Data processing performance analysis for ultrafast electron beam X-ray CT using parallel processing hardware architectures

被引:12
作者
Bieberle, A. [1 ]
Frust, T. [1 ]
Wagner, M. [2 ]
Bieberle, M. [2 ]
Hampel, U. [1 ,2 ]
机构
[1] Helmholtz Zentrum Dresden Rossendorf, Inst Fluid Dynam, D-01314 Dresden, Germany
[2] Tech Univ Dresden, AREVA Endowed Chair Imaging Tech Energy & Proc En, Dresden, Germany
关键词
Computed tomography; Many-core graphics processing units; Multi-core central processing units; Massive parallel data processing; HOLD-UP; RECONSTRUCTION; IMPLEMENTATION; ALGORITHM;
D O I
10.1016/j.flowmeasinst.2016.04.004
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
The ultrafast electron beam X-ray computed tomography (CT) measuring system of the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) is primarily operated for fundamental multiphase flow investigations, e.g. in various technical devices, and for validation of enhanced flow simulation models, e.g. developed for computational fluid dynamic codes (CFD). The CT scanner delivers cross-sectional material distributions by contactless measurements with a spatial resolution of approximately 1 mm and a temporal resolution of maximal 8 kHz. Currently, two central time-consuming processes have been identified limiting the efficient usage of that worldwide unique CT technique: a) the data transfer from the detector system to central data storages (e.g. computer or data base) and b) the data processing. Thus, data pre-processing and data reconstruction algorithms have been adapted for the use at multi-core central processing units (CPUs) and even many-core graphics processing units (GPUs). For optimal data processing results an advanced performance PC with two parallel operated high performance graphics processing units, a six-core processor, a high internal data bus speed and a large memory block has been assembled. The newly developed data processing algorithms induce a performance improvement of approximately 137 for the entire data processing sequence compared to the previous universally applicable single core CPU based data processing tool. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:180 / 188
页数:9
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