连续变量量子密钥分发数据协调加速运算的GPU实现

doi:10.11959/j.issn.1000-436x.2017222

Abstract

Abstract:

For the low computing speed of reconciliation for current continuous variable quantum key distribution， CPU&GPU-parallel reconciliation algorithms was designed based on LDPC of SEC protocol to speed up decoding computing.In order to raise decoding speed without sacrifice reconciliation efficiency,a static two-way cross linked list to efficiently store large scale sparse parity matrix was employed.The simulation experimental results show that the speed of the decoding rate reaches 16.4 kbit/s when the channel SNR is over 4.9 dB and the reliability of the 2×10⁵continuous variable quantum sequence,with reconciliation efficiency of 91.71%.The experimental based on the Geforce GT 650 MB GPU and the 2.5 GHz and 8 GB memory CPU hardware platform.Relative to the only CPU platform,computing speed increased by more than 15 times.

Key words: continuous variable quantum key distribution, reconciliation, low density parity check code, static linked list,GPU decoding

CLC Number:

TN918.91

Shao-ting LIU,Xiao-kai WANG,Da-bo GUO. Accelerated computational implementation of reconciliation for continuous variable quantum key distribution on GPU[J]. Journal on Communications, 2017, 38(11): 171-177.

Figures/Tables 6

References 17

[1]	BLOCH M , THANGARAJ A , MCLAUGHLIN S W . Efficient reconciliation of correlated continuous random variables using LDPC codes[J]. Arxiv preprint cs, 2005:0509041.
[2]	BRASSARD G , SALVAIL L . Secret-key reconciliation by public discussion[C]// The Workshop on Advances in Cryptology-Eurocrypt. 1993: 410-423.
[3]	NGUYEN K C , ASSCHE G V , CERF N J . Side-information coding with turbo codes and its application to quantum key distribution[J]. Mathematics, 2004.
[4]	LODEWYCK J , BLOCH R , GARCIA-PATRON R ,et al. Quantum key distribution over 25 km with an all-fiber continuous-variable system[J]. Physical Review A, 2007,76(4): 538-538.
[5]	LIN D K , HUANG D , HUANG P ,et al. High performance reconciliation for continuous-variable quantum key distribution with LDPC code[J]. International Journal of Quantum Information, 2015,13(2): 1550010.
[6]	GOLUB G H , VAN L , CHARLES F . Matrix Computations[M]. 北京: 科学出版社, 2009.
	GOLUB G H , VAN L , CHARLES F . Matrix Computations[M]. Beijing: Science Bress, 2009.
[7]	郭大波, 刘纲, 张宁 . 量子高斯密钥分发的逆向数据协调[J]. 量子光学学报, 2013,19(3): 219-226.
	GUO D B , LIU G , ZHANG N ,et al. Reverse Reconciliation of Quantum Gaussian Distributed Key[J]. Acta Sinica Quantum Optica, 2013,19(3): 219-226.
[8]	郭大波, 张彦煌, 王云艳 . 高斯量子密钥分发数据协调的性能优化[J]. 光学学报, 2014,34(1): 225-231.
	GUO D B , ZHANG Y H , WANG Y Y . Performance optimization for the reconciliation of Gaussian quantum key distribution[J]. Acta Optica Sinica, 2014,34(1): 225-231.
[9]	VAN ASSCHE G , CARDINAL J , CERF N J . Reconciliation of a quantum-distributed Gaussian key[J]. Information Theory IEEE Transactions on, 2002,50(2): 394-400.
[10]	白增亮, 王旭阳, 杜鹏燕 ,等. 连续变量量子密钥分发的数据逆向协调[J]. 量子光学学报, 2012,18(1): 23-26.
	BAI Z L , WANG X Y , DU P Y ,et al. Reverse reconciliation for continuous variable quantum key distribution[J]. Acta Sinica Quantum Optica, 2012,18(1): 23-26.
[11]	仇德元 . GPGPU编程技术—从GLSL、CUDA、到OpenCL[M]. 北京: 机械工业出版社, 2011.
	QIU D Y . GPGPU programming technology-from GLSL,CUDA,to OpenCL[M]. Beijing: China Maehine Press, 2011.
[12]	郭大波, 张宁, 刘纲 . 基于Turbo码的量子高斯密钥分发的数据协调[J]. 量子光学学报, 2013,19(1): 32-38.
	GUO D B , ZHANG N , LIU G . Reconciliation of quantum Gaussian distributed key based on Turbo codes[J]. Acta Sinica Quantum Optica, 2013,19(1): 32-38.
[13]	DAVID B K , WEN M , HWU W . 大规模并行处理器编程实战[M]. 北京: 清华大学出版社, 2013.
	DAVID B K , WEN M , HWU W . Programming massively parallel processor[M]. Beijing: Tsinghua University Press, 2013.
[14]	JABER BORRAN M , AAZHANG B . Multilevel codes and iterative multistage decoding:rate design rules and practical considerations[C]// Wireless Communications and Networking Conference, 2000: 36-41.
[15]	WACHSMANN U , FISCHER R F H , HUBER J B . Multilevel codes:theoretical concepts and practical design rules[J]. IEEE Transactions on Information Theory, 1999,45(5): 1361-1391.
[16]	BLOCH M , THANGARAJ A , MCLAUGHLIN S W ,et al. LDPC-based secret key agreement over the Gaussian wiretap channel[C]// IEEE International Symposium on Information Theory. 2006: 1179-1183.
[17]	MACKAY D J C , NEAL R M . Near Shannon limit performance of low density parity check codes[J]. Electronics Letters, 1997,33(6): 457-458.

Metrics

Recommended 0

No Suggested Reading articles found!

地址	排序	索引
A[0]	2	6
A[1]	4	2
A[2]	5	7
A[3]	8	NULL
A[4]	1	0
A[5]	7	3
A[6]	3	1
A[7]	6	5

方案	码长	每一级码率	收敛信噪比R_SN/dB	100次迭代的时间/s	译码速率/(kbit·s^?1)	协调效率
GPU并行	200 000	0/0/0.3/0.95	4.9	12.28	16.4	0.917 1
仅CPU	200 000	0/0/0.3/0.95	4.9	185.76	1.07	0.917 1
上海交大^[5]	10 000	未报道	未报道	未报道	25 000	—

Accelerated computational implementation of reconciliation for continuous variable quantum key distribution on GPU

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 6

References 17

Related Articles 2

Metrics

Recommended 0

[1]	Kang-ning SUN,Lin-hua MA,Le RU,Wen-tong FAN,Xing HU,Shao-cheng HUANG. Analysis of stability condition for LDPC codes and optimizing degree sequences over mixed channel [J]. Journal on Communications, 2016, 37(9): 168-174.
[2]	Xiao-mei TIAN,Da-fang ZHANG,Kun XIE,Can HU,Xiao-bo YANG,Chang-qiong SHI. Set reconciliation based on counting Bloom filters [J]. Journal on Communications, 2012, 33(8): 119-127.