Scalable paradigms and software for exascale scientific computing

在过去的三十年里，高性能计算(HPC)为克服社会上一些最大的挑战做出了深远的贡献，包括气候建模、食品和水安全以及基因测序。HPC应用是加拿大已确定的创新专长领域的核心，包括对水文流动、心肌组织中的电活动、流态化床和等离子体的大规模模拟。这些系统由通常具有离散时间尺度和物理基础的偏微分方程组(PDE)来建模。因此，没有一种单一的时间积分方法能够有效地处理所有这些问题。为了解决这一困难，我们提出使用高阶算子分裂策略，并设计优化的时间积分方法，如Runge-Kutta方法。 HPC编程在很大程度上基于消息传递接口(MPI)库。然而，从那时起，计算体系结构和软件需求发生了很大的变化，而MPI基本上保持不变。为了获得艾级计算的全部好处，该研究计划还建议通过并发编程的新应用来推动HPC编程超越MPI模型，从而允许追求许多否则仍然难以解决的新的和令人兴奋的研究方向。 这种计算的范围要求软件具有容错能力。虽然孤立故障很少，但所需组件的绝对数量意味着在计算过程中发生故障的可能性是不可忽略的。与MPI相反，并发编程范例(如参与者)提供了内置的容错能力。参与者还可以通过在冗余计算之间创建最小的物理间隔或通过启动新进程来替换错误计算来优雅地处理故障，从而消除了对潜在浪费的计算资源进行代价高昂的分配的需要。 此外，为了充分利用分布式HPC环境中的潜在并发性，数据并行和函数并行都是必需的。MPI能够很好地处理数据并行性。然而，功能并行性的管理通常在编程和计算效率方面都是非常有问题的。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，从而实现自然和低成本的编程和通信。 拟议的研究将使高性能计算资源在涉及PDE时间整合的应用中得到更有效的利用。这项研究还将产生重要的软件，这些软件将成为开源软件。我处于独特和特权的地位，可以领导通过应用现代并发编程范式推进亿级科学计算的基础研究，以及培训下一代计算科学家的后MPI范式的HPC。

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.