Orchestrating of Stream Programs by Genetic Algorithm
We are thankful to Almighty Allah for his blessings for the successful completion of our thesis. Our heartiest gratitude, profound indebtedness and deep respect go to our supervisor Dr. S. M. Farhad, Assistant Professor, BUET, Dhaka, Bangladesh, for his constant supervision, affectionate guidance an...
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Department of Computer Science and Engineering, Military Institute of Science and Technology
2015
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| On-line přístup: | http://hdl.handle.net/123456789/104 |
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| _version_ | 1868226999780114432 |
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| author | Ahmed Khan, S. M. Shameem Arif Hasan, Md. Shayed Hasan, Md. |
| author_browse | Ahmed Khan, S. M. Shameem Arif Hasan, Md. Shayed Hasan, Md. |
| author_facet | Ahmed Khan, S. M. Shameem Arif Hasan, Md. Shayed Hasan, Md. |
| author_sort | Ahmed Khan, S. M. Shameem |
| collection | DSpace |
| description | We are thankful to Almighty Allah for his blessings for the successful completion of our thesis. Our heartiest gratitude, profound indebtedness and deep respect go to our supervisor Dr. S. M. Farhad, Assistant Professor, BUET, Dhaka, Bangladesh, for his constant supervision, affectionate guidance and great encouragement and motivation. His keen interest on the topic and valuable advices throughout the study was of great help in completing thesis.
We are especially grateful to the Department of Computer Science and Engineering (CSE) of Military Institute of Science and Technology (MIST) for providing their all out support during the thesis work. Finally, we would like to thank our families and our course mates for their appreciable assistance, patience and suggestions during the course of our thesis. |
| format | Thesis |
| id | oai:localhost:123456789-104 |
| institution | My University |
| language | English |
| publishDate | 2015 |
| publishDateRange | 2015 |
| publishDateSort | 2015 |
| publisher | Department of Computer Science and Engineering, Military Institute of Science and Technology |
| publisherStr | Department of Computer Science and Engineering, Military Institute of Science and Technology |
| record_format | dspace |
| spelling | oai:localhost:123456789-1042015-06-28T04:03:50Z Orchestrating of Stream Programs by Genetic Algorithm Ahmed Khan, S. M. Shameem Arif Hasan, Md. Shayed Hasan, Md. Orchestrating, Stream, Programs, Genetic, Algorithm We are thankful to Almighty Allah for his blessings for the successful completion of our thesis. Our heartiest gratitude, profound indebtedness and deep respect go to our supervisor Dr. S. M. Farhad, Assistant Professor, BUET, Dhaka, Bangladesh, for his constant supervision, affectionate guidance and great encouragement and motivation. His keen interest on the topic and valuable advices throughout the study was of great help in completing thesis. We are especially grateful to the Department of Computer Science and Engineering (CSE) of Military Institute of Science and Technology (MIST) for providing their all out support during the thesis work. Finally, we would like to thank our families and our course mates for their appreciable assistance, patience and suggestions during the course of our thesis. Streamprogrammingonparallelhardwarehasbecomeubiquitous. Inthismodel,aprogram is executed on stream graph which consists of a set of actors performing different functions communicating through edges. It is difficult to schedule actors onto multicores balancing the workload because the assigned actor load can be overshadowed by the communication overhead of the edges. The proposed algorithm presents a heuristic technique to schedule the actors on multicores that balances the workload. Although the problem is time consuming and has much more computational overhead, this paper presents an efficient and effective technique that provides nearly optimal solution with minimum cost. We present a random partitioning steps based on Genetic Algorithm that assigns a set of actors to multicore processors in such a way that minimize the maximum total processing time on any processor. For facilitating understanding, an example of a stream graph is presented to visualize the chronological development of genetic algorithm. Our algorithm for scheduling actors in multicore is highly efficient and its results are accurate for standard number of actors and close to the optimal solution for larger number. For a range of stream graph considering the communication overhead a speedup of 3.87x on 4-Core, 5.72x on 6-Core and 7.59x on 8-Core architecture is achieved compared to a single core. Department of Computer Science and Engineering, Military Institute of Science and Technology 2015-06-28T04:03:50Z 2015-06-28T04:03:50Z 2013-12 Thesis http://hdl.handle.net/123456789/104 en Department of Computer Science and Engineering;TP- application/octet-stream Department of Computer Science and Engineering, Military Institute of Science and Technology |
| spellingShingle | Orchestrating, Stream, Programs, Genetic, Algorithm Ahmed Khan, S. M. Shameem Arif Hasan, Md. Shayed Hasan, Md. Orchestrating of Stream Programs by Genetic Algorithm |
| title | Orchestrating of Stream Programs by Genetic Algorithm |
| title_full | Orchestrating of Stream Programs by Genetic Algorithm |
| title_fullStr | Orchestrating of Stream Programs by Genetic Algorithm |
| title_full_unstemmed | Orchestrating of Stream Programs by Genetic Algorithm |
| title_short | Orchestrating of Stream Programs by Genetic Algorithm |
| title_sort | orchestrating of stream programs by genetic algorithm |
| topic | Orchestrating, Stream, Programs, Genetic, Algorithm |
| url | http://hdl.handle.net/123456789/104 |
| work_keys_str_mv | AT ahmedkhansmshameem orchestratingofstreamprogramsbygeneticalgorithm AT arifhasanmd orchestratingofstreamprogramsbygeneticalgorithm AT shayedhasanmd orchestratingofstreamprogramsbygeneticalgorithm |