Lecture and Tutorial by Prof. Veljko Milutinovic (University of Belgrade): DataFlow SuperComputing for BigData

25.04.2017 09:30

Prof. Veljko Milutinovic will be giving a short course (lecture + hands-on tutorial) on Dataflow Supercomputing.


25 April 2017:

  • 9:30-11:15 (Lecture)
  • 15:00-16:30 (Hands-on Tutorial)

26 April 2017:

  • 10:00-12:00 (Hands-on Tutorial)
  • 14:00-16:00 (Hands-on Tutorial)

Where: ML H 37.1

If interested in Hands-on Tutorial, please register by sending email to prof. Onur Mutlu (onur.mutlu@inf.ethz.ch) and Arash Tavakkol  (arash.tavakkol@inf.ethz.ch) since an individual account needs to be created for you to perform the hands-on tutorial activities on remote Maxeler machines.

Title: DataFlow SuperComputing for BigData


This short course 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 paradigm is explained using Maxeler as an example, and light is shedded on the ongoing research in the field. Examples include CreditDerivatives and related banking applications, SignalProcessing, GeoPhysics, WeatherForecast, OilGas, DataEngineering, DataMining, SmartGrid, medical applications, etc. Also, a recent study from Tsinghua University in China is presented, which reveals that, for Shallow Water Weather Forecast (a BigData problem), on the 1U level, the Maxeler DataFlow machine is 14 times faster than the Tianhe machine, rated #1 on the Top 500 list (based on Linpack, which is a smalldata benchmark). Given enough time, a tutorial about the programming in space is also given, which is the programming paradigm used for the Maxeler dataflow machines (established in 2014 by Stanford, Imperial, Tsinghua, and the University of Tokyo). The introductory talk concludes with selected examples and a tool overview (appgallery.maxeler.com and webIDE.maxeler.com). A detailed tutorial on programming in space will be available after the introductory talk. Related hands-on activities will be performed by remote login (maxeler.mi.sanu.ac.rs).

About the Speaker:

Prof. Veljko Milutinovic (1951) received his PhD from the University of Belgrade, spent about a decade on various faculty positions in the USA (mostly at Purdue University), and was a co-designer of the DARPAs first GaAs RISC microprocessor. Later, for almost 3 decades, he taught and conducted research at the University of Belgrade, in EE, MATH, and PHY/CHEM. Now he serves as the Chairman of the Board for the Maxeler operation in Belgrade, Serbia. His research is mostly in datamining algorithms and dataflow computing, with the emphasis on mapping of data analytics algorithms onto fast energy efficient architectures. For 7 of his books, forewords were written by 7 different Nobel Laureates with whom he cooperated on his past industry sponsored projects. He has over 40 IEEE and ACM journal papers, over 400 Thomson-Reuters citations, and about 4000 Google Scholar citations.

Accompanying Papers and Textbooks:

Trifunovic, N., Milutinovic, V., et al, The Appgallery.Maxeler.com for BigData SuperComputing, Journal of Big Data, Springer, 2016.

Milutinovic, V., et al, Guide to DataFlow SuperComputing, Springer, 2015 (textbook).

Milutinovic, V., editor, Advances in Computers: DataFlow, Elsevier, 2015 (textbook).

Trifunovic, N., Milutinovic, V. et al, Paradigm Shift in SuperComputing: DataFlow vs ControlFlow, Journal of Big Data, 2015

Jovanovic, Z., Milutinovic, V., "FPGA Accelerator for Floating-Point Matrix Multiplication," The IET Computers and Digital Techniques Premium Award for 2014, Volume 6, Issue 4, 2012 (pp. 249-256).

Flynn, M., Mencer, O., Milutinovic, V., at al, Moving from PetaFlops to PetaData, Communications of the ACM, May 2013.

Trobec, R. Vasiljevic, R., Tomasevic, M., Milutinovic, V., et al, "Interconnection Networks for PetaComputing," ACM Computing Surveys, September 2016. ==================================================