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Filter Decomposition for Supporting Coarse-Grained Pipelined Parallelism
 

Summary: Filter Decomposition for Supporting Coarse-Grained
Pipelined Parallelism
Wei Du Gagan Agrawal
Department of Computer Science and Engineering, Ohio State University, Columbus OH 43210
duw@cse.ohio-state.edu, agrawal@cse.ohio-state.edu
ABSTRACT
We consider the filter decomposition problem in supporting coarse-grained
pipelined parallelism. This form of parallelism is suitable for data-driven
applications in scenarios where the data is available on a repository or a data
collection site on the internet, and the final results are required on a user's
desktop. A filter decomposition algorithm takes an application divided into
a sequence of atomic filters, and maps them into a given number of filters.
We propose three polynomial time algorithms for this problem. Dynamic
programming algorithm MIN ONETRIP optimizes the one trip cost for a
packet passing through the pipeline. MIN BOTTLENECK is also a dy-
namic programming algorithm, which minimizes the time spent on the bot-
tleneck stage. Finally, MIN TOTAL is an approximate greedy algorithm
which tries to minimize the total execution time.
The results show that our heuristic algorithms work quite well in prac-
tice, with the possible exception of MIN ONETRIP when the number of

  

Source: Agrawal, Gagan - Department of Computer Science and Engineering, Ohio State University

 

Collections: Computer Technologies and Information Sciences