Parallelisation Of Maximal Patterns Finding Algorithm In Biological Sequences
| dc.contributor.author | Issa Hussein, Ahmad Mohd Aziz | |
| dc.date.accessioned | 2016-10-05T01:52:12Z | |
| dc.date.available | 2016-10-05T01:52:12Z | |
| dc.date.issued | 2008-06 | |
| dc.description.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. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/2672 | |
| dc.subject | Parallelisation Of Maximal Patterns | en_US |
| dc.subject | In Biological Sequences | en_US |
| dc.title | Parallelisation Of Maximal Patterns Finding Algorithm In Biological Sequences | en_US |
| dc.type | Thesis | en_US |
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