Add an eventSessions at EuroPython 2011 about Scientific Computing
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- Topic: Scientific Computing
Monday 20th June 2011
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High-performance computing on gamer PCs
by Yann Le Du
In Electron Paramagnetic Resonance Imaging, we are faced with a deconvolution problem that has a strong impact on the image actually reconstructed. Faced with the need of mapping the distribution of organic matter in Terrestrial and Martian rock samples for applications in exobiology, we needed to see how to extract a maximum amount of information from our data : our approach uses reservoir computing artificial neural networks coupled to a particle swarm algorithm that evolves the reservoirs’ weights.
The code runs on the Hybrid Processing Units for Science (HPU4Science) cluster located at the Laboratoire de Chimie de la Matière Condensée de Paris (LCMCP). The cluster is composed of a central data storage machine and a heterogeneous ensemble of 6 decentralized nodes. Each node is equipped with a Core2 Quad or i7 CPU and 3-7 NVIDIA Graphical Processing Units (GPUs) including the GF110 series. Each of the 28 GPUs independently explores a different parameter space sphere of the same problem. Our application shows a sustained real performance of 15.6 TFLOPS. The HPU4Science cluster cost $36,090 resulting in a 432.3 MFLOPS/$ cost performance.
That talk is meant to demonstrate on a practical case how consumer grade computer hardware coupled to a very popular computer language can be used to tackle a difficult yet very elementary scientific problem : how do you go from formulating the problem, to choosing the right hardware and software, and all the way to programming the algorithms using the appropriate development tools and methodologies (notably Literate Programming). On the math side, the talk requires a basic understanding of matrix algebra and of the discretization process involved when computing integrals.
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Python(x,y): Python as your next MATLAB
by Vincent Noel
This training session will introduce the Python scientific stack to Engineers who use matlab in their day-to-day job and want to switch to an open solution or explore other alternatives. The basics of Python will first be presented: syntax, variable types and data structures, functions and flow control, exceptions. Python modules and tools required for matlab-like programmation in Python will be presented: ipython, numpy and matplotlib. Several Python applications typical of engineering problems will be presented and compared with their matlab version, as time will allow: plotting (time series, histograms, pseudocolor plots, etc.), basic I/O (e.g. ASCII, CSV, matlab MAT files), signal processing, mapping, etc. The creation of user interfaces with PyQt will be briefly introduced. Differences between Interactive and non-interactive programming will be described. Along the session, key differences with matlab will be underlined and discussed. Sources of information and documentation, online and offline, will be presented.
These concepts will be introduced as coding exercises using the Python programming environment provided by the Python(x,y) distribution, which is freely downloadable and includes recent versions of Python, numpy and matplotlib. This session will also focus on using Python(x,y) efficiently for Python programmation. Attendees should bring their own laptop running Windows. It is also recommended that they download and install the pythonxy distribution from http://www.pythonxy.com/.
Although no knowledge of Python is required to attend this session, a basic knowledge of matlab and of its typical programming usage is needed.
Tuesday 21st June 2011
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Python(x,y): diving into scientific Python (teaser)
by Vincent Noel
Teaser for http://lanyrd.com/2011/europytho...
Wednesday 22nd June 2011
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Python(x,y): diving into scientific Python
by Vincent Noel
This training session will introduce the Python scientific stack to beginner or intermediate-level Python programmers. The basics of scientific programming with Python will be presented:
- creation of arrays and structured arrays using numpy
- fast, loopless manipulation of numpy arrays through fancy indexing and vectorized functions
- convenient saving/loading of array variables using numpy
- improved interactive use through ipython
- data analysis using various scipy modules (signal analysis, image classification, etc)
- plotting large time series, histograms, scatterplots, images etc. using matplotlib
- saving/loading large datasets in structured scientific formats such as netCDF, HDF (depending on interest)These concepts will be used in coding exercises, in the programming environnement provided by the python(x,y) distribution, which is freely downloadable and includes recent versions of Python, numpy and matplotlib. The Python(x,y) distribution runs on Windows, which will be the OS of choice for this session.
No prior knowledge of scientific programmation using Python is required. A minimum understanding of Python programmation is required.
Attendees should bring their own laptops to the session. They should download and install the Python(x,y) distribution prior to the session, even though they might be able to do so during the session itself.
Thursday 23rd June 2011
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Python for High Performance and Scientific Computing
Python is an accepted high-level scripting language with a growing community in academia and industry. It is used in a lot of scientific applications in many different scientific fields and in more and more industries, for example, in engineering or life science). In all fields, the use of Python for high-performance and parallel computing is increasing. Several organizations and companies are providing tools or support for Python development. This includes libraries for scientific computing, parallel computing, and MPI. Python is also used on many core architectures and GPUs, for which specific Python interpreters are being developed. A related topic is the performance of the various interpreter and compiler implementations for Python.
The talk gives an overview of Python’s use in HPC and Scientific Computing and gives information on many topics, such as Python on massively parallel systems, GPU programming with Python, scientific libraries in Python, and Python interpreter performance issues. The talk will include examples for scientific codes and applications from many domains.
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Building a Scientific Software Application with the Enthought Tool Suite (teaser)
by Mark Dickinson
Teaser for http://lanyrd.com/2011/europytho...
Friday 24th June 2011
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Building a Scientific Software Application with the Enthought Tool Suite
by Mark Dickinson
The Enthought Tool Suite (ETS) is a collection of Python-based open source components that form a foundation for nearly every application that we deliver to our customers. In this talk I'll demonstrate how to use ETS to rapidly develop an example scientific software application. We'll concentrate particularly on introducing Traits, Traits UI, and the Chaco and Mayavi visualisation tools.
Prerequisites: some previous experience of working with Python and NumPy / SciPy is recommended.
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