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Quantum Computer Simulation on Multi-GPU Incorporating Data Locality

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Algorithms and Architectures for Parallel Processing (ICA3PP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9528))

Abstract

Quantum computer simulation (QCS) provides an effective platform for the development and validation of quantum algorithms. The exponential runtime overhead limits the simulation scale on classical computers which makes advisable the use of Graphics Processing Units. However, simulating quantum computers on multi-GPU has poor performance due to low data locality and frequent data transfer. Here, we propose a novel implemental scheme for QCS on multi-GPU. Our implementation addresses the aforementioned challenges by (i) an efficient data distribution method enhancing high data locality on each GPU global memory and (ii) an assignment function for the threads mapping to each GPU memory space achieving high bandwidth and data reuse for multiple quantum gates. Experimental results show that the simulation of 29-qubit Quantum Fourier Transform algorithm using four NVIDIA K20c GPUs gains a performance ratio of 358, compared to the sequential implementation of released libquantum, along with a parallel efficiency of 0.92.

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Authors and Affiliations

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Pei Zhang, Jiabin Yuan & Xiangwen Lu

Authors
  1. Pei Zhang
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  2. Jiabin Yuan
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  3. Xiangwen Lu
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Corresponding author

Correspondence to Pei Zhang .

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Editors and Affiliations

  1. Central South University, Changsha, China

    Guojun Wang

  2. The University of Sydney, Sydney, New South Wales, Australia

    Albert Zomaya

  3. University of Murcia, Murcia, Murcia, Spain

    Gregorio Martinez

  4. Hunan University , Changsha, China

    Kenli Li

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© 2015 Springer International Publishing Switzerland

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Zhang, P., Yuan, J., Lu, X. (2015). Quantum Computer Simulation on Multi-GPU Incorporating Data Locality. In: Wang, G., Zomaya, A., Martinez, G., Li, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2015. Lecture Notes in Computer Science(), vol 9528. Springer, Cham. https://doi.org/10.1007/978-3-319-27119-4_17

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  • DOI: https://doi.org/10.1007/978-3-319-27119-4_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27118-7

  • Online ISBN: 978-3-319-27119-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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