Documents

  • 10238082

    Accepted author manuscript, 380 KB, PDF-document

DOI

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.
Original languageEnglish
Title of host publicationProceedings 3rd International Workshop on High Performance Computing on Bioinformatics
Pages1-8
Number of pages8
DOIs
Publication statusPublished - 2016
EventThe 3rd International Workshop on High Performance Computing on Bioinformatics (HPCB 2016) - Shenzhen, China
Duration: 15 Dec 201618 Dec 2016

Workshop

WorkshopThe 3rd International Workshop on High Performance Computing on Bioinformatics (HPCB 2016)
Abbreviated titleHPCB 2016
CountryChina
CityShenzhen
Period15/12/1618/12/16

ID: 10238082