Asynchronous Parallel GPU Acceleration Method Based on Total Variation Minimization Model

Fig. 3. Original image and reconstruction results of middle slices at iteration of 2000 times. (a) Original image; (b) result of experiment 1; (c) result of experiment 2; (d) result of sync-parallel computing in experiment 3; (e) result of async-parallel computing in experiment 3; (f) result of sync-parallel computing in experiment 4; (g) result of async-parallel computing in experiment 4

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Table 1. Parameters of reconstruction acceleration experimental platform

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Table 1. Parameters of reconstruction acceleration experimental platform

Item	Computing server model
Item	GPU computing server I	GPU computing server II
CPU	Intel IvyBridgeE5-2630v2 (2.6 GHz)	Intel IvyBridgeE5-2630v2 (2.6 GHz)
GPU	Tesla K20 (5 G)×1	Tesla K20 (5 G)×1Tesla K40 (12 G)×1
RAM	32 G	32 G
Operating system	Windows 7, 64 bit	Windows 7, 64 bit
Number	2	2

Table 2. Scanning parameters of cone-beam CT system

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Table 2. Scanning parameters of cone-beam CT system

Item	Parameter
Scanning angle range /(°)	0-360
Number of probes	1024×1024
Distance from source to rotation center /mm	600
Distance from source to detector /mm	1200
Projection number	60
Total of projection data	1024×1024×60
Reconstruction scale	512×512×512
Pixel size /mm×mm	0.25×0.25

Table 3. RMSE of results at iteration of 2000 times and average iteration time of each iteration for four experiments

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Table 3. RMSE of results at iteration of 2000 times and average iteration time of each iteration for four experiments

Result	Experiment 1	Experiment 2	Experiment 3		Experiment 4
Result	Experiment 1	Experiment 2	Synchronous parallel	Asynchronous parallel	Synchronous parallel	Asynchronous parallel
RMSE	6.78×10^-4	6.78×10^-4	6.78×10^-4	5.69×10^-4	6.78×10^-4	5.34×10^-4
Average single time /s	6842.00	130.50	53.93	51.49	53.26	45.67
$\begin{matrix} {R_{speed}}_{} \end{matrix}$ (compared with CPU)	-	52.4	126.9	132.9	128.5	149.8
$\begin{matrix} {R_{speed}}_{} \end{matrix}$ (compared with single GPU)	-	-	2.42	2.53	2.45	2.86

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Wanli Lu, Ailong Cai, Zhizhong Zheng, Linyuan Wang, Lei Li, Bin Yan. Asynchronous Parallel GPU Acceleration Method Based on Total Variation Minimization Model[J]. Acta Optica Sinica, 2018, 38(4): 0411004

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Paper Information

Category: Imaging Systems

Received: Apr. 5, 2017

Accepted: --

Published Online: Jul. 10, 2018

The Author Email: Yan Bin (ybspace@hotmail.com)

DOI:10.3788/AOS201838.0411004

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