[Crib-list] TODAY... SPEAKER: Hang Liu (UMass, Lowell) | Computational Research in Boston and Beyond Seminar (CRIBB) | Friday, April 5, 2019 | TIME: 12:00 PM - 1:00 PM in Building 32, Room 141 (STATA) (fwd)

[daisymae@mit.edu]

 	T O D A Y . . .


 	               Computational Research in Boston and Beyond Seminar
 				           (CRIBB)



DATE:		Friday, April 5, 2019

TIME:		12:00 PM to 1:00 PM

LOCATION:	Building 32, Room 141  (STATA)
 		        32 Vassar Street
 		        Cambridge, MA

 		      (Pizza/beverages will be provided at 11:45 AM outside
 		       Room 32-141)


TITLE:		Software-Hardware Co-Optimized Data Analytics


SPEAKER:	Hang Liu  (University of Massachusetts, Lowell)


ABSTRACT:


We are increasingly awash in data, both connected and disconnected, as a 
growing array of “sensors”, which is integrated in our daily life, 
continues to generate an explosive amount of data. Notably, IBM recently 
suggests that we are creating~ 2.5 quintillion bytes of data per day. 
Buried in such a rapidly growing flood of data are the key insights to 
resolve the critical issues surrounding our society, for improving 
productivity, enlisting new economic opportunities, and uncovering novel 
discoveries in science and engineering. While accommodating traditional 
queries, such as, word count, is straightforward, the emerging data 
analytical applications (e.g., graph analytics) tend to be more complex, 
thus will place server tax on conventional hardware systems. This talk 
unveils the enormous potentials of the emerging hardware, such as, 
Graphics Processing Unit (GPU), Field-Programmable Gate Array (FPGA) and 
Non-Volatile Memory express (NVMe). At meantime, Dr. Liu demonstrates how 
his work (i.e., USENIX FAST ‘17, DAC ‘19 and a recent submission to SIGMOD 
‘20) leverages the new hardware to accelerate complex analytical 
applications on both connected and disconnected data. As the future work, 
this talk further outlines the mounting challenges as well as the 
potential solutions for deploying the popular graph learning framework on 
hardware accelerators.


Bio:

Dr. Hang Liu is currently an assistant professor in the Department of 
Electrical and Computer Engineering at University of Massachusetts Lowell. 
He receives the Ph.D. degree from the George Washington University in 
2017, and B.E. from Huazhong University of Science and Technology in 2011. 
His research interests include exploiting emerging hardware to build 
high-performance systems for graph computing, machine learning, data 
compression, numerical simulation, cloud computing and software debugging. 
His publications appear in top tier conferences, such as, SC, SIGMOD, 
USENIX FAST and DAC. Particularly, he is the recipient of the NSF CRII 
award and the Champion of 2018 DARPA/MIT/AMAZON Graph Challenge. He is 
also the winner of the Best Dissertation Award from the Department of 
Electrical and Computer Engineering at the George Washington University. 
Notably, his graph traversal systems are ranked highly in both Graph500 
and Green Graph500 benchmarks, which measure the performance and energy 
efficiency of the most powerful supercomputers in the world.


===================================================================

Massachusetts Institute of Technology
Cambridge, MA


For information about the Computational Research in Boston and Beyond Seminar
(CRIBB), please visit....

 			http://math.mit.edu/crib/



===

Shirley A. Entzminger
Administrative Assistant II
Department of Mathematics
Massachusetts Institute of Technology
77 Massachusetts Avenue
Building 2, Room 350A
Cambridge, MA 02139
PHONE:	(617) 253-4347
FAX:	(617) 253-4358
E-mail:	daisymae at math.mit.edu
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