alan edelman, jeff bezanson viral shah, stefan karpinski and the vibrant open-source community
DESCRIPTION
Alan Edelman, Jeff Bezanson Viral Shah, Stefan Karpinski and the vibrant open-source community. 150 100 50 0. Daily Contributions. 2013. 2012. Computer Science & AI Laboratories. Collaborative Coding Vision (mockup) Realized in 18.337/6.338. Julia. - PowerPoint PPT PresentationTRANSCRIPT
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Alan Edelman, Jeff BezansonViral Shah, Stefan Karpinski
and the vibrant open-source community
Computer Science & AI Laboratories
150100 50 0
2012 2013
Daily Contributions
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Collaborative Coding Vision (mockup)
Realized in 18.337/6.338Julia
Bob> include(“MyBigDataSet”) Lucy> h=hist(bigdata[:])Mike> svdvals(bigdata)Alice> qrfactor(bigdata)Mike> daily_cycle()
“It’s like having google docs for big data exploring!”
Bob: (9:15am) The folks at BigCorp are excited about working together to explore their data Alice: (9:42am) I’m running the regression. What do you think? Mike: (9:45am) The daily cycle is getting clearer. Wow! a good fit!
Data Histogram
Daily Cycle
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Google Julia
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Julia Facts
• Released: February 2012
• Technical Problem: Computing Environment
– New
– Fast
– Human
– Open Source
– Flexible
– Scalable for “big data” and “many processors”
• You don’t need our permission to try it, or to contribute
Forthcoming Book
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Julia in the
traditional classroom
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Julia in the MOOCs classroom
Google: julia videos mit
Julia is MOOCsready for so many kinds of classes!
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Julia in the News
“Julia is a new language for scientific computing that is winning praise from a slew of very smart people, … As a language, it has lofty design goals, which, if attained, will make it noticeably superior to Matlab, R and Python for scientific programming.”
Top 100 R-posts of 2012 (Page Views)
TechCrunch
Written by the author of “Machine Learning for Hackers”
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BenchmarkPerformance
fibparse_intquicksortmandelpi_sumrand_mat_statrand_mat_mul
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Why a fresh approach?
Life in the 1980’s:• Performance was poor, but nobody cared• Programs were easy (even fun!) to use• Processors were getting faster anyway
Today:
• Users want much more• More sophistication• More speed• Easier to use, Easier to Collaborate• Bigger Machines• More Open, more Extensible• Easy Deployment
Current Players:
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Every Day a New Package(Tailored Toolkit!) At least 150 by now
A hot optimization algorithm usedin machine learning!Implemented using Julia’s asynchronous parallel technologies
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Innovation 2013 Style
–We are building what we wanted–They said it could not be done–Others are joining us!
–What do you want?