Creating scalable, high performance PDE-based simulations requires a suitable combination of discretizations, differential operators, preconditioners and solvers. The required combination changes with the application and with the available hardware, yet software development time is a severely limited resource for most scientists and engineers. Here we demonstrate that generating simulation code from a high-level Python interface provides an effective mechanism for creating high performance simulations from very few lines of user code. We demonstrate that moving from one supercomputer to another can require significant algorithmic changes to achieve scalable performance, but that the code generation approach enables these algorithmic changes to be achieved with minimal development effort.

中文翻译：

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在Firedrake中进行生产性便携式可扩展有限元模拟的代码生成

创建可扩展的，基于PDE的高性能仿真需要离散化，微分算子，预处理器和求解器的适当组合。所需的组合随应用程序和可用硬件的变化而变化，但是对于大多数科学家和工程师而言，软件开发时间是极为有限的资源。在这里，我们演示了从高级Python界面生成仿真代码提供了一种有效的机制，可以从很少的用户代码行创建高性能仿真。我们证明了从一台超级计算机转移到另一台超级计算机可能需要重大的算法更改才能实现可扩展的性能，但是代码生成方法使这些算法更改可以以最少的开发工作量来实现。