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Title:
Hardware and Software Challenges in Data-Intensive Computing
Speaker:
Prof. Hao (Howie) Huang
Date:
December 7, 2010 (Tuesday)
Time:
9:30-11:00 AM
Venue:
Room 1131, No.1 Science Building, Peking University
Abstract.
Datasets generated by scientific instruments and business
transactions continue to double per year, creating a dire need for a scalable
data-intensive computing solution. Today, IBM, Amazon, Google, Microsoft, and
many others are investing billions of dollars on large data centers and Clouds
that house hundreds of thousands of computers to access, analyze and store
petabyte- and exabyte-scale datasets. In this talk, I will discuss the research
challenges, from computer hardware and software perspectives, in data-intensive
supercomputing, as well as two of my on-going projects at GWU:
·
Current system architectures
used to build data warehouses are about to hit a power consumption wall. We
propose a novel alternative architecture comprising large number of Amdahl blades that combine
energy-efficient CPUs with solid-state disks to increase sequential read I/O
throughput by an order of magnitude without increasing power consumption. While
keeping the total cost of ownership constant, Amdahl blades offer five times
the throughput of a state-of-the-art computing cluster for data-intensive
applications.
·
A file system with
billions of files, millions of directories and petabytes of storage is no
longer an exception. As file systems grow, users and administrators are increasingly
interested in acquiring high-level analytical information for file management
and analysis. Traditional hierarchical file systems (e.g., ext3 and NTFS),
unfortunately, cannot quickly answer these queries without access to a
pre-existing metadata index. We advocate that user interests can often be
sufficiently satisfied by approximate - statistically accurate - answers. Glance is a Just-In-Time analytics
framework for large-scale file systems, which after consuming a small number of
disk accesses, is capable of producing extremely accurate answers for a broad
class of aggregate and top-k queries over a file system without the requirement
of any prior knowledge. Glance has
been accepted by USENIX FAST 2011.
Speaker Bio: Dr. Hao Howie Huang is an assistant
professor in Computer Engineering in Department of Electrical and Computer
Engineering at the George Washington University. His research interests are
data-intensive computing, file and storage systems, computer and systems
architecture, operating systems, Grid and Cloud Computing. His research has
been supported by grants from National Science Foundation (NSF), IBM, and GWU
Institute of Biomedical Engineering. Dr. Huang won an IBM Real Time Innovation
Faculty Award in 2008, and his research project, Amdahl Blades, is a
High-Performance Storage Challenge Finalist at Supercomputing (SC) 2009.
Dr. Huang received his Ph.D. in Computer Science
from the University of Virginia, and his B.S. in Computer Software from Wuhan
University China. He is a senior member of IEEE and member of ACM.
Email: howie@gwu.edu Web:
http://www.seas.gwu.edu/~howie/
