Speeding-up Bioinformatics Algorithms with Heterogeneous Architectures: Highly Heterogeneous Smith-Waterman (HHeterSW)
The Smith-Waterman algorithm has a great sensitivity when used for biological sequence-database searches, but at the expense of high computing-power requirements. To overcome this problem, there are implementations in literature that exploit the different hardware-architectures available in a standard PC, such as GPU, CPU, and coprocessors. We introduce an application that splits the original database-search problem into smaller parts, resolves each of them by executing the most efficient implementations of the Smith-Waterman algorithms in different hardware architectures, and finally unifies the generated results. Using non-overlapping hardware allows simultaneous execution, and up to 2.58-fold performance gain, when compared with any other algorithm to search sequence databases. Even the performance of the popular BLAST heuristic is exceeded in 78% of the tests. The application has been tested with standard hardware: Intel i7-4820K CPU, Intel Xeon Phi 31S1P coprocessors, and nVidia GeForce GTX 960 graphics cards. An important increase in performance has been obtained in a wide range of situations, effectively exploiting the available hardware.
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | artículo biblioteca |
| Language: | English |
| Published: |
Mary Ann Liebert
2016-10
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| Subjects: | Balance load, Bioinformatics, Coarse-grained parallelization, Cuda, Non-overlapping hardware, Many-core, Sequence alignment, Xeon Phi, |
| Online Access: | http://hdl.handle.net/10261/158379 http://dx.doi.org/10.13039/501100003329 http://dx.doi.org/10.13039/100007652 http://dx.doi.org/10.13039/501100011011 |
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