Heterogeneous multi-core systems for bioinformatics
The bioinformatics research area is now faced with an obstacle of ever-increasing biological data to verify their biological discovery. As data increases, so does the workload for managing, processing and analysing this data. Combined with the inherent complexity of biological problems, traditional...
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sg-ntu-dr.10356-420962023-03-04T00:44:43Z Heterogeneous multi-core systems for bioinformatics Adrianto Wirawan Bertil Schmidt Kwoh Chee Keong School of Computer Engineering Bioinformatics Research Centre DRNTU::Engineering::Computer science and engineering::Computer applications::Life and medical sciences The bioinformatics research area is now faced with an obstacle of ever-increasing biological data to verify their biological discovery. As data increases, so does the workload for managing, processing and analysing this data. Combined with the inherent complexity of biological problems, traditional approaches results in long run-time and huge memory requirements. The emergence of accelerator technologies such as multi-core architectures provides the opportunity to achieve significant improvements in execution time for many bioinformatics applications, compared to sequential general-purpose platforms. Using multi-cores to solve large scale bioinformatics applications, such as sequence analysis, is therefore a promising and challenging research field, since large-scale computational bioinformatics problems can benefit much from this kind of processing power. DOCTOR OF PHILOSOPHY (SCE) 2010-09-16T07:28:39Z 2010-09-16T07:28:39Z 2010 2010 Thesis Adrianto, W. (2010). Heterogeneous multi-core systems for bioinformatics. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/42096 10.32657/10356/42096 en 188 p. application/pdf |
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DRNTU::Engineering::Computer science and engineering::Computer applications::Life and medical sciences Adrianto Wirawan Heterogeneous multi-core systems for bioinformatics |
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The bioinformatics research area is now faced with an obstacle of ever-increasing biological data to verify their biological discovery. As data increases, so does the workload for managing, processing and analysing this data. Combined with the inherent complexity of biological problems, traditional approaches results in long run-time and huge memory requirements. The emergence of accelerator technologies such as multi-core architectures provides the opportunity to achieve significant improvements in execution time for many bioinformatics applications, compared to sequential general-purpose platforms. Using multi-cores to solve large scale bioinformatics applications, such as sequence analysis, is therefore a promising and challenging research field, since large-scale computational bioinformatics problems can benefit much from this kind of processing power. |
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Bertil Schmidt |
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Bertil Schmidt Adrianto Wirawan |
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Theses and Dissertations |
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Adrianto Wirawan |
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Adrianto Wirawan |
title |
Heterogeneous multi-core systems for bioinformatics |
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Heterogeneous multi-core systems for bioinformatics |
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Heterogeneous multi-core systems for bioinformatics |
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Heterogeneous multi-core systems for bioinformatics |
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Heterogeneous multi-core systems for bioinformatics |
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heterogeneous multi-core systems for bioinformatics |
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2010 |
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https://hdl.handle.net/10356/42096 |
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