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 language | English |
|---|---|
| Pages (from-to) | 1011-1022 |
| Number of pages | 12 |
| Journal | Computers in Biology and Medicine |
| Volume | 43 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - 2013 Sep 1 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Health Informatics
Cite this
}
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 journal › Article
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
C2 - 23816173
AN - SCOPUS:84879185554
VL - 43
SP - 1011
EP - 1022
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
SN - 0010-4825
IS - 8
ER -