pySDC - Prototyping spectral deferred corrections

TL;DR

pySDC is a comprehensive Python framework that facilitates prototyping and implementing spectral deferred correction methods and their parallel variants for solving complex collocation problems efficiently.

Contribution

This paper introduces pySDC, a versatile Python framework with diverse implementations, tutorials, and coupling capabilities, enhancing accessibility and performance in spectral deferred correction methods.

Findings

pySDC supports various SDC and PFASST implementations.

The framework includes tutorials and examples for new users.

Couplings with FEniCS and PETSc extend its capabilities.

Abstract

In this paper we present the Python framework pySDC for solving collocation problems with spectral deferred correction methods (SDC) and their time-parallel variant PFASST, the parallel full approximation scheme in space and time. pySDC features many implementations of SDC and PFASST, from simple implicit time-stepping to high-order implicit-explicit or multi-implicit splitting and multi-level spectral deferred corrections. It comes with many different, pre-implemented examples and has seven tutorials to help new users with their first steps. Time-parallelism is implemented either in an emulated way for debugging and prototyping as well as using MPI for benchmarking. The code is fully documented and tested using continuous integration, including most results of previous publications. Here, we describe the structure of the code by taking two different perspectives: the user's and the developer's perspective. While the first sheds light on the front-end, the examples and the tutorials, the second is used to describe the underlying implementation and the data structures. We show three different examples to highlight various aspects of the implementation, the capabilities and the usage of pySDC. Also, couplings to the FEniCS framework and PETSc, the latter including spatial parallelism with MPI, are described.