Lisandro Dalcín

Cython: The Best of Both Worlds

Cython is a Python language extension that allows explicit type declarations and is compiled directly to C. As such, it addresses Pythons large overhead for numerical loops and the difficulty of efficiently using existing C and Fortran code, which Cython can interact with natively.

MPI for Python

MPI for Python provides bindings of the Message Passing Interface (MPI) standard for the Python programming language and allows any Python program to exploit multiple processors. This package is constructed on top of the MPI-1 specification and defines an object-oriented interface which closely follows MPI-2 C++bindings. It supports point-to-point (sends, receives) and collective (broadcasts, scatters, gathers) communications of general Python objects. Efficiency has been tested in a Beowulf class cluster and satisfying results were obtained. MPI for Python is open source and available for download on the web (http://www.cimec.org.ar/python).

MPI for Python: Performance improvements and MPI-2 extensions

MPI for Python provides bindings of the message passing interface (MPI) standard for the Python programming language and allows any Python program to exploit multiple processors. In its first release, MPI for Python was constructed on top of the MPI-1 specification defining an object-oriented interface that closely followed the MPI-2 C++ bindings, and provided support for communications of general Python objects. In the latest release, this package is improved to enable direct blocking/non-blocking communication of numeric arrays, and to support almost all MPI-2 features. Improvements in communication performance have been tested in a Beowulf class cluster. Results showed a negligible overhead in comparison to compiled C code. MPI for Python is open source and available for download on the web (http://mpi4py.scipy.org/).

The Cost of Continuity: Performance of Iterative Solvers on Isogeometric Finite Elements

In this paper we study how the use of a more continuous set of basis functions affects the cost of solving systems of linear equations resulting from a discretized Galerkin weak form. Specifically, we compare performance of linear solvers when discretizing using

An energy-stable convex splitting for the phase-field crystal equation

C^0

A survey on direct solvers for Galerkin methods

B-splines, which span traditional finite element spaces, and

Time adaptivity in the diffusive wave approximation to the shallow water equations

C^{p-1}

On round-off error for adaptive finite element methods

B-splines, which represent maximum continuity. We provide theoretical estimates for the increase in cost of the matrix-vector product as well as for the construction and application of black-box preconditioners. We accompany these estimates with numerical results and study their sensitivity to various grid parameters such as element size

Diffusive Wave Approximation to the Shallow Water Equations: Computational Approach

h

A preconditioner for the Schur complement matrix

and polynomial order of approximation