High Performance Computing for Learning

用于学习的高性能计算

基本信息

  • 批准号:
    9217041
  • 负责人:
  • 金额:
    $ 303.05万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing grant
  • 财政年份:
    1992
  • 资助国家:
    美国
  • 起止时间:
    1992-10-01 至 1999-09-30
  • 项目状态:
    已结题

项目摘要

The Grand Challenge Application Groups competition provides one mechanism for the support of multidisciplinary teams of scientists and engineers to meet the goals of the High Performance Computing and Communications (HPCC) Initiative in Fiscal Year 1992. The ideal proposal provided not only excellence in science: focussed problem with potential for substantial impact in a critical area of science and engineering) but also significant interactions between scientific and computational activities, usually involving mathematical, computer or computational scientists, that would have impact in high-performance computational activity beyond the specific scientific or engineering problem area(s) or discipline being studied. In the award to Berwick, Bizzi, Bulthoff, Jordan, Wexler, Poggio, Rivest, Winston, and Yang at MIT, the research project - High Performance Computing for Learning - has been designed explicitly to push the High Performance Computing algorithmic and architectural envelope via a CM-5 and VLSI testbed and to address many of the HPCC goals. It will advance new algorithms and software for a broad class of optimization and learning problems, tested on and directly driving operating system and architectural changes on the CM-5 (working with one of the CM-5's key architects). The learning problems addressed are essentially an entire class of modeling/optimization problems that intersect with nearly all HPCC Grand Challenge Problems.
大挑战应用组竞赛为科学家和工程师组成的多学科团队提供了一种支持机制,以实现1992财年高性能计算和通信(HPCC)倡议的目标。理想的建议不仅在科学方面提供了卓越的成果:可能在科学和工程的关键领域产生重大影响的集中问题),而且还提供了科学活动和计算活动之间的重大互动,通常涉及数学、计算机或计算科学家,这将对超出特定科学或工程问题领域(S)或正在研究的学科的高性能计算活动产生影响。在授予麻省理工学院的Berwick、Bizzi、Bulthoff、Jordan、Wexler、Poggio、Rivest、Winston和Yang的奖项中,研究项目--用于学习的高性能计算--被明确地设计为通过CM-5和VLSI试验台来推动高性能计算算法和体系结构的框架,并解决许多HPCC目标。它将为一大类优化和学习问题提供新的算法和软件,在CM-5上测试并直接推动操作系统和架构变化(与CM-5的S关键架构师之一合作)。所解决的学习问题本质上是与几乎所有HPCC Grand Challenges问题相交的一类建模/优化问题。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Robert Berwick其他文献

The combinatorics of Merge and Workspace right-sizing
合并和工作区大小调整的组合
  • DOI:
  • 发表时间:
    2019
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Sandiway Fong;Robert Berwick;Jason Ginsburg
  • 通讯作者:
    Jason Ginsburg
Minimalist Parsing
极简解析
  • DOI:
  • 发表时间:
    2019
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Robert Berwick;Edward P. Stabler
  • 通讯作者:
    Edward P. Stabler

Robert Berwick的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Robert Berwick', 18)}}的其他基金

Workshop on Rich Grammars From Poor Inputs
从不良输入中获取丰富语法的研讨会
  • 批准号:
    0951620
  • 财政年份:
    2009
  • 资助金额:
    $ 303.05万
  • 项目类别:
    Standard Grant
ITR: Bayesian Learning at the Syntax-Semantics Interface
ITR:句法语义接口的贝叶斯学习
  • 批准号:
    0218852
  • 财政年份:
    2002
  • 资助金额:
    $ 303.05万
  • 项目类别:
    Continuing Grant
Workshop on the Relationship between Computation and Linguistics
计算与语言学关系研讨会
  • 批准号:
    9312835
  • 财政年份:
    1993
  • 资助金额:
    $ 303.05万
  • 项目类别:
    Standard Grant
Presidential Young Investigator Award (Computer and Information Science)
总统青年研究员奖(计算机与信息科学)
  • 批准号:
    8552543
  • 财政年份:
    1986
  • 资助金额:
    $ 303.05万
  • 项目类别:
    Continuing Grant
Learnability And Parsability (Information Science)
可学习性和可解析性(信息科学)
  • 批准号:
    8511531
  • 财政年份:
    1986
  • 资助金额:
    $ 303.05万
  • 项目类别:
    Continuing grant

