Enhancing the Performance of a Microarray Gridding Algorithm via GPU Computing Techniques

Stamos Katsigiannis, Eleni Zacharia, Dimitris Maroulis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

cDNA microarrays are a useful tool for studying the expression levels of genes. Nevertheless, microarray image gridding remains a challenging and complex task. Most of the microarray image analysis tools require human intervention, leading to variations of the gene expression results. Automatic methods have also been proposed, but present high computational complexity. In this work, the performance enhancement via GPU computing techniques of a fully automatic gridding method, previously proposed by the authors' research group, is presented. The NVIDIA CUDA architecture was utilized in order to achieve parallel computation of complex steps of the algorithm. Experimental results showed that the proposed approach provides enhanced performance in terms of computational time, while achieving higher utilization of the available computational resources.
Original languageEnglish
Title of host publicationBioinformatics and Bioengineering (BIBE), 2013 IEEE 13th International Conference on
PublisherIEEE
ISBN (Electronic)978-1-4799-3163-7
ISBN (Print)978-1-4799-3164-4
DOIs
Publication statusPublished - 2013
Externally publishedYes

Publication series

Name
PublisherIEEE
ISSN (Print)2471-7819

Cite this

Katsigiannis, S., Zacharia, E., & Maroulis, D. (2013). Enhancing the Performance of a Microarray Gridding Algorithm via GPU Computing Techniques. In Bioinformatics and Bioengineering (BIBE), 2013 IEEE 13th International Conference on IEEE. https://doi.org/10.1109/BIBE.2013.6701689
Katsigiannis, Stamos ; Zacharia, Eleni ; Maroulis, Dimitris. / Enhancing the Performance of a Microarray Gridding Algorithm via GPU Computing Techniques. Bioinformatics and Bioengineering (BIBE), 2013 IEEE 13th International Conference on. IEEE, 2013.
@inproceedings{3251e27a01f44e1fa94e5e409120b0cc,
title = "Enhancing the Performance of a Microarray Gridding Algorithm via GPU Computing Techniques",
abstract = "cDNA microarrays are a useful tool for studying the expression levels of genes. Nevertheless, microarray image gridding remains a challenging and complex task. Most of the microarray image analysis tools require human intervention, leading to variations of the gene expression results. Automatic methods have also been proposed, but present high computational complexity. In this work, the performance enhancement via GPU computing techniques of a fully automatic gridding method, previously proposed by the authors' research group, is presented. The NVIDIA CUDA architecture was utilized in order to achieve parallel computation of complex steps of the algorithm. Experimental results showed that the proposed approach provides enhanced performance in terms of computational time, while achieving higher utilization of the available computational resources.",
author = "Stamos Katsigiannis and Eleni Zacharia and Dimitris Maroulis",
year = "2013",
doi = "10.1109/BIBE.2013.6701689",
language = "English",
isbn = "978-1-4799-3164-4",
publisher = "IEEE",
booktitle = "Bioinformatics and Bioengineering (BIBE), 2013 IEEE 13th International Conference on",
address = "United States",

}

Katsigiannis, S, Zacharia, E & Maroulis, D 2013, Enhancing the Performance of a Microarray Gridding Algorithm via GPU Computing Techniques. in Bioinformatics and Bioengineering (BIBE), 2013 IEEE 13th International Conference on. IEEE. https://doi.org/10.1109/BIBE.2013.6701689

Enhancing the Performance of a Microarray Gridding Algorithm via GPU Computing Techniques. / Katsigiannis, Stamos; Zacharia, Eleni; Maroulis, Dimitris.

Bioinformatics and Bioengineering (BIBE), 2013 IEEE 13th International Conference on. IEEE, 2013.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Enhancing the Performance of a Microarray Gridding Algorithm via GPU Computing Techniques

AU - Katsigiannis, Stamos

AU - Zacharia, Eleni

AU - Maroulis, Dimitris

PY - 2013

Y1 - 2013

N2 - cDNA microarrays are a useful tool for studying the expression levels of genes. Nevertheless, microarray image gridding remains a challenging and complex task. Most of the microarray image analysis tools require human intervention, leading to variations of the gene expression results. Automatic methods have also been proposed, but present high computational complexity. In this work, the performance enhancement via GPU computing techniques of a fully automatic gridding method, previously proposed by the authors' research group, is presented. The NVIDIA CUDA architecture was utilized in order to achieve parallel computation of complex steps of the algorithm. Experimental results showed that the proposed approach provides enhanced performance in terms of computational time, while achieving higher utilization of the available computational resources.

AB - cDNA microarrays are a useful tool for studying the expression levels of genes. Nevertheless, microarray image gridding remains a challenging and complex task. Most of the microarray image analysis tools require human intervention, leading to variations of the gene expression results. Automatic methods have also been proposed, but present high computational complexity. In this work, the performance enhancement via GPU computing techniques of a fully automatic gridding method, previously proposed by the authors' research group, is presented. The NVIDIA CUDA architecture was utilized in order to achieve parallel computation of complex steps of the algorithm. Experimental results showed that the proposed approach provides enhanced performance in terms of computational time, while achieving higher utilization of the available computational resources.

U2 - 10.1109/BIBE.2013.6701689

DO - 10.1109/BIBE.2013.6701689

M3 - Conference contribution

SN - 978-1-4799-3164-4

BT - Bioinformatics and Bioengineering (BIBE), 2013 IEEE 13th International Conference on

PB - IEEE

ER -

Katsigiannis S, Zacharia E, Maroulis D. Enhancing the Performance of a Microarray Gridding Algorithm via GPU Computing Techniques. In Bioinformatics and Bioengineering (BIBE), 2013 IEEE 13th International Conference on. IEEE. 2013 https://doi.org/10.1109/BIBE.2013.6701689