부산대학교 도서관

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.