Parallelisation Of Maximal Patterns Finding Algorithm In Biological Sequences
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Date
2008-06
Authors
Issa Hussein, Ahmad Mohd Aziz
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Abstract
Rapid increased of the biological data opens up new challenges for scientist
to discover new methods to manage, analyse and understand them. A computer
scientist is of help in taking up these challenges by producing efficient and fast
algorithms. One of the methods in analyzing these biological data is by looking at the
maximal patterns that exists in the data. Maximal patterns in biological sequences are
important for revealing the relationship among biological sequences. These maximal
patterns can be used to build indexes for faster searching. In this research, we used
parallel methods to improve the speed of an existing maximal pattern finding
algorithm, TEIRESIAS. There are two phases in the algorithm, which are the
scanning and convolution phase. The first phase detects short patterns in the
biological data and the second phase combines the short patterns into longer patterns
without sacrificing the meaning. The output will be maximal patterns. The first phase
of the algorithm is very compute intensive. We improve the overall process of
finding maximal patterns by decomposing the biological database and distribute it to
be input to TEIRESIAS algorithm. We applied the master-slave model and used
OpenMP to implement the model. Our results show that there the performance
decreased when we used 8 threads and our results show that there are 1.6 time and
2.0 times improvement in term the overall speed of the algorithm when we used two
threads and four.
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Keywords
Parallelisation Of Maximal Patterns , In Biological Sequences