Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (12): 36-42.doi: 10.11896/jsjkx.201200023

• Computer Architecture • Previous Articles     Next Articles

Quantum Fourier Transform Simulation Based on “Songshan” Supercomputer System

XIE Jing-ming1, HU Wei-fang1, HAN Lin2, ZHAO Rong-cai2, JING Li-na3   

  1. 1 Information Engineering Institute,Zhengzhou University,Zhengzhou 450000,China
    2 Henan Supercomputing Center,Zhengzhou University,Zhengzhou 450000,China
    3 Zhong Yuan Network Security Research Institute,Zhengzhou University,Zhengzhou 450001,China
  • Received:2020-12-02 Revised:2021-03-19 Online:2021-12-15 Published:2021-11-26
  • About author:XIE Jing-ming,born in 1995,postgra-duate.His main research interests include high-performance parallel computation and heterogeneous computation.
    HAN Lin,born in 1978,Ph.D,associate professor,master instructor,is a member of China Computer Federation.His main research interests include high-performance computing and compilation optimization.
  • Supported by:
    National Key R & D Program of China(2018YFB0505000).

PDF (PC)

1176

Abstract: The “Songshan” supercomputer system is a new generation of heterogeneous supercomputer cluster independently developed by China.The CPU and DCU accelerators it carries are all independently developed by my country.In order to expand the scientific computing ecology of the platform and verify the feasibility of quantum computing research on this platform,the paper uses a heterogeneous programming model to implement a heterogeneous version of the quantum Fourier transform simulation on the “Songshan” supercomputer system.The computing hotspots of the program are allocated to run on the DCU;then MPI is used to enable multiple processes on a single computing node to realize the concurrent data transmission and calculation of the DCU accelerator;finally,the hiding of computing and communication prevents the DCU from being in the middle of data transmission.The experiment implements a 44 Qubits-scale quantum Fourier transform simulation on a supercomputing system for the first time.The results show that the heterogeneous version of the quantum Fourier transform module makes full use of the computing resources of the DCU accelerator and achieves 11.594 compared to the traditional CPU version.The speedup ratio is high,and it has good scalability on the cluster.This implementation method provides a reference for the simulation implementation and optimization of other quantum algorithms on the “Songshan” supercomputer system.

Key words: Communication hiding, DCU accelerator, Heterogeneous computing, HIP-C, MPI, Quantum Fourier transform

CLC Number: 

