``Auto-CFD: efficiently parallelizing CFD applications on clusters"

Li Xiao, Xiaodong Zhang, Zhengqian Kuan, Baiming Feng, and Jichang Kang

Proceedings of IEEE International Conference on Cluster Computing
(Cluster2003), Hong Kong, China, December 1-4, 2003.

Abstract

Computational Fluid Dynamics (CFD) applications are highly demanding
for parallel computing. Many such applications have been shifted
from expensive MPP boxes to cost-effective clusters and Networks of
Workstations (NOW).  Auto-CFD is a pre-compiler which transforms
Fortran CFD sequential programs to efficient message-passing
parallel programs running on NOW. Our work has the following three
unique contributions. First, this pre-compiler is highly automatic,
requiring a minimum number of user directives for parallelization.
Second, we have applied a dependency analysis technique for the CFD
applications, called analysis after partitioning. We propose a
mirror-image decomposition technique to parallelize self-dependent
field loops that are hard to parallelize by existing methods.  Finally,
traditional optimizations of communication focus on eliminating
redundant synchronizations. We have developed an optimization scheme
which combines all the non-redundant synchronizations in CFD programs
to further reduce the communication overhead. The Auto-CFD has been
implemented on networks of workstations and has been successfully used
for automatically parallelizing structured CFD application programs. Our
experiments show its effectiveness and scalability for parallelizing
large CFD applications.

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