相似海外基金

High Performance Computing and Machine Learning Infrastructure for Oregon Life Sciences
俄勒冈生命科学的高性能计算和机器学习基础设施
  • 批准号:
    10630777
  • 财政年份:
    2023
  • 资助金额:
    $ 303.05万
  • 项目类别:
High-Performance Machine Learning Computing Using Non-deterministic Superconducting Circuits
使用非确定性超导电路的高性能机器学习计算
  • 批准号:
    23H03365
  • 财政年份:
    2023
  • 资助金额:
    $ 303.05万
  • 项目类别:
    Grant-in-Aid for Scientific Research (B)
MRI:Acquisition of a Multi-Purpose High-Performance Computing Infrastructure for Machine Learning and Computational Research at Temple University
MRI:天普大学购买用于机器学习和计算研究的多用途高性能计算基础设施
  • 批准号:
    2216289
  • 财政年份:
    2022
  • 资助金额:
    $ 303.05万
  • 项目类别:
    Standard Grant
High-performance Computing for Scalable Graph Representation Learning
用于可扩展图表示学习的高性能计算
  • 批准号:
    546268-2020
  • 财政年份:
    2021
  • 资助金额:
    $ 303.05万
  • 项目类别:
    Postdoctoral Fellowships
MRI: Acquisition of Dolly Sods GPU Cluster for Accelerated High-Performance Computing and Applications in Machine Learning and Artificial Intelligence in West Virginia
MRI:收购 Dolly Sods GPU 集群,以加速西弗吉尼亚州机器学习和人工智能的高性能计算和应用
  • 批准号:
    2117575
  • 财政年份:
    2021
  • 资助金额:
    $ 303.05万
  • 项目类别:
    Standard Grant
Optimization of performance based on the deep learning and mathematical models for the cloud and edge computing
基于云和边缘计算的深度学习和数学模型的性能优化
  • 批准号:
    20K11799
  • 财政年份:
    2020
  • 资助金额:
    $ 303.05万
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
Collaborative Research: CNS Core: Medium: Exploiting Synergies Between Machine-Learning Algorithms and Hardware Heterogeneity for High-Performance and Reliable Manycore Computing
合作研究:CNS Core:Medium:利用机器学习算法和硬件异构性之间的协同作用实现高性能和可靠的众核计算
  • 批准号:
    1955353
  • 财政年份:
    2020
  • 资助金额:
    $ 303.05万
  • 项目类别:
    Continuing Grant
Collaborative Research: CNS Core: Medium: Exploiting Synergies Between Machine-Learning Algorithms and Hardware Heterogeneity for High-Performance and Reliable Manycore Computing
合作研究:CNS Core:Medium:利用机器学习算法和硬件异构性之间的协同作用实现高性能和可靠的众核计算
  • 批准号:
    1955196
  • 财政年份:
    2020
  • 资助金额:
    $ 303.05万
  • 项目类别:
    Continuing Grant
Multimodal Machine-Learning and High Performance Computing Strategies for Big MS Proteomics Data
MS 蛋白质组大数据的多模态机器学习和高性能计算策略
  • 批准号:
    10372290
  • 财政年份:
    2020
  • 资助金额:
    $ 303.05万
  • 项目类别:
Multimodal Machine-Learning and High Performance Computing Strategies for Big MS Proteomics Data
MS 蛋白质组大数据的多模态机器学习和高性能计算策略
  • 批准号:
    10163880
  • 财政年份:
    2020
  • 资助金额:
    $ 303.05万
  • 项目类别:
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了