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Workshop Python for High Performance and Scientific Computing (PyHPC 2013)



Program

Date: Monday, November 18, 2013

Room: 505

 

Time Presenters Title Paper Slides
9:00 - 9:10 Andreas Schreiber (DLR) and William Scullin (ANL) Welcome & Introduction    
9:10 - 9:30

Matthew Turk for Anthony Scopatz and NumFOCUS

NumFOCUS: A Foundation Supporting Open Scientific Software

 

 

9:30 - 10:10 Brian Brennan, Robert C. Kirby, John Zweck and Susan E. Minkoff High-Performance Python-based Simulations of Pressure and Temperature Waves in a Trace Gas Sensor

 

 

10:10 - 10:30 Break
10:30 - 11:10 Simon A. F. Lund, Kenneth Skovhede, Mads R. B. Kristensen and Brian Vinter Doubling the Performance of Python/NumPy with less than 100 SLOC

 

 
11:10 - 11:50 Stephane Thiell, Aurelien Degremont and Henri Doreau ClusterShell, Python library and tools for scalable cluster administration

 

 

11:50 - 12:50 Lunch Break
12:50 - 13:30 Mads R. B. Kristensen, Simon A. F. Lund, Troels Blum, Kenneth Skovhede and Brian Vinter Bohrium: Unmodified NumPy Code on CPU, GPU, and Cluster

 

 
13:30 - 14:10 Joseph Peterson, Michael J Hallock, John A Cole and Zan Luthey-Schulten A Problem Solving Environment for Stochastic Biological Simulations

 

 
14:10 - 14:50 Marat Dukhan Peach-Py: A Python Framework for Developing High-Performance Assembly Kernels

 

 
15:00 - 15:30 Break
15:30 - 16:10 James Amundson, Qiming Lu and Eric Stern Synergia: Driving Massively Parallel Particle Accelerator Simulations with Python

 

 
16:10 - 16:50 Serge Guelton, Pierrick Brunet and Mehdi Amini Compiling Python Modules to Native Parallel Modules Using Pythran and OpenMP Annotations

 

 
16:50 - 17:30 Andreas Schreiber (DLR) and William Scullin (ANL) Discussion and Closing    

 


PyHPC 2013
Program
Call for Papers
Links
SC13
Submission page (EasyChair)
Facebook event page
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Twitter @PyHPC2013
PyHPC.org
PyHPC BoF session signup
Paper
High-Performance Python-based Simulations of Pressure and Temperature Waves in a Trace Gas Sensor (0.39 MB)
Doubling the Performance of Python/NumPy with less than 100 SLOC (0.38 MB)
ClusterShell, Python library and tools for scalable cluster administration (0.17 MB)
Bohrium: Unmodified NumPy Code on CPU, GPU, and Cluster (0.33 MB)
A Problem Solving Environment for Stochastic Biological Simulations (11.56 MB)
Peach-Py: A Python Framework for Developing High-Performance Assembly Kernels (0.21 MB)
Synergia: Driving Massively Parallel Particle Accelerator Simulations with Python (0.25 MB)
Compiling Python Modules to Native Parallel Modules Using Pythran and OpenMP Annotations (0.24 MB)
not licensed
not licensed
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