  • TP387
[1]GIBNEY E.Quantum computer race intensifies as alternative technology gains steam[J].Nature,2020,587(7834):342-343.
[2]CHO A.Google claims quantum computing milestone[J]. Science,2019,365(6460):1364.
[3]LLOYD S,GARNERONE S,ZANARDI P.Quantum algorithms for topological and geometric analysis of data[J].Nature Communications,2016,7:10138.
[4]ZHOU S S,LOKE T,IZAAC J A,et al.Quantum Fourier transform in computational basis[J].Quantum Information Proces-sing,2017,16(3):1-19.
[5] LIU X N,JING L N.Large scale Quantum Fourier Transform Simulation Based on SW26010[J].Computer Science,2020,47(8):93-97.
[6]LIU X,YANG Z,YANG Y.A nested split load balancing algorithm for Tianhe No.2[J].Computer Research and Development,2018,55(2):418-425.
[7]BAKHODA A,YUAN G L,FUNG W W L,et al.Analyzing CUDA workloads using a detailed gpu simulator[C]//the 2009 IEEE International Symposium on Performance Analysis of Systems and Software.2009.
[8]JOHN C.Professional CUDAC Programming[M].Wiley Inter Science,2014.
[9]GUPTA S,BABU M R.Generating Performance Analysis of GPU Compared to Single-core and Multi-core CPU for Natural Language Applications[J].International Journal of Advanced Computer Science and Applications,2011,2(5):50-53.
[10]CHENG S Y.Research on performance evaluation and optimization technology of heterogeneous(CPU-GPU) computer systems[D].National University of Defense Technology,2011.
[11]HASANIJAFARI S,PARSAMEHR S.Solving the Fourier Transform Issue Using Quantum Coherent States[J].International Journal of Theoretical Physics,2019,58(8):2407-2413.
[12]LIU X,GUO H,SUN R J,et al.The Characteristic Analysis and Exascale Scalability Research of Large Scale Parallel Applications on Sunway Taihulight Supercomputer[J].Journal of Computer,2018,14(10):2209-2220.
[13]YE C,ZHENG S G,LONG C,et al.Quantum Fourier Transform and Phase Estimation in Qudit System[J].Communications in Theoretical Physics,2011,55(5):790-794.
[14]COURTNEY D.The guide to CUDA[M].Create Space Independent Publishing Platform,2015.
[15]YU Q,CHILDERS B,HUANG L,et al.A quantitative evaluation of unified memory in GPUs[J].The Journal of Supercomputing,2020,76(2):2958-2985.
[16]SEREN S,CAN Ö.Integer programming based heterogeneous CPU-GPU cluster schedulers for SLURM resource manager[J].Journal of Computer and System Sciences,2015,81(1):38-56.
[17]FORUM M P.MPI:A Message-Passing Interface Standard[J]. Intl J of Supercomputing Applications,1994,8(2):179.
[18]WANG Y H,QIAO J Z,LIN S K, et al. An Optimization Stra- tegy for Improving Throughput of GPU Global Memory[J].Journal of Grey System,2018,30(2):42-56.
[1] LU Hao-song, HU Yong-hua, WANG Shu-ying, ZHOU Xin-lian, LI Hui-xiang. Study on Hybrid Resource Heuristic Loop Unrolling Factor Selection Method Based on Vector DSP [J]. Computer Science, 2022, 49(6A): 777-783.
[2] FENG Yan, WANG Rui-cong. Quantum Voting Protocol Based on Quantum Fourier Transform Summation [J]. Computer Science, 2022, 49(5): 311-317.
[3] LIU Jiang, LIU Wen-bo, ZHANG Ju. Hybrid MPI+OpenMP Parallel Method on Polyhedral Grid Generation in OpenFoam [J]. Computer Science, 2022, 49(3): 3-10.
[4] CHI Hao-yu, CHEN Chang-bo. Survey on Automatic Tuning of Compilers by Machine Learning [J]. Computer Science, 2022, 49(1): 241-251.
[5] GAO Shi-shun, ZHAO Hai-tao, ZHANG Xiao-ying, WEI Ji-bo. Self-adaptive Intelligent Wireless Propagation Model to Different Scenarios [J]. Computer Science, 2021, 48(7): 324-332.
[6] TANG Zhen, HU Yong-hua, LU Hao-song, WANG Shu-ying. Research on DSP Register Pairs Allocation Algorithm with Weak Assigning Constraints [J]. Computer Science, 2021, 48(6A): 587-595.
[7] HU Wei-fang, CHEN Yun, LI Ying-ying, SHANG Jian-dong. Loop Fusion Strategy Based on Data Reuse Analysis in Polyhedral Compilation [J]. Computer Science, 2021, 48(12): 49-58.
[8] JIANG Hua-nan, ZHANG Shuai, LIN Yu-fei, LI Hao. Simulation Optimization and Testing Based on Gazebo of MPI Distributed Parallelism [J]. Computer Science, 2021, 48(11A): 672-677.
[9] ZHANG Ning, FANG Jing-wen, ZHAO Yu-xuan. Bitcoin Price Forecast Based on Mixed LSTM Model [J]. Computer Science, 2021, 48(11A): 39-45.
[10] HU Teng, WANG Yan-ping, ZHANG Xiao-song, NIU Wei-na. Data and Behavior Analysis of Blockchain-based DApp [J]. Computer Science, 2021, 48(11): 116-123.
[11] YANG Ping, WANG Sheng-yuan. Analysis of Target Code Generation Mechanism of CompCert Compiler [J]. Computer Science, 2020, 47(9): 17-23.
[12] HAN Lei, HU Jian-peng. Deduplication Algorithm of Abstract Syntax Tree in GCC Based on Trie Tree of Keywords [J]. Computer Science, 2020, 47(9): 47-51.
[13] YANG Wang-dong, WANG Hao-tian, ZHANG Yu-feng, LIN Sheng-le, CAI Qin-yun. Survey of Heterogeneous Hybrid Parallel Computing [J]. Computer Science, 2020, 47(8): 5-16.
[14] GUO Jie, GAO Xi-ran, CHEN Li, FU You, LIU Ying. Parallelizing Multigrid Application Using Data-driven Programming Model [J]. Computer Science, 2020, 47(8): 32-40.
[15] LI Yu-rong, LIU Jie, LIU Ya-lin, GONG Chun-ye, WANG Yong. Parallel Algorithm of Deep Transductive Non-negative Matrix Factorization for Speech Separation [J]. Computer Science, 2020, 47(8): 49-55.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.