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) 为克服一些社会最大的挑战做出了深远的贡献，包括气候建模、粮食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。这些系统通过偏微分方程 (PDE) 进行建模，这些方程通常具有分散的时间尺度和物理基础。因此，没有任何一种时间积分方法能够有效地处理所有这些问题。为了解决这个困难，我们建议使用高阶算子分割策略并结合优化时间积分方法（例如龙格-库塔方法）的设计。 HPC 编程主要基于消息传递接口 (MPI) 库。然而，从那时起，计算架构和软件需求已经发生了很大的变化，而 MPI 基本上保持不变。为了充分发挥百亿亿级计算的优势，该研究计划还建议通过并发编程的新颖应用，将 HPC 编程推进到 MPI 模型之外，从而允许追求许多新的、令人兴奋的研究方向，否则这些方向将变得棘手。此类计算的范围要求软件具有容错能力。尽管孤立的故障很少见，但所需组件的数量庞大意味着计算过程中发生故障的可能性是不可忽略的。与 MPI 相比，并发编程范例（例如参与者）提供内置的容错能力。参与者还可以通过在冗余计算之间创建最小的物理隔离或启动新进程来替换故障进程来优雅地处理故障，从而无需昂贵地分配可能浪费的计算资源。此外，要充分利用分布式 HPC 环境中的潜在并发性，需要数据并行性和功能并行性。 MPI 可以很好地处理数据并行性。然而，功能并行性的管理在编程和计算效率方面通常存在很大问题。在这种情况下，参与者的运行时环境可以管理参与者的创建、行为和迁移，以熟练地处理功能并行性，从而实现自然且低成本的编程和通信。拟议的研究将能够在涉及偏微分方程时间积分的应用中更有效地利用 HPC 资源。这项研究还将产生重要的开源软件。我处于独特且优越的地位，可以通过应用现代并发编程范式来领导百亿亿次科学计算发展的基础研究，并在 HPC 的后 MPI 范式中培训下一代计算科学家。