GPU-based acceleration of an RNA tertiary structure prediction algorithm

Yongkweon Jeon, Eesuk Jung, Hyeyoung Min, Eui Young Chung, Sungroh Yoon

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

Experimental techniques such as X-ray crystallography and nuclear magnetic resonance have been useful for the accurate determination of RNA tertiary structures. However, high-throughput structure determination using such methods often becomes difficult, due to the need for a large quantity of pure samples. Computational techniques for the prediction of RNA tertiary structures are thus becoming increasingly popular. Most of the existing prediction algorithms are computationally intensive, and there is a clear need for acceleration. In this paper, we propose a parallelization methodology for the fragment assembly of RNA (FARNA) algorithm, one of the most effective methods for computational prediction of RNA tertiary structure. The proposed parallelization scheme exploits multi-core CPUs and GPUs in harmony to maximize their utilization. We tested our approach with a number of RNA sequences and confirmed that it allows the time required for structure prediction to be significantly reduced. With respect to the baseline architecture equipped with a single CPU core, we achieved a speedup of up to approximately 24×(roughly 4 × by multi-core CPUs and 20 × by GPUs). Compared with a quad-core CPU setup, the proposed approach delivers an additional 12 × speedup by utilizing GPU devices. Given that most PCs these days have a multi-core CPU and a GPU card, our methodology will be very helpful for accelerating algorithms in a cost-effective manner.

Original languageEnglish
Pages (from-to)1011-1022
Number of pages12
JournalComputers in Biology and Medicine
Volume43
Issue number8
DOIs
Publication statusPublished - 2013 Sep 1

Fingerprint

RNA
Program processors
X Ray Crystallography
X ray crystallography
Magnetic Resonance Spectroscopy
Costs and Cost Analysis
Equipment and Supplies
Throughput
Nuclear magnetic resonance
Graphics processing unit
Costs

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Health Informatics

Cite this

Jeon, Yongkweon ; Jung, Eesuk ; Min, Hyeyoung ; Chung, Eui Young ; Yoon, Sungroh. / GPU-based acceleration of an RNA tertiary structure prediction algorithm. In: Computers in Biology and Medicine. 2013 ; Vol. 43, No. 8. pp. 1011-1022.
@article{920d6d12ddae43bba6f13a9564468206,
title = "GPU-based acceleration of an RNA tertiary structure prediction algorithm",
abstract = "Experimental techniques such as X-ray crystallography and nuclear magnetic resonance have been useful for the accurate determination of RNA tertiary structures. However, high-throughput structure determination using such methods often becomes difficult, due to the need for a large quantity of pure samples. Computational techniques for the prediction of RNA tertiary structures are thus becoming increasingly popular. Most of the existing prediction algorithms are computationally intensive, and there is a clear need for acceleration. In this paper, we propose a parallelization methodology for the fragment assembly of RNA (FARNA) algorithm, one of the most effective methods for computational prediction of RNA tertiary structure. The proposed parallelization scheme exploits multi-core CPUs and GPUs in harmony to maximize their utilization. We tested our approach with a number of RNA sequences and confirmed that it allows the time required for structure prediction to be significantly reduced. With respect to the baseline architecture equipped with a single CPU core, we achieved a speedup of up to approximately 24×(roughly 4 × by multi-core CPUs and 20 × by GPUs). Compared with a quad-core CPU setup, the proposed approach delivers an additional 12 × speedup by utilizing GPU devices. Given that most PCs these days have a multi-core CPU and a GPU card, our methodology will be very helpful for accelerating algorithms in a cost-effective manner.",
author = "Yongkweon Jeon and Eesuk Jung and Hyeyoung Min and Chung, {Eui Young} and Sungroh Yoon",
year = "2013",
month = "9",
day = "1",
doi = "10.1016/j.compbiomed.2013.05.007",
language = "English",
volume = "43",
pages = "1011--1022",
journal = "Computers in Biology and Medicine",
issn = "0010-4825",
publisher = "Elsevier Limited",
number = "8",

}

GPU-based acceleration of an RNA tertiary structure prediction algorithm. / Jeon, Yongkweon; Jung, Eesuk; Min, Hyeyoung; Chung, Eui Young; Yoon, Sungroh.

In: Computers in Biology and Medicine, Vol. 43, No. 8, 01.09.2013, p. 1011-1022.

Research output: Contribution to journalArticle

TY - JOUR

T1 - GPU-based acceleration of an RNA tertiary structure prediction algorithm

AU - Jeon, Yongkweon

AU - Jung, Eesuk

AU - Min, Hyeyoung

AU - Chung, Eui Young

AU - Yoon, Sungroh

PY - 2013/9/1

Y1 - 2013/9/1

N2 - Experimental techniques such as X-ray crystallography and nuclear magnetic resonance have been useful for the accurate determination of RNA tertiary structures. However, high-throughput structure determination using such methods often becomes difficult, due to the need for a large quantity of pure samples. Computational techniques for the prediction of RNA tertiary structures are thus becoming increasingly popular. Most of the existing prediction algorithms are computationally intensive, and there is a clear need for acceleration. In this paper, we propose a parallelization methodology for the fragment assembly of RNA (FARNA) algorithm, one of the most effective methods for computational prediction of RNA tertiary structure. The proposed parallelization scheme exploits multi-core CPUs and GPUs in harmony to maximize their utilization. We tested our approach with a number of RNA sequences and confirmed that it allows the time required for structure prediction to be significantly reduced. With respect to the baseline architecture equipped with a single CPU core, we achieved a speedup of up to approximately 24×(roughly 4 × by multi-core CPUs and 20 × by GPUs). Compared with a quad-core CPU setup, the proposed approach delivers an additional 12 × speedup by utilizing GPU devices. Given that most PCs these days have a multi-core CPU and a GPU card, our methodology will be very helpful for accelerating algorithms in a cost-effective manner.

AB - Experimental techniques such as X-ray crystallography and nuclear magnetic resonance have been useful for the accurate determination of RNA tertiary structures. However, high-throughput structure determination using such methods often becomes difficult, due to the need for a large quantity of pure samples. Computational techniques for the prediction of RNA tertiary structures are thus becoming increasingly popular. Most of the existing prediction algorithms are computationally intensive, and there is a clear need for acceleration. In this paper, we propose a parallelization methodology for the fragment assembly of RNA (FARNA) algorithm, one of the most effective methods for computational prediction of RNA tertiary structure. The proposed parallelization scheme exploits multi-core CPUs and GPUs in harmony to maximize their utilization. We tested our approach with a number of RNA sequences and confirmed that it allows the time required for structure prediction to be significantly reduced. With respect to the baseline architecture equipped with a single CPU core, we achieved a speedup of up to approximately 24×(roughly 4 × by multi-core CPUs and 20 × by GPUs). Compared with a quad-core CPU setup, the proposed approach delivers an additional 12 × speedup by utilizing GPU devices. Given that most PCs these days have a multi-core CPU and a GPU card, our methodology will be very helpful for accelerating algorithms in a cost-effective manner.

UR - http://www.scopus.com/inward/record.url?scp=84879185554&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84879185554&partnerID=8YFLogxK

U2 - 10.1016/j.compbiomed.2013.05.007

DO - 10.1016/j.compbiomed.2013.05.007

M3 - Article

VL - 43

SP - 1011

EP - 1022

JO - Computers in Biology and Medicine

JF - Computers in Biology and Medicine

SN - 0010-4825

IS - 8

ER -