Online registration for the workshop "State-of-the-Art in DataFlow SuperComputing for BigData DeepAnalytics and SignalProcessing"

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Professor Veljko Milutinovic
Life Member of the ACM
Life Fellow of the IEEE
Member, a Former Trustee and Treasurer, of Academia Europaea
Founding Member of the Serbian National Academy of Engineering
Foreign Member of the Montenegro National Academy of Sciences and Arts

DataFlow SuperComputing for BigData DeepAnalytics and SignalProcessing
This 30-minute lecture, followed by a 90-minute tutorial on DataFlow programming, analyses the essence of DataFlow SuperComputing, defines its advantages and sheds light on the related programming model that corresponds to the recent Intel patent about the future Intel's dataflow processor. The stress is on issues of interest for Signal Processing.
Acording to Alibaba and Google, as well as the open literature, the DataFlow paradigm, compared to the ControlFlow paradigm, offers: (a) Speedups of at least 10x to 100x and sometimes much more (depends on the algorithmic characteristics of the most essential loops and the spatial/temporal characteristics of the Big Data Streem, etc.), (b) Potentials for a better precision (depends on the characteristics of the optimizing compiler and the operating system, etc.), (c) Power reduction of at least 10x (depends on the clock speed and the internal architecture, etc.), and (d) Size reduction of well over 10x (depends on the chip implementation and the packiging technology, etc.). However, the programming paradigm is different, and has to be mastered. The follow-up tutorial explains the programming paradigm details, using Maxeler as an example and sheds light on the ongoing research, which, in the case of the speaker, was highly influenced by four different Nobel Laureates: (a) from Richard Feynman it was learned that future computing paradigms will be successful only if the amount of data communications is minimized; (b) from Ilya Prigogine it was learned that the entropy of a computing system would be minimized if spatial and temporal data get decoupled; (c) from Daniel Kahneman it was learned that the system software should offer options related to approximate computing; and (d) from Tim Hunt it was learned that the system software should be able to trade between latency and precision. This tutorial also includes hands-on opportunities for attendees.

About the Speaker:
Prof. Veljko Milutinovic (1951) received his PhD from the University of Belgrade in Serbia, spent about a decade on various faculty positions in the USA (mostly at Purdue University and more recenlty at the Indiana University in Bloomington), and was a co-designer of the DARPAs first GaAs RISC microprocessor at 200MHz (about a decade before commercial efforts on the same speed) and the DARPAs first GaAs Systolic Array with 4096 processors on 200MHz (both well documented in the open literature). Later, for about three decades, he taught and conducted research at the University of Belgrade, in EE, MATH, BA, and PHYS/CHEM. Now he serves as the Chairman of the Board of IPSI Belgrade (a spin-off of Fraunhofer IPSI from Darmstadt, Germany). 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 18 of his books and related publications, forewords were written by 18 different Nobel Laureates with whom he cooperated on his past industry sponsored projects. He has over 100 SCI journal papers (mostly in IEEE and ACM journals), well over 1000 Thomson-Reuters citations, well over 1000 SCOPUS citations and well over 4000 Google Scholar citations, with h=36 and i10=100. Short or long courses on the subject he delivered so far in a number of universities worldwide: MIT, Harvard, Boston, NEU, Dartmouth, U of Massachusetts at Amherst, USC, UCLA, Columbia, NYU, Princeton, NJIT, CMU, Temple, Purdue, IU, UIUC, Michigan, Wisconsin, Minnesota, FAU, FIU, Miami, Central Florida, Alabama, Tennessee, GeorgiaTech, OhioState, Imperial, King's, Manchester, Haddersfield, Cambridge, Oxford, Dublin, Cork, Cardiff, Edinburgh, EPFL, ETH, TUWIEN, UNIWIE, Karlsruhe, Stuttgart, Bonn, Frankfurt, Heidelberg, Aachen, Darmstadt, Dortmund, KTH, Uppsala, Karlskrona, Karlstad, Napoli, Salerno, Siena, Pisa, Barcelona, Madrid, Valencia, Oviedo, Ankara, Bogazici, Koc, Istanbul, Technion, Haifa, BerSheba, Eilat, Arad, Cluj, etc, etc. Also at the World Bank in Washington DC, IMF, the Telenor Bank of Norway, the Reiffeisen Bank of Austria, Brookhaven National Laboratory, Lawrence Livermore National Laboratory, IBM TJ Watson, HP Encore Labs, Intel Oregon, Qualcomm VP, NCR, RCA, Fairchild, Honeywell, Yahoo NY, Google CA, Microsoft, Finsoft, ABB Zurich, Oracle Zurich, and many other industrial labs, as well as at Tsinghua, Shandong, NIS of Singapore, NTU of Singapore, Tokyo, Sendai, Seoul, Pusan, Sydney, Hobart, Auckland, Wellington, Toronto, Montreal, MexicoCity, Durango, etc.

Accompanying Textbooks and Journal Papers:

Milutinovic, V., et al, Guide to DataFlow SuperComputing, Springer, 2015 (one textbook, part I) and 2017 (two textbooks, parts II and III).

Hurson, A., Milutinovic, V., editors, Advances in Computers: DataFlow, Elsevier, 2015 (one SCI textbook) and 2017 (two SCI textbooks).

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

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

Milutinovic, V., "The HoneyComb Architecture," Proceedings of the IEEE, 1989.

Milutinovic, V. et all, "Splitting Spatial and Temporal Localities for Entropy Minimiation" Tutorial of the IEEE ISCA, 1995.

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

Milutinovic, V., "A Comparison of Suboptimal Detection Algorithms (Suboptimal Algorithms for Data Analytics)," Proceedings of the IEE (now IET), 1988.

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, November 2016.

Kotlar, M., Milutinovic, V., "The Tensor Calculus Operations for the Data Flow Paradigm," The ExaComm Workshop of the International Supercomputing Conference, Frankfurt, Germany, June 28, 2018.

Milutinovic, V., "The Ultimate DataFlow," Springer, 2019, Invited Key Talk at the ExaComm Workshop of the ISC, Frankfurt, Germany, June 28, 2018.

Milutinovic, V., Kotlar M,, "Handbook of Supercomputing," IGI Global, Hershey, Pennsylvania, USA, 2021.

Milutinovic, V., et al, "The Unltimate DataFlow for Ultimate Super-Computing-on-a Chip," ArXiv, January 2021.