Fernando Pérez

Site Navigation

External links

Table Of Contents

Previous topic

Guido van Rossum at the Py4Science meeting

Next topic

Teaching

This Page


Python for Scientific Computing at SIAM CSE 2011: Slides

At the 2011 SIAM CSE meeting held in Reno on February 28-March 6, Randy LeVeque from U. Washington, Hans-Petter Langtangen from the Simula Research Laboratory in Norway and I co-organized a 2-part minisymposium entitled Python for Scientific Computing. This was done partly as a followup to the successful one we had in 2009, and we had again good attendance and lively discussions.

Our minisymposium was divided in two parts, and I am posting here links to the original program pages as well as all the slides I have so far from the speakers, along with any links they may have provided to their personal or project pages. A short narrative is my blog and I also put some pictures up.

As a sign of the healthy growth of the scientific python ecosystem, this year there were also other python-focused minisymposia at this conference, as well as several python talks in other MS; those slides are below.

MS: Python in Scientific Computing - Part I of II

Capabilities and Recent Developments of NumPy for Scientific Computing
Travis E. Oliphant, Enthought, Inc., USA
Cython: Compiled Code meets Dynamic Python
Lisandro Dalcin, Centro Int. de Métodos Computacionales en Ingeniería, Argentina; Robert Bradshaw, University of Washington, Seattle, USA
Matplotlib - from Interactive Exploration to Publication Graphics
John Hunter, Tradelink Securities, Inc., USA
Why Modern, High-performance Networking Matters for Interactive Computing
Fernando Perez, University of California, Berkeley, USA; Brian Granger, California Polytechnic State University, San Luis Obispo, USA; Evan Patterson, California Institute of Technology, USA

MS: Python in Scientific Computing - Part II of II

Interactive Parallel Python with ZeroMQ
Min Ragan-Kelley and Fernando Perez, University of California, Berkeley, USA; Brian Granger, California Polytechnic State University, San Luis Obispo, USA
Real-time Classification of Astronomical Events with Python
Joshua Bloom, Dan Starr, Joseph Richards, Nathaniel Butler, and Dovi Poznanski, University of California, Berkeley, USA
SymPy: Symbolic Mathematics in Pure Python
Mateusz Paprocki, University of Nevada, Reno, USA
FEMhub Online Numerical Methods Laboratory
Pavel Solin and Ondrej Certik, University of Nevada, Reno, USA; Mateusz Paprocki, University of Wroclaw, Poland; Aayush Poudel, University of Nevada, Reno, USA

Other Python talks

There were a number of other sessions dedicated to Python tools, as well as talks based on Python elsewhere. Here is a summary of the ones for which I have slides (a few are pending review by National Labs for public release):

MS: Python-based Software for Solving Partial Differential Equations - Part I of II

Lessons Learned and Open Issues from the Development of the Proteus Toolkit for Coastal and Hydraulics Modeling
Chris Kees and Matthew Farthing, U.S. Army Engineer Research and Development Center, USA
FEniCS: An Attempt to Combine Simplicity, Generality, Efficiency and Reliability
Kent-Andre Mardal, University of Oslo, Norway; Hans Petter Langtangen, Simula Research Laboratory and University of Oslo, Norway; Anders Logg, Simula Research Laboratory, Norway; Garth N. Wells, University of Cambridge, United Kingdom

MS: Python-based Software for Solving Partial Differential Equations - Part II of II

Making a Python based PDE Solver Work Efficiently in Parallel with the Available Open Source Interfaces to MPI
Daniel Wheeler and Jonathan Guyer, National Institute of Standards and Technology, USA; James O’Beirne, George Mason University, USA
Python, Clawpack, and PyClaw
Randall J. LeVeque and Kyle T. Mandli, University of Washington, USA
Mpi4py and Petsc4py: Using Python to develop Scalable PDE Solvers
Lisandro Dalcin, Centro Int. de Métodos Computacionales en Ingeniería, Argentina; Chris Kees, U.S. Army Engineer Research and Development Center, USA
Paper to GPU: Optimizing and Executing Discontinuous Galerkin Operators in Python
Andreas Kloeckner, Courant Institute of Mathematical Sciences, New York University, USA

MS: Python Software for Numerical Optimization

A Flexible Python Environment for PDE-Constrained Optimization
Dominique Orban, École Polytechnique de Montréal, Canada; Nick Gould and Sue Thorne, Rutherford Appleton Laboratory, United Kingdom
Analyzing Optimization Models Developed with the Pyomo Modeling Software (slides pending review by Sandia NL for release)
William E. Hart and Jean-Paul Watson, Sandia National Laboratories, USA; David Woodruff, University of California, Davis, USA
Stochastic Nonlinear Programming with Pyomo (slides pending review by Sandia NL for release)
Carl Laird, Texas A&M University, USA; Jean-Paul Watson, Sandia National Laboratories, USA
Algorithmic Differentiation in Python
Sebastian F. Walter, Humboldt University Berlin, Germany; Bradley M. Bell, University of Washington, USA

MS: FEniCS: Automated Solution of Differential Equations

On the Construction of Preconditioners for Systems of PDEs
Kent-Andre Mardal, University of Oslo, Norway
Automated Goal-Oriented Error Control
Marie E. Rognes and Anders Logg, Simula Research Laboratory, Norway

MS: CSE Education

Sculpture, Geometry and Computer Science
Randy Heiland, Indiana University, USA; Charles Perry, www.charlesperry.com, USA; Barbara Ream, International School of Columbus, USA; Andrew Lumsdaine, Indiana University, USA

MS: Verifiable, Reproducible Research and Computational Science

Reproducible Research, Lessons from the Madagascar Project
Sergey Fomel, University of Texas at Austin, USA (lead of the Magadascar project).

MS: Kinetic Plasma Modeling

Design and Preliminary Results for PIC on GPUs with Python
Min Ragan-Kelley, University of California, Berkeley, USA; John Verboncoeur, University of California, USA

MS: Advanced Algorithms on GPUs

A Case Study of GPUs in Scientific Computing: Low-Order FEM
Matthew G. Knepley, Argonne National Laboratory, USA
High-Order Discontinuous Galerkin Methods by GPU Metaprogramming
Andreas Kloeckner, Courant Institute of Mathematical Sciences, New York University, USA; Timothy Warburton, Rice University, USA; Jan S. Hesthaven, Brown University, USA