The increasingly diverse ecosystem of high-performance architectures and programming models presents a mounting challenge for programmers seeking to accelerate scientific computing applications. Code generation offers a promising solution, transforming a simple and general representation of computations into lower level, hardware-specialized and -optimized code. We describe the philosophy set forth by the Python package Pystella, a high-performance framework built upon such tools to solve partial differential equations on structured grids. A hierarchy of abstractions provides increasingly expressive interfaces for specifying physical systems and algorithms. We present an example application from cosmology, using finite-difference methods to solve coupled hyperbolic partial differential equations on (multiple) GPUs. Such an approach may enable a broad range of domain scientists to make efficient use of increasingly heterogeneous computational resources while mitigating the drastic effort and expertise nominally required to do so.

中文翻译：

---

GPU 上 PDE 的模板求解器：来自宇宙学的一个例子

日益多样化的高性能架构和编程模型生态系统对寻求加速科学计算应用程序的程序员提出了越来越大的挑战。代码生成提供了一种很有前景的解决方案，它将计算的简单通用表示转换为较低级别的、硬件专用和优化的代码。我们描述了 Python 包 Pystella 提出的理念，Pystella 是一个基于此类工具构建的高性能框架，用于求解结构化网格上的偏微分方程。抽象的层次结构为指定物理系统和算法提供了越来越具有表现力的接口。我们展示了一个来自宇宙学的示例应用，使用有限差分方法在（多个）GPU 上求解耦合双曲偏微分方程。