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Author:

Lin, C.-Y. (Lin, C.-Y..) | Hung, C.-L. (Hung, C.-L..) | Wang, C.-H. (Wang, C.-H..) (Scholars:王朝辉) | Su, M. (Su, M..) | Tan, J. (Tan, J..)

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Scopus

Abstract:

Current high-end graphics processing units (abbreviate to GPUs), such as NVIDIA Tesla, Fermi, Kepler series cards which contain up to thousand cores per-chip, are widely used in the high performance computing fields. These GPU cards (called desktop GPUs) should be installed in personal computers/servers with desktop CPUs; moreover, the cost and power consumption of constructing a high performance computing platform with these desktop CPUs and GPUs are high. NVIDIA releases Tegra K1, called Jetson TK1, which contains 4 ARM Cortex-A15 CPUs and 192 CUDA cores (Kepler GPU) and is an embedded board with low cost, low power consumption and high applicability advantages for embedded applications. NVIDIA Jetson TK1 becomes a new research direction. Hence, in this paper, a bioinformatics platform was constructed based on NVIDIA Jetson TK1. ClustalWtk and MCCtk tools for sequence alignment and compound comparison were designed on this platform, respectively. Moreover, the web and mobile services for these two tools with user friendly interfaces also were provided. The experimental results showed that the cost-performance ratio by NVIDIA Jetson TK1 is higher than that by Intel XEON E5-2650 CPU and NVIDIA Tesla K20m GPU card. Copyright © 2015, IGI Global.

Keyword:

Compound Comparison; CUDA; Mutliple Sequence Alignment; NVIDIA Jetson TK1; Parallel Processing

Author Community:

  • [ 1 ] [Lin, C.-Y.]Chang Gung University, Taoyuan City, Taiwan
  • [ 2 ] [Hung, C.-L.]Department of Computer Science and Communication Engineering, Providence University, Taichung, Taiwan
  • [ 3 ] [Wang, C.-H.]Chang Gung University, Taoyuan City, Taiwan
  • [ 4 ] [Su, M.]Chang Gung University, Taoyuan City, Taiwan
  • [ 5 ] [Tan, J.]Beijing University of Technology, Beijing, China

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Source :

International Journal of Grid and High Performance Computing

ISSN: 1938-0259

Year: 2015

Issue: 4

Volume: 7

Page: 57-73

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 3

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 12

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