2019-09-05, 16:45–17:00, Track 2 (Baroja)
PSYDAC takes input from SymPDE (a SymPy extension for partial differential equations), applies a finite-element discretization, generates MPI-parallel code, and accelerates it with Numba, Pythran, or Pyccel. We present design, usage and performance.
PSYDAC is a Python 3 library for the solution of partial differential equations. Its current focus is on isogeometric analysis using B-spline finite elements, but extensions to other methodologies are under consideration. In order to use PSYDAC, the user defines geometry and model equations in an abstract form using SymPDE, an extension of Sympy that provides the mathematical expressions and checks their semantic validity. Once a finite element discretization has been chosen, PSYDAC maps the abstract concepts into concrete objects, the basic building blocks being MPI-distributed vectors and matrices. Python code is generated for all the computationally intensive operations (matrix and vector assembly, matrix-vector products, etc.), and it is accelerated using either Numba, Pythran, or Pyccel. We present the library design, the user interface, and the performance results.