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4th Workshop on Python for High Performance and Scientific Computing (PyHPC 2014)
Program
Date:
Monday, November 17, 2014
Room:
291
Time
Presenters
Title
Paper
Slides
9:00 - 9:05
Andreas Schreiber
(
DLR
) and William Scullin
(ANL)
Welcome & Introduction
9:05 - 10:00
Matthew G. Knepley
Keynote:
Interoperating with Languages in HPC
10:00 - 10:30
Break
10:30 - 11:00
Martin Bauer, Florian Schornbaum, Christian Godenschwager, Matthias Markl, Daniela Anderl, Harald Köstler and Ulrich Rüde
A Python extension for the massively parallel framework waLBerla
11:00 - 11:30
Michael Klemm and Jussi Enkovaara
pyMIC: A Python Offload Module for the Intel® Xeon Phi™ Coprocessor
11:30 - 12:00
Sebastien Jourdain, Scott Wittenburg and Patrick O'Leary
Python Enabled ParaViewWeb for HPC Analysis and Visualization
12:00 - 13:30
Lunch Break
13:30 - 14:30
Andy R. Terrel
Python in the Hadoop Ecosystem
14:30 - 15:00
Rosa Filgueira, Malcolm Atkinson, Andrew Bell, Ian Main and Branwen Snelling
VarPy: A Python library for volcanology and rock physics data analysis
15:00 - 15:30
Break
15:30 - 16:00
Christine Harvey, Scott Rosen, James Ramsey, Christopher Saunders and Samar Guharay
Computationally and Statistically Efficient Model Fitting Techniques
16:00 - 16:30
Mads R. B. Kristensen, Simon Andreas Frimann Lund, Troels Blum and Kenneth Skovhede
Separating NumPy API from Implementation
16:30 - 17:00
Utkarsh Ayachit and Berk Geveci
Scientific data analysis and visualization at scale in VTK/ParaView with NumPy
17:00 - 17:30
Andreas Schreiber
(
DLR
) and William Scullin
(ANL)
Discussion and Closing
PyHPC 2014
Call for Papers
Program
Links
SC14
Submission page (EasyChair)
Facebook event page
Twitter @PythonHPC
Lanyrd page
PyHPC Workshop Series
PyHPC 2011 (Seattle, USA)
PyHPC 2012 (Salt Lake City, USA)
PyHPC 2013 (Denver, USA)
PyHPC 2014 (New Orleans, USA)
PyHPC 2015 (Austin, USA)
PyHPC 2016 (Salt Lake City, USA)
PyHPC 2017 (Denver, USA)
Paper
Scientific data analysis and visualization at scale in VTK/ParaView with NumPy
(0.56 MB)
Separating NumPy API from Implementation
(0.37 MB)
Computationally and Statistically Efficient Model Fitting Techniques
(0.18 MB)
VarPy: A Python library for volcanology and rock physics data analysis
(1.46 MB)
Python Enabled ParaViewWeb for HPC Analysis and Visualization
(7.92 MB)
pyMIC: A Python Offload Module for the Intel(R) Xeon Phi(tm) Coprocessor
(0.38 MB)
A Python extension for the massively parallel framework waLBerla
(3.67 MB)
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