Introduction to Open Source Software and its use for Scientific Computing followed by demonstrations of Python/IPython, Octave, SciLab, and Freemat. The presentation took place at the 20th Information Technology Conference (IT'15) in Zabljak, Montenegro.

1. Open Source and
Scientific Computing
Tomo Popović
Žarko Zečević
Luka Lazović
Stevan Šandi
Novica Daković
Žabljak 2015

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Outline
● Scientific Computing
● Open Source Software
● Scientific Tools
● Learn more
● Conclusions and Demo

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Scientific Computing
● What is it?
● So what?

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Scientific Computing

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Scientific Computing
source: http://igmcs.utk.edu (The University of Tennessee)

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Why Open Source?
● Widely used by the
industry and academia
● Open Data
– Data management
– Libraries, APIs
Source: http://shutterstock.com
(Marko Rullkoetter)

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Who is using Open Source?
● Pretty much everyone
– Government
– Academia
– IBM
– Google
– Oracle
– Yahoo
– Facebook
– Microsoft
– NYSE
– Audi, Mercedes, Toyota...
– ...

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Open Source Software Model
● Initiation
● Execution
● Releasing

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Roles in software development
● Commercial
– Developers
– Users
– Customers
● Open Source
– Developers
– Users (co-developers)

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Open Source Software

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Scientific Software Tools
● General
– Python, SciPy/NumPy
– R, RStudio
– Octave, FreeMat SciLab
– ...
● Libraries
– Data management
– Visualization
– Various APIs
– …
● Domain Specific
– Networks
– GIS
– Bioscience
– Automotive
– …

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Open Data
● Range
– Non-existant
– Big data
● Access
– OSS Data Management Tools
– Libraries, APIs
● Examples
– Genome Project (UCSC)
– Weather Data
– GIS
– Open City

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Enabling Global Ecosystems
source: http://datameer
(The Hadoop Ecosystem)

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Promoting Repeatable Research
● Problem definition (documentation, code)
● Data and configuration
● Model implementation (code)
● Results sharing
● Collaborate

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Do your own research!
● Documentation, Wiki
● Google, YouTube
● StackOverflow, GitHub
● Local communities
● Scientific publications

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Open Source Education

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Advantages/Disadvantages
● Advantages
– Relatively easy to obtain, learn, and
use
– Modern
– Increasingly popular
– Often cross-platform (desktop, server,
cloud, HPC)
– Large and ever growing set of
libraries and support tools
– No vendor lock-in
– Collaboration and standardization
– Free
– ...
● Disadvantages
– Mis-understanding of the
maturity model
– Mis-interpretation of hidden costs
and licenses
– Performance in some specific
domains
– ...

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Things to Consider
● Maturity model
– Quality of software
– Documentation
– Community
– Project activity
– Adoption by others
– Support options
● Data Sources/Tools
– Libraries
– APIs
● Learning curve vs. ROI
● Licenses (BSD, GNU, Eclipse,...)

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Conclusions
● Computational thinking is part of all scientific domains
● Open Source Software plays important role in scientific
computing (general tools, domain specific, libraries,...)
● Suggestions:
– Look into Python and R!!! Consider using Octave instead of Matlab!
– Do your own research on OSS tools!
– Learn about (open) data relevant to your research! Investigate OSS
data management tools, libraries, APIs,...
– Enroll in relevant online courses (edX, Coursera,...)
– What about open source hardware?

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Demo
● Žarko Zečević
● Luka Lazović
● Stevan Šandi
● Novica Daković