Abstract

Technology advances are unbalanced. CPU performance has been improving at a
much faster pace than memory technologies during the last three decades,
which has led to the so-called memory-wall problem. In the meantime, newly
emerged IT applications, such as computer animation, social networks, and
sensor networks, are all data intensive, which has led to the so-called
big-data problem. The lasting memory-wall problem compounded with the newly
emerged big-data problem has changed the landscape of computing. CPU speed
is no longer the performance bottleneck of a computing system, the data
access speed is. However, historically computing systems are designed and
developed to utilize CPU performance, not data accessing. A paradigm change
is needed to support data-centric computing. In this talk we first review
the history and concepts of the big-data and memory-wall problems. We then
discuss the challenges of design advanced memory systems for extreme-scale
computing. Finally, we present some our recent results in understanding and
optimizing the performance of memory systems from the data-centric
point-of-view.

Bio-Short version

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 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.
More information about Dr. Sun can be found at his web site
www.cs.iit.edu/~sun/