Acceleration of image reconstruction in 3D Electrical Capacitance Tomography in heterogeneous, multi-GPU system using sparse matrix computations and Finite Element Method

Kapusta, Paweł; Majchrowicz, Michał; Sankowski, Dominik; Jackowska-Strumiłło, Lidia

doi:10.15439/2016f344

Cited by 8 publications

(8 citation statements)

References 5 publications

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“…Inverse problem is nonlinear and therefore two main groups of image reconstruction algorithms can be distinguished: nonlinear and linear. Non-linear algorithms are more accurate but slower [16]. Linear algorithms use approximate linear model, which is less accurate but simple and useful in engineering praxis.…”

Section: Image Reconstruction In Ectmentioning

confidence: 99%

“…In dynamic measurements of fast changing media, time of measurement and data processing is equally or even more important parameter than accuracy. Therefore different solutions accelerating these calculation have been reported in the past, especially dealing with parallel computing [11][12][13][14][15], sparse matrices and Finite Elements Method [16], Fourier-based sparse representations [17], neural networks approach [18,19], fuzzy logic [20] or field-programmable gate array (FPGA) implementation [21]. Usually a compromise between accuracy and rapidity of ECT measurement must be taken.…”

Section: Introductionmentioning

confidence: 99%

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Multi-GPU, Multi-Node Algorithms for Acceleration of Image Reconstruction in 3D Electrical Capacitance Tomography in Heterogeneous Distributed System

Majchrowicz

Kapusta

Jackowska-Strumiłło

et al. 2020

Sensors

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Electrical capacitance tomography (ECT) is one of non-invasive visualization techniques which can be used for industrial process monitoring. However, acquiring images trough 3D ECT often requires performing time consuming complex computations on large size matrices. Therefore, a new parallel approach for 3D ECT image reconstruction is proposed, which is based on application of multi-GPU, multi-node algorithms in heterogeneous distributed system. This solution allows to speed up the required data processing. Distributed measurement system with a new framework for parallel computing and a special plugin dedicated to ECT are presented in the paper. Computing system architecture and its main features are described. Both data distribution as well as transmission between the computing nodes are discussed. System performance was measured using LBP and the Landweber’s reconstruction algorithms which were implemented as a part of the ECT plugin. Application of the framework with a new network communication layer reduced data transfer times significantly and improved the overall system efficiency.

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Section: Image Reconstruction In Ectmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-GPU, Multi-Node Algorithms for Acceleration of Image Reconstruction in 3D Electrical Capacitance Tomography in Heterogeneous Distributed System

Majchrowicz

Kapusta

Jackowska-Strumiłło

et al. 2020

Sensors

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“…As a result of the earlier performed studies [8] algorithms used in ECT [9]. The earlier developed solution was based on the Xgrid platform, used as a network layer.…”

Section: Design Assumptionsmentioning

confidence: 99%

“…In order to achieve a high quality of 3D image, complex reconstruction algorithms performing many matrix calculations have to be applied. Therefore different solutions accelerating these calculation have been reported in the past by the Authors [8] [14], especially these dealing with sparse matrices and Finite Elements Method [9] as well as neural networks approach [5] [6] and even fuzzy logic [21].…”

Section: Introductionmentioning

confidence: 99%

Acceleration of 3D ECT image reconstruction in heterogeneous, multi-GPU, multi-node distributed system

Majchrowicz¹,

Kapusta²,

Jackowska-Strumiiio³

et al. 2018

Annals of Computer Science and Information Systems

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Electrical Capacitance Tomography (ECT) is an effective and non-invasive visualization technique, which is used in many industrial applications. Unfortunately, image reconstruction in 3D ECT is a complex computational task requiring operations on large size matrices. In this paper, a new approach to 3D ECT image reconstruction is proposed. A new heterogeneous, multi-GPU, multi-node distributed system has been developed, with a framework for parallel computing and a special plug-in dedicated to ECT.

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“…Superior performance was achieved in [20] when the algorithm uses the intrinsic fine parallelism ability of the FPGA. In addition, the substantial functioning of Graphics Processing Units (GPU) for arithmetically exhaustive algorithms drives the implementation of the 3D ECT system [21]. However, their performance is lower than the FPGA since it needs a lot of random off-chip memory access.…”

Section: Introductionmentioning

confidence: 99%

FPGA Implementation of ECT Digital System for Imaging Conductive Materials

Deabes

2019

Algorithms

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This paper presents the hardware implementation of a stand-alone Electrical Capacitance Tomography (ECT) system employing a Field Programmable Gate Array (FPGA). The image reconstruction algorithms of the ECT system demand intensive computation and fast processing of large number of measurements. The inner product of large vectors is the core of the majority of these algorithms. Therefore, a reconfigurable segmented parallel inner product architecture for the parallel matrix multiplication is proposed. In addition, hardware-software codesign targeting FPGA System-On-Chip (SoC) is applied to achieve high performance. The development of the hardware-software codesign is carried out via commercial tools to adjust the software algorithms and parameters of the system. The ECT system is used in this work to monitor the characteristic of the molten metal in the Lost Foam Casting (LFC) process. The hardware system consists of capacitive sensors, wireless nodes and FPGA module. The experimental results reveal high stability and accuracy when building the ECT system based on the FPGA architecture. The proposed system achieves high performance in terms of speed and small design density.

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Acceleration of image reconstruction in 3D Electrical Capacitance Tomography in heterogeneous, multi-GPU system using sparse matrix computations and Finite Element Method

Cited by 8 publications

References 5 publications

Multi-GPU, Multi-Node Algorithms for Acceleration of Image Reconstruction in 3D Electrical Capacitance Tomography in Heterogeneous Distributed System

Multi-GPU, Multi-Node Algorithms for Acceleration of Image Reconstruction in 3D Electrical Capacitance Tomography in Heterogeneous Distributed System

Acceleration of 3D ECT image reconstruction in heterogeneous, multi-GPU, multi-node distributed system

FPGA Implementation of ECT Digital System for Imaging Conductive Materials

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