GENERAL
CHAIR |
Layne
Watson, Virginia Tech |
PROGRAM
CO-CHAIRS |
Tomasz Haupt, Mississippi State University |
Cliff Shaffer,
Virginia Tech |
Masha Sosonkina,
Ames Laboratory |
IMPORTANT DATES |
Paper submission due:
December 1, 2006
Acceptance Notification: December 22, 2006
Revised manuscript due: January 15, 2007
Registration packet due: January 29, 2007
Symposium: March 26-29, 2007 |
FURTHER INFORMATION |
Please
contact the General Chair:
Layne Watson <ltw@cs.vt.edu> |
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HPC 2007 Keynote Address
Mathematical Software for High-end Computational Science and
Engineering
David Keyes
Fu Foundation Professor of Applied Mathematics,
Columbia University
Multiscale, multirate scientific and engineering applications based
on systems of partial differential equations possess resolution
requirements that are typically inexhaustible and demand execution
on the highest-capability computers available, which will soon
reach the petascale. While the variety of applications is enormous,
their needs for mathematical software infrastructure are
surprisingly coincident. Domains with complex geometry require
versatile meshing and discretization tools. Resolution requirements
that evolve with the solution require dynamic adaptivity. Implicit
methods for stable and accurate integration of transient problems
and efficient treatments for equilibrium problems lead to large,
ill-conditioned algebraic systems that must be solved with an
algorithmic complexity that is close to linear in problem size or
storage complexity. Distributed memory architectures demand
efficient means of creating and managing load-balanced partitions
of unstructured objects. These and other algorithmic challenges
that are generic to nearly all mesh- and particle-based
applications are addressed in the SciDAC Institute and Centers for
Enabling Technologies in mathematics, which we briefly overview in
this talk.
The chief to bottleneck to scalability is often the solver. At their
current scalability limits, many applications spend a vast majority
of their operations in solvers, due to solver algorithmic
complexity that is superlinear in the problem size, whereas other
phases scale linearly. Furthermore, the solver may be the phase of
the simulation with the poorest parallel scalability, due to
intrinsic global dependencies. The Towards Optimal PDE Simulations
(TOPS) center focuses on relieving this bottleneck while providing
a multilevel programming interface that allows users to advance
from initial concerns of correctness and robustness to ultimate
concerns of efficiency and performance portability.
Copyright ©SCS, 2007 all rights reserved |
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