The participants in these work- hoo! have made large-scale clusters
shops are primarily researchers. The available to the academic community
workshopsalsoinvolverepresentatives for education and research, and the
of funding agencies—critical to transi- National Science Foundation has an-tioning research visions into funded nounced its CluE (Cluster Exploratory)
programs. Often they also involve in- research initiative. There is no magic
dustrial participants. A recent example here—it takes dedicated individuals to
of success is CCC’s “Big Data Comput- make things happen. But CCC can be
ing Study Group.” In late March 2008, an enabler.
two workshops were held in Sunnyvale, A number of other CCC activities are
CA. The first was the “Hadoop Sum- described on CCC’s Web site, which
mit,” whose goal was to build a com- includes descriptions of various grand
munity of users of Hadoop, an open- challengeproblemsandablogdevoted
source version of Google’s MapReduce to discussions of research visions for
system1 for distributing computations the field. More broadly, CCC is work-across clusters of thousands of nodes. ing along with other organizations to
The second was the “Data-Intensive “get the word out” regarding our field.
Scalable Computing Symposium,” I encourage you to become engaged.
whose goal was to build a community Participate in the CCC research visions
of researchers concerned with various blog ( www.cccblog.org/). Join with col-issues related to “big-data computing” leagues to propose a workshop to chart
(slides, videos, and summaries are a compelling vision for future of your
linked from the CCC Web site; www. subfield.
cra.org/ccc/). Both of these communi- Computer science has accom-ty-building exercises were successful. plished so much, and there is so much
And, as a result of preliminary work additional exciting work to do. The op-
TOdIotn_ehablyft_hpeagceo_re4Cg_rmoaurpkso.fpodrfg:aLnaiyzeorust 1por6t/u2n6i/ti0e8s ar3e:t2ru8lyPeMxtra Poar gdein1ary. It’s
of this effort, Google, IBM, and Ya- up to us to seize these opportunities.
References
1. Dean, J. and Ghemawat, S. MapReduce: Simplified
data processing on large clusters. In Proceedings of
the Sixth Symposium on Operating System Design and
Implementation (OSDI ’04), (San Francisco, CA, Dec.
2004); labs.google.com/papers/mapreduce.html.
2. Evolving the High Performance Computing and
Communications Initiative to Support the nation’s
Information Infrastructure. Computer Science
and Telecommunications Board, National Research
Council, 1995; www7.national-academies.org/cstb/
pub_hpcci.html.
3. Grand Challenges for Engineering. National Academy
of Engineering; www.engineeringchallenges.org.
4. Innovation in Information Technology. Computer
Science and Telecommunications Board, National
Research Council, 2003; www7.national-academies.
org/cstb/ pub_itinnovation.html.
5. The Computing Community Consortium: Who,
what, when, where, why, and how. Computing
Research news 20, 1 (Jan. 2008); www.cra.org/CRN/
issues/0801.pdf.
Ed Lazowska ( lazowska@cs.washington.edu) is the Bill &
Melinda Gates Chair in Computer Science & Engineering
at the University of Washington and the chair of the
Computing Community Consortium.
© 2008 ACM 0001-0782/08/0800 $5.00
ACM Transactions on
Internet Technology
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