Scalable paradigms and software for exascale scientific computing

在过去的三十年里，高性能计算（HPC）为克服一些社会最大的挑战做出了巨大贡献，包括气候建模，粮食和水安全以及基因测序。HPC应用是加拿大已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。这些系统是由偏微分方程（PDE），通常具有分散的时间尺度和物理基础建模。因此，没有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，我们提出了使用高阶算子分裂策略结合优化的时间积分方法，如龙格库塔法的设计。 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大多保持静态。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程序来推进HPC编程超越MPI模型，从而实现许多新的和令人兴奋的研究方向，否则这些研究方向仍然难以解决。 这种计算的范围要求软件是容错的。虽然孤立的故障是罕见的，所需的组件的绝对数量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，并发编程范例（如actor）提供了内置的容错能力。Actor还可以通过在冗余计算之间创建最小的物理隔离或通过启动新进程来替换故障进程来优雅地处理故障，从而消除对潜在浪费的计算资源的昂贵分配的需要。 此外，数据并行和功能并行都需要充分利用分布式HPC环境中的潜在并发性。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是非常成问题的。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。 拟议的研究将使更有效地利用HPC资源的应用程序，涉及时间集成的PDE。这项研究还将产生重要的软件，这些软件将成为开源软件。我在一个独特的和特权的位置，通过现代并发编程范式的应用，以及培养下一代的计算科学家在HPC的后MPI范式的exascale科学计算的进步领导基础研究。

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.