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Departmental Seminars
DataFlow SuperComputing for BigData DeepAnalytics
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Europe/Athens
B228 (ΘΕΕ02)
B228
ΘΕΕ02
UCY Department of Physics Lecture Room 1 (Aglantzia Campus)
Description
Abstract: The presentation analyses the essence of DataFlow SuperComputing, defines its advantages and sheds light on the related programming model. The stress is on issues of interest for General Engineering. 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 Stream, 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 packaging technology, etc.). The bigger the data, and the higher the reusability of individual data items (which is typical of ML), the higher the benefits of the dataflow paradigm over the control flow paradigm. However, the programming paradigm is different, and has to be mastered. The ongoing research of the speaker has been highly influenced by four different Nobel Laureates: (a) from Richard Feynman it has been learned that future computing paradigms will be successful only if the amount of data communications is minimized; (b) from Ilya Prigogine it has been learned that the entropy of a computing system could be minimized if spatial and temporal data get decoupled; (c) from Daniel Kahneman it has been learned that the system software should offer options related to approximate computing; and (d) from Tim Hunt it has been learned that the system software should be able to trade latency for precision.
About the speaker: Veljko Milutinovic received his PhD from the University of Belgrade in Serbia, and throughout his career, has held multiple faculty positions in the USA. He is credited for the DARPAs first GaAs (Gallium Arsendie) microprocessor at 200MHz (about a decade before mainstream) and the DARPAs first GaAs Systolic Array with 4096 CPUs. His current research and industrial interests are in dataflow acceleration of complex algorithms needing low power. He is currently an adjunct professor in the University of Montenegro, University of Belgrade, Technical University of Graz, and University of Indiana in Bloomington, a member of Montenegrin Academy of Sciences and Arts, Serbian Academy of Engineering, and Academia Europaea, as well as a fellow of the IEEE.
