[Fwd: [Cs-tenure-faculty] [Coe.faculty] CS Colloquium, Monday, 12/3, Markus Pueschel, Carnegie Mellon University]

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You are invited to the

Drexel University Computer Science Colloquium

Can We Teach Computers To Write Fast Libraries?

Markus Pueschel
Electrical and Computer Engineering Department
Carnegie Mellon University

Date:
Monday, December 3, 2007
11:000 AM

Location:
Hill Conference Room, Lebow 240

NOTE: Lunch will be served after the talk

Abstract:
As the computing world "goes multicore," high performance
library development finally becomes a nightmare. Optimal
programs, and their underlying algorithms, have to be
adapted to take full advantage of the platform's parallelism,
memory hierarchy, and available instruction set. To make
things worse, the best implementations are often platform-
dependent and platforms are constantly evolving, which quickly
renders libraries obsolete. As a consequence, developers are
forced to permanently re-implement and re-optimize the same
functionality and often even revert to assembly coding just as
50 years ago.

A number of research efforts have started to address this
problem in a new area called Automatic Performance Tuning with
the common goal to rethink the way libraries are created. In
this talk we present Spiral (www.spiral.net), a program
generation system for linear transforms. Spiral generates
highly optimized, platform-tuned implementations of transforms
directly from a problem specification. For a user-specified
transform, Spiral generates alternative algorithms, optimizes
them, compiles them into programs, and "intelligently"
searches for the best match to the computing platform. The
main idea behind Spiral is a mathematical, declarative
framework to represent algorithms and the use of rewriting
systems to generate and optimize algorithms at a high level
of abstraction. Optimization includes parallelization for
vector architectures, shared and distributed memory platforms,
GPUs, and even FPGAs. Experimental results show that the code
generated by Spiral competes with, and sometimes outperforms,
the best available human-written library code. Further,
recent research shows that it may be possible to extend
Spiral into other domains such as coding or linear algebra.
As for the question in the title: Spiral shows that, at least
for well-understood problem domains, a positive answer may be
in reach.

Bio:
Markus Pueschel is an Associate Research Professor of
Electrical and Computer Engineering at Carnegie Mellon
University. He received his Diploma (M.Sc.) in Mathematics
and his Doctorate (Ph.D.) in Computer Science, in 1995 and
1998, respectively, both from the University of Karlsruhe,
Germany. From 1998-1999 he was a Postdoctoral Researcher at
Mathematics and Computer Science, Drexel University. Since
2000 he has been with Carnegie Mellon University. He is an
Associate Editor for the IEEE Transactions on Signal
Processing, and was a Guest Editor of the Journal of Symbolic
Computation, the Proceedings of the IEEE, and an Associate
Editor for the IEEE Signal Processing Letters. He is a
recipient of the Outstanding Research Award of the College of
Engineering at Carnegie Mellon and holds the title of
Privatdozent at the University of Technology, Vienna,
Austria. His research interests include computing,
algorithms, applied mathematics, and signal processing
theory/software/hardware. More information is available at
www.ece.cmu.edu/~pueschel.
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Received on Fri Nov 30 10:19:26 2007