Abstract
The aim with this thesis is to investigate how we can create unified
interfaces to some key software components that are needed when
solving partial differential equations. Two particular components are
addressed here: sparse matrices and visualization. We want the
interfaces to be simple to use, preferably with a Matlab-like
syntax. We also want the interfaces to be "thin" in the sense that the
interface code is small and provides access to core functionality
only, not all nice-to-have options that one can think of. The
interfaces are written in Python [29], a scripting language with a
simple, clean and easy-to-use syntax, great software development
flexibility, rapidly growing popularity, and rich libraries for both
numerical and administrative tasks. The idea is to use Python to write
the main algorithm for solving PDEs and thereby steer underlying
numerical software.
Chapter 1 presents a matrix library for storage, factorization, and
"solve" operations. The goal is to have a unified interface
to many different types of matrix formats, mainly sparse matrix
formats, where the various formats typically have the core numerics in
widely different packages (BLAS, LAPACK, PySparse, for instance). PDE
solvers written in Python can then work with one API for creating
matrices and solving linear systems. This idea is not new and has been
explored in many C++ libraries, e.g., Diffpack [3], DOLFIN [5] and
GLAS [10]. The new contribution in this thesis is to have such an
interface in Python and explore some of Python's flexibility. We
also have a more heterogeneous collection of underlying matrix
libraries than what the cited C++ packages aim at. Python is slow for
number crunching so it is crucial to perform the factorization and
solve operations in compiled Fortran, C or C++ libraries. The PyACTS
project [26] has a goal quite similar to ours, but is aimed at the
tools in the ACTS collection only (ScaLAPACK, SuperLU, Hyper, to
mention some). A natural future extension of our matrix library will
be to incorporate PyACTS. This will also provide support for parallel
computing. The present thesis is limited to serial computing only.
At the end of Chapter 1 we explore the matrix library for solving
diffusion equations. In particular, we compare the the efficiency of
the traditional ADI methods with sparse matrix factorization-based
solution techniques.
Chapter 2 deals with a unified interface, called Easyviz, to
visualization packages, both for curve plotting and for 2D/3D
visualization of scalar and vector fields. The interface calls up
various plotting packages, some simple and easy-to-install like
Gnuplot and some more comprehensive like VTK and Matplotlib. The
Easyviz syntax almost coincides with that of Matlab, thus making it
easy for students and researchers trained in Matlab to start plotting
with a Python-based platform. Or in other words, one can use
Matlab-like syntax for accessing a wide range of visualization
tools. In this way, PDE solvers can visualize the solutions with a
code independent of the underlying plotting package that actually
produces the plots. Chapter 2 acts as a tutorial for Easyviz and has
been written together with my supervisor Hans Petter Langtangen.
The focus of this thesis has been on developing reusable and stable
software and to document this software such that it can readily be
applied by students and scientists.