Publications

@inproceedings{WOS:000569996300272,

title = {Heterogenous Computing for Markov Models in Big Data},

author = {Mihaela Malita and George Vladut Popescu and Gheorghe M. Stefan},

doi = {10.1109/CSCI49370.2019.00279},

isbn = {978-1-7281-5584-5},

year  = {2019},

date = {2019-01-01},

booktitle = {2019 6TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND 

 COMPUTATIONAL INTELLIGENCE (CSCI 2019)},

pages = {1500-1505},

publisher = {IEEE},

address = {345 E 47TH ST, NEW YORK, NY 10017 USA},

abstract = {Many Big Data problems, Markov Model related included, are solved using 

 heterogenous systems: host + parallel programmable accelerator. The 

 current solutions for the accelerator part - for example, GPU used as 

 GPGPU - provide limited accelerations due to some architectural 

 constraints. The paper introduces the use of a programmable parallel 

 accelerator able to perform efficient vector and matrix operations 

 avoiding the limitations of the current systems designed using 

 ``of-theshelf'' solutions. Our main result is an architecture whose 

 actual performance is a much higher percentage from its peak performance 

 than those of the consecrated accelerators. The performance improvements 

 we offer come from the following two features: the addition of a 

 reduction network at the output of a linear array of cells and an 

 appropriate use of a serial register distributed along the same linear 

 array of cells. Thus, for a n-state Markov Model, instead of a solution 

 with the size in O(n2) and an acceleration in O(n2/logn), we offer an 

 accelerator with the size in O(n) and the acceleration in O(n).},

note = {6th Annual Conference on Computational Science and Computational 

 Intelligence (CSCI), Las Vegas, NV, DEC 05-07, 2019},

keywords = {Big Data; Markov Models; parallel architecture; accelerators;   heterogenous computing},

pubstate = {published},

tppubtype = {inproceedings}

}