UFO: A Scalable GPU-based Image Processing Framework for On-line Monitoring

Vogelgesang, Matthias; Chilingaryan, S.; Rolo, Tomy dos_Santos; Kopmann, A.

doi:10.1109/hpcc.2012.116

Cited by 63 publications

(43 citation statements)

References 13 publications

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“…Real-time experimental data analysis [49][50][51] and steering have been active research areas [52,53]. ASTRA is a popular GPU-based toolkit for processing and reconstruction of x-ray data [51].…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Accelerated Light Source Experiments

Liu

Biçer

Kettimuthu

et al. 2019

2019 IEEE/ACM Third Workshop on Deep Learning on Supercomputers (DLS)

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Experimental protocols at synchrotron light sources typically process and validate data only after an experiment has completed, which can lead to undetected errors and cannot enable online steering. Real-time data analysis can enable both detection of, and recovery from, errors, and optimization of data acquisition. However, modern scientific instruments, such as detectors at synchrotron light sources, can generate data at GBs/sec rates. Data processing methods such as the widely used computational tomography usually require considerable computational resources, and yield poor quality reconstructions in the early stages of data acquisition when available views are sparse. We describe here how a deep convolutional neural network can be integrated into the real-time streaming tomography pipeline to enable better-quality images in the early stages of data acquisition. Compared with conventional streaming tomography processing, our method can significantly improve tomography image quality, deliver comparable images using only 32% of the data needed for conventional streaming processing, and save 68% experiment time for data acquisition.

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Section: Related Workmentioning

confidence: 99%

Deep Learning Accelerated Light Source Experiments

Liu

Biçer

Kettimuthu

et al. 2019

2019 IEEE/ACM Third Workshop on Deep Learning on Supercomputers (DLS)

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“…However, iterative approaches typically require more computational throughput (Sidky et al, 2006;Agulleiro & Fernandez, 2011;Treibig et al, 2013;Beister et al, 2012). Accelerators, such as GPUs, have been used to improve performance of iterative algorithms (Chilingaryan et al, 2010;Mirone et al, 2014;Brun et al, 2015;Vogelgesang et al, 2012), but memory limitations of these accelerators can introduce significant overheads due to the data movement from host to device memory. The TomoPy framework provides a complete set of tools for the portable analysis of tomography datasets, which can run on workstations (Gü rsoy et al, 2014).…”

Section: Related Workmentioning

confidence: 99%

Optimization of tomographic reconstruction workflows on geographically distributed resources

Biçer

Gürsoy

Kettimuthu

et al. 2016

J Synchrotron Radiat

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New technological advancements in synchrotron light sources enable data acquisitions at unprecedented levels. This emergent trend affects not only the size of the generated data but also the need for larger computational resources. Although beamline scientists and users have access to local computational resources, these are typically limited and can result in extended execution times. Applications that are based on iterative processing as in tomographic reconstruction methods require high-performance compute clusters for timely analysis of data. Here, time-sensitive analysis and processing of Advanced Photon Source data on geographically distributed resources are focused on. Two main challenges are considered: (i) modeling of the performance of tomographic reconstruction workflows and (ii) transparent execution of these workflows on distributed resources. For the former, three main stages are considered: (i) data transfer between storage and computational resources, (i) wait/queue time of reconstruction jobs at compute resources, and (iii) computation of reconstruction tasks. These performance models allow evaluation and estimation of the execution time of any given iterative tomographic reconstruction workflow that runs on geographically distributed resources. For the latter challenge, a workflow management system is built, which can automate the execution of workflows and minimize the user interaction with the underlying infrastructure. The system utilizes Globus to perform secure and efficient data transfer operations. The proposed models and the workflow management system are evaluated by using three high-performance computing and two storage resources, all of which are geographically distributed. Workflows were created with different computational requirements using two compute-intensive tomographic reconstruction algorithms. Experimental evaluation shows that the proposed models and system can be used for selecting the optimum resources, which in turn can provide up to 3.13Â speedup (on experimented resources). Moreover, the error rates of the models range between 2.1 and 23.3% (considering workflow execution times), where the accuracy of the model estimations increases with higher computational demands in reconstruction tasks.

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“…A low-level C library called libuca [22] has been developed to generalize low latency access to 2D pixel detectors from multiple vendors, including our custom smart camera based on the ALPS driver. Because there is already a libucabased detector implementation within Concert, we could seamlessly implement device-specific setup and calibration procedures.…”

Section: Concertmentioning

confidence: 99%

“…For this, we integrated our GPU-based data processing framework ufo [22] within Concert. With this framework, a user describes their processing workflow as a graph of processing tasks.…”

Section: Data Processingmentioning

confidence: 99%

A Control System and Streaming DAQ Platform with Image-Based Trigger for X-ray Imaging

Stevanovic

Caselle

Cecilia

et al. 2015

IEEE Trans. Nucl. Sci.

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High-speed X-ray imaging applications play a crucial role for non-destructive investigations of the dynamics in material science and biology. On-line data analysis is necessary for quality assurance and data-driven feedback, leading to a more efficent use of a beam time and increased data quality. In this article we present a smart camera platform with embedded Field Programmable Gate Array (FPGA) processing that is able to stream and process data continuously in real-time. The setup consists of a Complementary Metal-Oxide-Semiconductor (CMOS) sensor, an FPGA readout card, and a readout computer. It is seamlessly integrated in a new custom experiment control system called Concert that provides a more efficient way of operating a beamline by integrating device control, experiment process control, and data analysis. The potential of the embedded processing is demonstrated by implementing an image-based trigger. It records the temporal evolution of physical events with increased speed while maintaining the full field of view. The complete data acquisition system, with Concert and the smart camera platform was successfully integrated and used for fast X-ray imaging experiments at KIT's synchrotron radiation facility ANKA.Index Terms-CMOS image sensors, control systems, data processing, FPGAs, smart cameras. 0018-9499

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UFO: A Scalable GPU-based Image Processing Framework for On-line Monitoring

Cited by 63 publications

References 13 publications

Deep Learning Accelerated Light Source Experiments

Deep Learning Accelerated Light Source Experiments

Optimization of tomographic reconstruction workflows on geographically distributed resources

A Control System and Streaming DAQ Platform with Image-Based Trigger for X-ray Imaging

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