Weitere ähnliche Inhalte Ähnlich wie Open Source and Scientific Computing (20) Mehr von Tomo Popovic (10) Kürzlich hochgeladen (20) Open Source and Scientific Computing2. IT'15 Conference Žabljak, Montenegro © 2015 T. Popovic Slide 2
Outline
● Scientific Computing
● Open Source Software
● Scientific Tools
● Learn more
● Conclusions and Demo
5. IT'15 Conference Žabljak, Montenegro © 2015 T. Popovic Slide 5
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)
7. IT'15 Conference Žabljak, Montenegro © 2015 T. Popovic Slide 7
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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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
13. IT'15 Conference Žabljak, Montenegro © 2015 T. Popovic Slide 13
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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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?
20. IT'15 Conference Žabljak, Montenegro © 2015 T. Popovic Slide 20
Demo
● Žarko Zečević
● Luka Lazović
● Stevan Šandi
● Novica Daković