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Data Intensive Scalable Computing Applications

July 7, 2008
Time: 10:00 AM
Location: MSL Auditorium

Data analytics "at scale" become extremely difficult as dataset sizes
increase.  These tasks are data intensive in nature and obtain little
benefit from abundant computational resources.  Internet services
companies have developed systems and abstractions to support their
search business.  Frameworks such as Map-Reduce and BigTable using a
scalable distributed file systems are used to build applications to
process, index, and analyze web-scale datasets.
We are exploring an approach for using these frameworks as building
blocks for data-intensive scalable computing systems for science
(DISCS) that are easy to program and use.  In this talk I will show
how we have used Hadoop, an open-source implementation of Map-Reduce,
to build a couple of science applications.  This will serve as a
starting point for a dialogue about the needs and requirements of data
analytic science applications and what services and abstractions
should be provided by DISCS.
Julio Lopez is a Systems Scientist faculty in the Parallel Data
Laboratory (PDL) at Carnegie Mellon University.  He is interestede in
systems and applications for data intensive computing at large scale.
He is a member of the CMU Quake team, winners of the 2006
Supercomputing analytics challenge and 2003 Gordon Bell award.  His
work includes methods for compression of large seismic wavefields,
scalable I/O for ground motion simulations and indexing techniques for
multi-dimensional meshes.  He obtained his M.S. and Ph.D. in
Electrical and Computer Engineering from Carnegie Mellon University,
and his B.E. in Computer Systems from Universidad EAFIT in Medellín,

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