[Talks] Brown CS Thesis Defense: Jose Castanos talk in Lubrano on 4/25/2000 at 2:30 pm

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			  CS Thesis Defense
		  The Department of Computer Science
			   BROWN UNIVERSITY

			       presents

			    Jose Castanos
			    

		  Tuesday, April 25, 2000 at 2:30 PM

	          Lubrano Conference Room (CIT 4th floor)

	          

	    ``Parallel Adaptive Unstructured Computation''

			       Abstract

Adaptive computation offers the potential to provide large storage and
computational savings on problems with dissimilar scales by focusing
the available computational resources on the regions where the
solution changes rapidly. Many static problems have regions of high
physical activity embedded in large domains in which the solution is
smooth. In transient problems, the regions of interest can appear or
vanish, and modify their size, shape or location, as occurs in the
study of turbulence in fluid flows. In all cases, it is necessary to
``adapt'' the mesh to follow the physical anomalies so that regions of
high gradients are not under-resolved while maintaining a coarser mesh
everywhere else.  Unfortunately, adaptivity significantly increases
the complexity of algorithms and software.

In this talk we describe the problems that arise when adaptive schemes
are used in a parallel computing environment. We address the
difficulties of designing parallel adaptation methods by introducing a
new parallel mesh refinement algorithm based on Rivara's bisection
algorithm, for triangular and tetrahedral meshes. We have shown that
this algorithm generates the same refined meshes in the serial and
parallel cases.  Because adaptive methods produce imbalances in the
work assigned to processors they provide an excellent context for the
study of dynamic load balancing schemes on distributed memory parallel
computers. We present a new parallel graph partitioning algorithm that
has its roots in multilevel algorithms.  Compared with other
partitioning heuristics, our method greatly reduces the movement of
data between processors as a result of small changes in the mesh, by
amounts close to those predicted by analysis. We have also designed a
data migration procedure to rebalance the work between processors.

To evaluate and test these ideas we have developed and implemented a
parallel adaptive system, called PARED, for the solution of PDEs by
the finite element method. PARED is an object system that executes in
distributed memory machines such as the IBM RS6000/SP and networks of
workstations. PARED supports the creation of dynamic distributed data
structures. Our system also provides a communications library built on
top of MPI that allows for an efficient exchange of objects between
processors.

  
		 Host: Professor John Hughes