS&E: SuperSTARLU - STacked, AcceleRated Algorithms for Sparse Linear Systems
    • 批准号:
      1710371
    • 项目类别:
      Standard Grant
    • 资助金额:
      $50.0万
    • 财政年份:
      2017
    • 负责人:
      Jeffrey Young
    • 依托单位:
    海外基金