A Multi GPU Read Alignment Algorithm with Model-Based Performance Optimization
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This paper describes a performance model for read alignment problem, one of the most computationally intensive tasks in bioinformatics. We adapted Burrows Wheeler transform based index to be used with GPUs to reduce overall memory footprint. A mathematical model of computation and communication costs was developed to find optimal memory partitioning for index and queries. Last we explored the possibility of using multiple GPUs to reduce data transfers and achieved super-linear speedup. Performance evaluation of experimental implementation supports our claims and shows more than 10fold performance gain per device.
KeywordsIndex Size Read Alignment Memory Footprint Multiple GPUs Short Read Alignment
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- 4.Gharaibeh, A., Ripeanu, M.: Size matters: Space/time tradeoffs to improve gpgpu applications performance. In: SC 2010 Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE Computer Society (2010)Google Scholar
- 7.Rothberg, J.M., Hinz, W., Rearick, T.M., et al.: An integrated semiconductor device enabling non-optical genome sequencing. Nature (475), 348–352 (2011)Google Scholar
- 8.Gusfield, D.: Algorithms on Strings, Trees and Sequences: Computer Science and Computational Biology. Cambridge University Press (1997)Google Scholar
- 9.Burrows, M., Wheeler, D.J.: A block-sorting lossless data compression algorithm. Technical Report 124, Digital Equipment Corporation (1994)Google Scholar
- 10.Langmead, B., Trapnell, C., Pop, M., Salzberg, S.L.: Ultrafast and memory-efficient alignment of short dna sequences to the human genome. Genome Biology 10(3), 10(25) (2009)Google Scholar
- 12.Chen, S., Jiang, H.: An exact matching approach for high throughput sequencing based on bwt and gpus. In: 2011 IEEE 14th International Conference on Computational Science and Engineering (CSE). IEEE Computer Society (2011)Google Scholar