TITLE: Data-Intensive Computing and CS Education
AUTHOR: Eugene Wallingford
DATE: March 26, 2008  7:27 AM
DESC: 
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BODY:
An article in the March 2008 issue of
<A HREF="http://www.cra.org/CRN/">
   Computing Research News</A>
describes a relatively new partnership among NSF, Google,
and IBM to help the academic computing "explore innovative
research and education ideas in data-intensive computing".
They define data-intensive computing a new paradigm in
which the size of the data dominates all other performance
features.  Google's database of the web is one example, but
so are terabytes and petabytes of scientific data collected
from satellites and earth-bound sensors.  On the hardware
side of the equation, we need to understand better how to
assemble clusters of computers to operate on the data and
how to network them effectively.  Just as important is the
need to develop programming abstractions, languages, and
tools that are powerful enough so that we mortals can grasp
and solve problems at this massive scale. Google's Map-Reduce
algorithm (an idea adapted from the functional programming
world) is just a start in this direction.

This notion of data-intensive computing came up in two of
the plenary addresses at the recent
<A HREF="http://www.cs.uni.edu/~wallingf/blog/archives/monthly/2008-03.html#e2008-03-13T19_22_31.htm">
   SIGCSE conference</A>.
Not surprisingly, one was the talk by Google's
<A HREF="http://www.cs.uni.edu/~wallingf/blog/archives/monthly/2008-03.html#e2008-03-20T16_21_45.htm">
   Marissa Mayer</A>,
who encouraged CS educators to think about how we can help
our students prepare to work within this paradigm.  The
second was the banquet address by
<A HREF="http://lazowska.cs.washington.edu/">
   Ed Lazowska</A>,
the chair of Washington's Computer Science department.
Lazowska's focus was more on the need for research into
the hardware and software issues that undergird computing
on massive data sets.  (My notes on Lazowska's talk are
still in the works.)

This recurring theme is one of the reasons that our Hot
Topic group at
<A HREF="http://www.cs.uni.edu/~wallingf/blog/archives/monthly/2008-03.html#e2008-03-19T00_40_23.htm">
   ChiliPLoP</A>
began its work on the assembly and preparation of large
data sets for use in early programming courses.  What
counts as "large" for a freshman surely differs from what
counts as "large" for Google, but we can certainly begin
to develop a sense of scale in our students' minds as they
write code and see the consequences of their algorithms
and implementations.  Students already experience large
data in their lives, with 160 GB video iPods in their
pockets.  Having them compute on such large sets should
be a natural step.

The Computing Research News also has an announcement of
a meeting of the Big-Data Computing Study Group, which is
holding a one-day Data-Intensive Computing Symposium today
in Sunnyvale.  I don't know how much of this symposium will
report new research results and how much will share background
among the players, in order to forge working relationships.
I hope that someone writes up the results of the symposium
for the rest of us...

Though our ChiliPLoP group ended up working on a different
project this year, I expect that several of us will continue
with the idea, and it may even be a theme for us at a future
ChiliPLoP.  The project that we worked on instead --
designing a radically different undergraduate computer
science degree program -- has some currency, though, too.
In this same issue of the CRN, CRA board chair Dan Reed
talks about the importance of innovation in computing and
computing education:

<BLOCKQUOTE><EM>
As we debate the possible effects of an economic downturn,
it is even more important that we articulate -- clearly and
forcefully -- the importance of computing innovation and
education as economic engines.

[...  T]he CRA has created a new computing education committee
... whose charge is to think broadly about the future of
computing education.  We cannot continue the infinite addition
of layers to the computing curriculum onion that was defined
in the 1970s.  I believe we need to rethink some of our
fundamental assumptions about computing education approaches
and content.
</EM></BLOCKQUOTE>

Rethink fundamental assumptions and start from a fresh point
of view is just what we proposed.  We'll have to share our
work with Reed and the CRA.
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