Memory System Parallelism for Data-intensive and Data-driven Applications

Speaker:        Professor Xian-He Sun
                Illinois Institute of Technology
                Chicago, USA

Title:          "Memory System Parallelism for Data-intensive and
                 Data-driven Applications"

Date:           Thursday, 5 December 2013

Time:           11:00am - 12 noon

Venue:          Room 1504 (near lifts 25 & 26), HKUST

Abstractz;

Computing becomes more and more data-intensive and data-driven. The
lasting memory-wall problem of system design compounded with the newly
emerged big-data problem of application practice has changed the landscape
of computing. CPU speed is no longer the performance bottleneck of a
high-end computing system, the data access speed is, whereas the data
access speed is limited by the performance of memory and file systems.
Concurrency exists in memory and file systems. Historically, this
concurrency is designed and utilized around computing, not sustained data
accessing. A paradigm shift is needed to support data-centric computing.
In this talk we introduce the memory parallelism concept. First, we review
the concurrency available in modern memory systems, and propose the C-AMAT
formulation for system design analysis of concurrent data accesses. Next,
we illustrate the difference between memory-concurrency from a
computing-centric view and memory-parallelism from a data-centric view,
and discuss the considerations of utilizing parallel data access for big
data applications. Finally, we present some of our recent results which
quantize and utilize parallel I/O following the memory-parallelism
concept.

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Biography:

Dr. Xian-He Sun is the chairman and a professor of the Department of
Computer Science, the director of the Scalable Computing Software
laboratory at the Illinois Institute of Technology (IIT) and a guest
faculty in the Mathematics and Computer Science Division at the Argonne
National Laboratory. Before joining IIT, he worked at DoE Ames National
Laboratory, at ICASE, NASA Langley Research Center, at Louisiana State
University, Baton Rouge, and was an ASEE fellow at Navy Research
Laboratories. Dr. Sun is an IEEE fellow and is known for his
memory-bounded speedup model, also called Sun-Ni's Law, for scalable
computing. His research interests include parallel and distributed
processing, high-end computing, memory and I/O systems, and performance
evaluation. He has close to 200 publications and 4 patents in these areas.
He is a vice chair of the IEEE Technical Committee on Scalable Computing,
a member of the 2013 IEEE fellow evaluation committee, serving and served
on the editorial board of most of the leading professional journals in the
field of parallel processing, and is a named overseas expert of Chinese
Academy of Sciences. More information about Dr. Sun can be found at his
web site www.cs.iit.edu/~sun/.