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Efficient and Scalable Communication and System Software for Exascale Computing

Efficient and Scalable Communication and System Software for Exascale Computing
用于百亿亿次计算的高效且可扩展的通信和系统软件
批准号:
RGPIN-2016-05389
负责人:
Afsahi, Ahmad
金额:
$2.26万
依托单位:
依托单位国家:
加拿大
项目类别:
Discovery Grants Program - Individual
财政年份:
2017
资助国家:
加拿大
项目状态:
已结题
起止时间:
2017-01-01 至 2018-12-31

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中文摘要
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英文摘要
High-Performance Computing (HPC) is used to tackle computationally-intensive problems in fields as diverse as green energy and biofuels, cancer research and drug discovery, weather forecasting and climate change, seismic processing for oil and gas, genomics and bioinformatics, astrophysics, material science, automotive, defense, data mining and analytics, and financial computing. It is the key to many scientific discoveries and engineering innovations. However, the demand for more computational power is never ending. Far more computational power on much larger-scale machines is required to unravel the scientific mysteries.
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Efficient and Scalable Communication and System Software for Exascale Computing
  • 批准号:
    RGPIN-2016-05389
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $2.26万
  • 财政年份:
    2021
  • 负责人:
    Afsahi, Ahmad
  • 依托单位:
Efficient and Scalable Communication and System Software for Exascale Computing
  • 批准号:
    RGPIN-2016-05389
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $2.26万
  • 财政年份:
    2020
  • 负责人:
    Afsahi, Ahmad
  • 依托单位:
Efficient and Scalable Communication and System Software for Exascale Computing
  • 批准号:
    RGPIN-2016-05389
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $2.26万
  • 财政年份:
    2019
  • 负责人:
    Afsahi, Ahmad
  • 依托单位:
Efficient and Scalable Communication and System Software for Exascale Computing
  • 批准号:
    RGPIN-2016-05389
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $2.26万
  • 财政年份:
    2018
  • 负责人:
    Afsahi, Ahmad
  • 依托单位:
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis