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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
Google+ event page
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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