Exploration of Alternative GPU Implementations of the Pair-HMMs Forward Algorithm

Exploration of Alternative GPU Implementations of the Pair-HMMs Forward Algorithm

Ren, S. (TU Delft Computer Engineering)
Bertels, K.L.M. (TU Delft Quantum & Computer Engineering)
Al-Ars, Z. (TU Delft Computer Engineering)

Quantum & Computer Engineering

2016

In order to handle the massive raw data generated by next generation sequencing (NGS) platforms, GPUs are widely used by many genetic analysis tools to speed up the used algorithms. In this paper, we use GPUs to accelerate the pair-HMMs forward algorithm, which is used to calculate the overall alignment probability in many genomics analysis tools. We firstly evaluate two different implementation methods to accelerate the pair-HMMs forward algorithm according to their effectiveness on GPU platforms. Based on these two methods, we present several implementations of the pair-HMMs forward algorithm.
We execute these implementations on the NVIDIA Tesla K40 card using different datasets to compare the performance. Experimental results show that the intra-task implementation has the highest throughput in most cases, achieving pure computational throughput as high as 23.56 GCUPS for synthetic datasets.
On a real dataset, the inter-task implementation achieves 4.82x speedup compared with a vectorized implementation executed on a 20-core POWER8 system.

http://resolver.tudelft.nl/uuid:e1703289-7f50-4cef-9e0d-5621a66918ed

https://doi.org/10.1109/BIBM.2016.7822645

Proceedings 3rd International Workshop on High Performance Computing on Bioinformatics

The 3rd International Workshop on High Performance Computing on Bioinformatics (HPCB 2016), 2016-12-15 → 2016-12-18, Shenzhen, China

Institutional Repository

conference paper

© 2016 S. Ren, K.L.M. Bertels, Z. Al-Ars