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
• 负责人: Spiteri, Raymond
• 金额: $ 2.99万
• 依托单位: University of Saskatchewan
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749917
• 关键词: Scalable; paradigms; software; exascale; scientific

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.

在过去的三十年中，高性能计算（HPC）有助于克服一些社会最大的挑战，包括气候建模，食品和水安全以及基因测序。 HPC应用是加拿大确定的创新专业领域的核心，包括对水文流，心肌组织中的电活动，流化床和等离子体的大规模模拟。这些系统是通过偏微分方程（PDE）建模的，这些方程（PDE）通常会散布时间尺度和物理基础。因此，没有单一的时间整合方法能够有效地处理所有时间。为了解决这一困难，我们建议使用高阶操作员分解策略，并结合设计优化的时间整合方法（例如Runge-Kutta方法）的设计。 HPC编程主要基于消息传递接口（MPI）库。但是，从那以后，计算体系结构和软件需求就已经发生了很大的发展，而MPI大多仍然是静态的。为了获得Exascale计算的全部好处，该研究计划还建议通过并发编程的新应用将HPC编程超越MPI模型，从而允许追求许多新的和令人兴奋的研究方向，否则这些方向将保持不足。此类计算的范围要求软件具有容错性。尽管孤立的断层很少见，但所需的组件数量却意味着在计算过程中发生故障的机会是不可忽略的。与MPI相反，类似参与者等并发编程范例可提供内置的容错性。参与者还可以通过在冗余计算之间创建最小的物理分离或启动新的过程来替换有缺陷的过程，从而优雅地处理故障，从而消除了对潜在浪费的计算资源的昂贵分配的需求。此外，需要数据并行性和功能并行性，以完全利用分布式HPC环境中的潜在并发。 MPI可以很好地处理数据并行性。然而，在编程和计算效率方面，功能并行性的管理通常都是高度问题的。在这种情况下，参与者的运行时间环境可以管理参与者的创建，行为和迁移，以熟练处理功能并行性，从而导致自然和低成本的编程和交流。拟议的研究将使HPC资源在涉及PDE的时间集成的应用中更有效地利用HPC资源。这项研究还将生产出重要的软件，以开源。我处于一个独特而特权的立场，通过应用现代并发计划范式以及培训HPC的MPI后范式中的下一代计算科学家，领导基本研究的发展。