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DIST Department of information sciences and technologies

When: Monday, 23rd of March 2015 at 4 pm 

Lecture is a part of the joint Seminars for Mathematics and Computer Science held by the Department of Mathematics and the Department of Information Science and Technology at UP FAMNIT, Department of Mathematics and Department of Information science and Technology at UP IAM, Department of Mathematics and Computer Science at UP PEF and the Department of Mathematics and theoretical Computer Science at IMFM.

Where: FAMNIT-1-MP2 at the Faculty of Mathematics, Natural Sciences and Information Technology, University of Primorska, Glagoljaška 8, Koper

LECTURER: Prof. Veljko Milutinovic,

Fellow of the IEEE
Life Member of the ACM
Member of Academia Europaea
Member of the Serbian Academy of Engineering
Member of the Scientific Advisory Board of Maxeler
Member of the Scientific Advisory Board of Moskowitz-Jacobs

TITLE: DataFlow SuperComputing for BigData Analytics

ABSTRACT:
This presentation analyses the essence of DataFlow SuperComputing,  defines its advantages and sheds light on the related programming  model. DataFlow computers, compared to ControlFlow computers, offer  speedups of 20 to 200 (even 2000 for some applications), power  reductions of about 20, and size reductions of also about 20. However,  the programming paradigm is different, and has to be mastered. The  talk explains the paradigm, using Maxeler as an example, and sheds  light on the ongoing research in the field. Examples include  DataEngineering, DataMining, FinancialAnalytics, etc. A recent study  from Tsinghua University in China reveals that, for Shallow Water  Weather Forecast, which is a BigData problem, on the 1U level, the  Maxeler DataFlow machine is 14 times faster than the Tianhe machine,  which is rated #1 on the Top 500 list (based on Linpack, which is a  smalldata benchmark). Given enough time, the talk also gives a  tutorial about the programiing in space, which is the programming  paradigm used for the Maxeler dataflow machines (establishe din 2014  by Stanford, Imperial, Tsinghua, and the University of Tokyo).