Scalable paradigms and software for exascale scientific computing

用于百亿亿次科学计算的可扩展范式和软件

基本信息

  • 批准号:
    RGPIN-2020-04467
  • 负责人:
  • 金额:
    $ 2.99万
  • 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:
    2020
  • 资助国家:
    加拿大
  • 起止时间:
    2020-01-01 至 2021-12-31
  • 项目状态:
    已结题

项目摘要

For the past three decades, high-performance computing (HPC) has profoundly contributed toward overcoming some of society's greatest challenges, including climate modelling, food and water security, and gene sequencing. HPC applications are core to Canada's identified sectors of innovation expertise and include large-scale simulations of hydrological flows, electrical activity in myocardial tissue, fluidized beds, and plasmas. These systems are modelled by partial differential equations (PDEs) that typically have disperate time scales and physical bases. Accordingly, no single time-integration method is able to effectively handle them all. To address this difficulty, we propose the use of high-order operator-splitting strategies combined with the design of optimized time-integration methods such as Runge-Kutta methods. HPC programming is largely based on the Message Passing Interface (MPI) library. Computing architectures and software requirements, however, have evolved considerably since then, whereas MPI has mostly remained static. In order to reap the full benefits of exascale computing, this research program also proposes to advance HPC programming beyond the MPI model through novel applications of concurrent programming, allowing many new and exciting research directions to be pursued that would otherwise remain intractable. The scope of such computations requires the software be fault tolerant. Although isolated faults are rare, the sheer number of components required means the chance of a fault occurring during a computation is non-negligible. In contrast to MPI, concurrent programming paradigms such as actors provide built-in fault tolerance. Actors can also elegantly handle faults by creating a minimum physical separation between redundant computations or by starting up new processes to replace faulty ones, eliminating the need for costly allocation of potentially wasted compute resources. Furthermore, both data parallelism and functional parallelism are required to fully exploit potential concurrency in a distributed HPC environment. MPI can handle data parallelism well. Management of functional parallelism, however, is generally highly problematic, both in terms of programming and computational efficiency. In this case, the run-time environments of actors can manage the creation, behaviour, and migration of actors to adeptly handle functional parallelism, leading to natural and low-cost programming and communication. The proposed research will enable more effective utilization of HPC resources in applications that involve the time integration of PDEs. This research will also produce significant software that will be made open source. I am in a unique and privileged position to lead fundamental research in the advancement of exascale scientific computing through the application of modern concurrency programming paradigms as well as to train the next generation of computational scientists in the post-MPI paradigm of HPC.
在过去的三十年里,高性能计算(HPC)为克服一些社会上最大的挑战做出了深刻的贡献,包括气候建模、食品和水安全以及基因测序。高性能计算应用是加拿大确定的创新专业领域的核心,包括水文流动、心肌组织电活动、流化床和等离子体的大规模模拟。这些系统由偏微分方程(PDEs)建模,通常具有分散的时间尺度和物理基础。因此,没有一个单一的时间积分方法能够有效地处理它们。为了解决这一困难,我们提出使用高阶算子分裂策略结合优化时间积分方法的设计,如龙格-库塔方法。

项目成果

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Spiteri, Raymond其他文献

Spiteri, Raymond的其他文献

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{{ truncateString('Spiteri, Raymond', 18)}}的其他基金

Scalable paradigms and software for exascale scientific computing
用于百亿亿次科学计算的可扩展范式和软件
  • 批准号:
    RGPIN-2020-04467
  • 财政年份:
    2022
  • 资助金额:
    $ 2.99万
  • 项目类别:
    Discovery Grants Program - Individual
Scalable paradigms and software for exascale scientific computing
用于百亿亿次科学计算的可扩展范式和软件
  • 批准号:
    RGPIN-2020-04467
  • 财政年份:
    2021
  • 资助金额:
    $ 2.99万
  • 项目类别:
    Discovery Grants Program - Individual
Game-Changing Time Integration of Complex Systems for the Exaflop Era
Exaflop 时代复杂系统的改变游戏规则的时间集成
  • 批准号:
    228090-2013
  • 财政年份:
    2019
  • 资助金额:
    $ 2.99万
  • 项目类别:
    Discovery Grants Program - Individual
Game-Changing Time Integration of Complex Systems for the Exaflop Era
Exaflop 时代复杂系统的改变游戏规则的时间集成
  • 批准号:
    228090-2013
  • 财政年份:
    2018
  • 资助金额:
    $ 2.99万
  • 项目类别:
    Discovery Grants Program - Individual
Using Big Data methods to improve fuel cell manufacturing
利用大数据方法改进燃料电池制造
  • 批准号:
    523106-2018
  • 财政年份:
    2018
  • 资助金额:
    $ 2.99万
  • 项目类别:
    Engage Grants Program
Mercedes Benz fuel cell process optimization
梅赛德斯奔驰燃料电池工艺优化
  • 批准号:
    518149-2017
  • 财政年份:
    2017
  • 资助金额:
    $ 2.99万
  • 项目类别:
    Connect Grants Level 1
Game-Changing Time Integration of Complex Systems for the Exaflop Era
Exaflop 时代复杂系统的改变游戏规则的时间集成
  • 批准号:
    228090-2013
  • 财政年份:
    2017
  • 资助金额:
    $ 2.99万
  • 项目类别:
    Discovery Grants Program - Individual
Game-Changing Time Integration of Complex Systems for the Exaflop Era
Exaflop 时代复杂系统的改变游戏规则的时间集成
  • 批准号:
    228090-2013
  • 财政年份:
    2015
  • 资助金额:
    $ 2.99万
  • 项目类别:
    Discovery Grants Program - Individual
Mathematical modelling and simulation of product-innovation diffusion
产品创新扩散的数学建模与仿真
  • 批准号:
    485461-2015
  • 财政年份:
    2015
  • 资助金额:
    $ 2.99万
  • 项目类别:
    Engage Grants Program
Predictive modelling and simulation of treatments for heart disease
心脏病治疗的预测建模和模拟
  • 批准号:
    491461-2015
  • 财政年份:
    2015
  • 资助金额:
    $ 2.99万
  • 项目类别:
    Engage Grants Program

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Scalable paradigms and software for exascale scientific computing
用于百亿亿次科学计算的可扩展范式和软件
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Scalable paradigms and software for exascale scientific computing
用于百亿亿次科学计算的可扩展范式和软件
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    RGPIN-2020-04467
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