2015年5月8日,孙贤和教授应邀来中国科学院深圳先进技术研究院云计算研究中心进行学术交流,作了题为“Parallelism for High Performance Data Processing: a rethinking”的学术讲座,报告由王洋研究员主持。

Abstract

Scalable data managementfor big data applications is a challenging task. Itputs even more pressure on the lasting memory-wall problem, which makes data access the prominent performance bottleneck for high performance computing (HPC), and has changed the interest of HPC to HPDP (High Performance Data Processing). HPC is known for its massivelyparallel architectures. A natural way to achieve HPDP is to increase and utilize memory concurrencyto a level commensurate with that of HPC. We argue that substantial memory concurrency exists at each layer of current memory systems, but it has not been fully utilized. In this talk wereevaluate memory systems and introduce the novel C-AMAT model for system design analysis of concurrent data accesses. C-AMAT is a paradigm shift to support sustained data accessing from a data-centric view. The power of C-AMAT is that it has opened new directions to reduce data access delay. In an ideal parallel memory system, the system will explicitly express and utilize parallel data accesses. This awareness is largely missing from current memory systems and missing from current architecture and algorithm design. Wewill review the concurrency available in modern memory systems, present theconcept of C-AMAT, anddiscuss the considerations and possibility of optimizing parallel data access for big data applications. We will also presentsome of our recent results which quantize and utilizeparallel I/Ofollowing the parallel memory concept for HPDP.


Bio

Dr. Xian-He Sun is a Distinguished Professor of Computer Science of the Department of Computer Science at the Illinois Institute of Technology (IIT). He is the director of the Scalable Computing Software laboratory at 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 includeparallel and distributed processing, memory and I/O systems, software systems for big data applications, and performance evaluation. He has over 200 publications and 5 patents in these areas. He is a former IEEE CS distinguished speaker, a former vice chair of the IEEE Technical Committee on Scalable Computing, the past chair of the Computer Science Department at IIT (9/2009-8/2014), and is serving and served on the editorial board of most of the leading professional journals in the field of parallel processing. More information about Dr. Sun can be found at his web site www.cs.iit.edu/~sun/.