open source and scientific computing
TRANSCRIPT
Open Source andScientific Computing
Tomo PopovićŽarko ZečevićLuka LazovićStevan 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
– Oracle
– Yahoo
– 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