Cart (Loading....) | Create Account
Close category search window
 

GPU Acceleration of Pyrosequencing Noise Removal

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Yang Gao ; Dept. of Comput. Sci. & Eng., Univ. of South Carolina, Columbia, SC, USA ; Bakos, J.D.

Amplicon Noise [1], an updated version of Py-ronoise [2], is a tool for removing noise from metagenomic data recorded by a 454 pyrosequencer. Amplicon Noise has shown to be effective in reducing overestimation of operational taxonomic units (OTUs) and chimera detection. Amplicon-Noise's noise removal method relies on clustering a large set of short sequences read by the sequencer. The DNA sequencing algorithm requires the computation of O(n2 ) pair wise distances using a global sequence alignment method. Each sequence consists of a few hundred base pairs and a typical dataset contains 104 sequences, making the clustering computation extremely expensive. In this paper we describe of GPU kernel implementation of the most computationally expensive module in the Amplicon Noise software package, SeqDist. With our GPU workstation (Intel Core i7 980 @ 3.33GHz + 3 x NVIDIATesla C2070) and a typical 454 dataset, our implementation achieves a 8.6X (CUDA-SeqDist) speedup with a single GPU when compared with a 12 MPI ranks of the original tools running on the CPU alone. With three GPUs, we achieve a2.1X further speedup over the single GPU version, yielding a total speedup of 18.3X. We measure the throughput of our kernel to be 1.4 giga floating-point cell updates per second(GFCUPS) with a single GPU and 2.9 GFCUPS with 3 GPUs, where GFCUPS refers to the unique method by which the score matrix must be updated in the specialized alignment algorithm used in Amplicon Noise.

Published in:

Application Accelerators in High Performance Computing (SAAHPC), 2012 Symposium on

Date of Conference:

10-11 July 2012

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.