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Quantum Computer Simulation on GPU Cluster Incorporating Data Locality

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Cloud Computing and Security (ICCCS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10602))

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Abstract

Quantum computer simulation provides researchers with tools for verification of quantum algorithms. GPU (Graphics Processing Units) cluster is an advisable platform for this task. However, the high cost of communication between GPUs makes the simulation inefficiency. To overcome this drawback, we propose the following two methods. (1) A method for GPU cluster quantum simulation to improve the data locality is introduced, and two schemes for data exchanging are proposed. (2) A novel data distribution method for quantum computer simulation on GPU cluster is proposed. Experimental results show that the simulation of 33-qubit Quantum Fourier Transform algorithm using 4 nodes outperforms the serial program of the CPU cluster with a speedup of 129 times.

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Acknowledgments

This work was supported by Funding of National Natural Science Foundation of China (Grant Nos. 61571226), Natural Science Foundation of Jiangsu Province, China (Grant Nos. BK20140823).

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Authors and Affiliations

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Zhen Li & Jiabin Yuan

Authors
  1. Zhen Li
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  2. Jiabin Yuan
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Corresponding author

Correspondence to Zhen Li .

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Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. National Dong Hwa University, Shoufeng, Taiwan

    Han-Chieh Chao

  3. China Information Technology Security Evaluation Center, Nanjing, China

    Xingang You

  4. Department of Computer Science, Purdue University, West Lafayette, Indiana, USA

    Elisa Bertino

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Li, Z., Yuan, J. (2017). Quantum Computer Simulation on GPU Cluster Incorporating Data Locality. In: Sun, X., Chao, HC., You, X., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2017. Lecture Notes in Computer Science(), vol 10602. Springer, Cham. https://doi.org/10.1007/978-3-319-68505-2_8

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  • DOI: https://doi.org/10.1007/978-3-319-68505-2_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68504-5

  • Online ISBN: 978-3-319-68505-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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