Abstract
Genomic sequencing is rapidly becoming a premier generator of Big Data, posing great computational challenges. Hence, acceleration of the algorithms used is of utmost importance. This paper presents a GPU-accelerated implementation of BWA-MEM, a widely used algorithm to map genomic sequences onto a reference genome. BWA-MEM contains three main computational functions: Seed Generation, Seed Extension and Output Generation. This paper discusses acceleration of the Seed Extension function on a GPU accelerator.
The GPU-based Extend kernel achieves three times higher performance and, by offloading the kernel onto an accelerator and overlapping its execution with the other functions, this results in an overall improvement to application-level execution time of up to 1.6x.
To ensure that using an accelerator always results in an overall performance improvement, especially when considering slower GPUs, an adaptive load balancing solution is introduced, which intelligently distributes work between host and GPU. This provides, compared to not using load balancing, up to +46 % more performance.
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Notes
- 1.
These numbers are obtained while executing the first 50,000 reads of the GCAT 150bp-se-small-indel data set using the nvprof and nvcc tools.
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Acknowledgments
The authors would like to thank the people at the Neuroscience Department of the Erasmus Medical Center for kindly granting access to their computing facilities for performance tests.
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Houtgast, E.J., Sima, VM., Bertels, K., Al-Ars, Z. (2016). GPU-Accelerated BWA-MEM Genomic Mapping Algorithm Using Adaptive Load Balancing. In: Hannig, F., Cardoso, J.M.P., Pionteck, T., Fey, D., Schröder-Preikschat, W., Teich, J. (eds) Architecture of Computing Systems – ARCS 2016. ARCS 2016. Lecture Notes in Computer Science(), vol 9637. Springer, Cham. https://doi.org/10.1007/978-3-319-30695-7_10
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