Abundance estimation algorithms using NVIDIA CUDA technology

González, David; Sánchez, Christian; Veguilla, Ricardo; Santiago, Nayda G.; Rosario-Torres, Samuel; Vélez-Reyes, Miguel

doi:10.1117/12.777890

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…As these coefficients are independent among elements, its computation is susceptible to be run on parallel. An algorithm of this problem is shown in Algorithm 1 [19]. Figure 1 shows the workflow of the program.…”

Section: Practical Assignmentmentioning

confidence: 99%

Facilitating the learning process in parallel computing by using instant messaging

Guerrero‐Higueras

Sánchez-González

Conde

et al. 2019

Proceedings of the Seventh International Conference on Technological Ecosystems for Enhancing Multiculturality

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Parallel Programming skills may require a long time to acquire. "Think in parallel" is a skill that requires time, effort, and experience. In this work, we propose to facilitate the students' learning process in parallel programming by using instant messaging. Our aim was to find out whether students' interaction through instant messaging tools is beneficial for the learning process. In order to do so, we asked several students of an HPC course of the Master's degree in Computer Science of the University of León to develop a specific parallel application, each of them using a different application program interface: OpenMP, MPI, CUDA, or OpenCL. Even though the used APIs are different, there are common points in the design process. We encouraged students to interact with each other by using Gitter, an instant messaging tool for GitHub users. Our analysis of the communications and results demonstrate that the direct interaction of students through the Gitter tool has a positive impact on the learning process.

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Section: Practical Assignmentmentioning

confidence: 99%

Facilitating the learning process in parallel computing by using instant messaging

Guerrero‐Higueras

Sánchez-González

Conde

et al. 2019

Proceedings of the Seventh International Conference on Technological Ecosystems for Enhancing Multiculturality

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“…This can be achieved using e.g. GPU processing, [8][9][10][11][12][13][14] multi-core, memory-optimized CPU processing or FPGA processing. 8 Such processing algorithms will contribute to an earlier diagnostic answer after image acquisition and reduce the total processing cost after the image has been fully obtained.…”

Section: -6mentioning

confidence: 99%

Towards real-time medical diagnostics using hyperspectral imaging technology

Bjørgan

Randeberg

2015

SPIE Proceedings

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Hyperspectral imaging provides non-contact, high resolution spectral images which has a substantial diagnostic potential. This can be used for e.g. diagnosis and early detection of arthritis in finger joints. Processing speed is currently a limitation for clinical use of the technique. A real-time system for analysis and visualization using GPU processing and threaded CPU processing is presented. Images showing blood oxygenation, blood volume fraction and vessel enhanced images are among the data calculated in real-time. This study shows the potential of real-time processing in this context. A combination of the processing modules will be used in detection of arthritic finger joints from hyperspectral reflectance and transmittance data.

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“…Previous works have shown that HSI data processing can significantly benefit from parallel computing resources of hardware platforms like computer clusters, field-programmable gate arrays (FPGA), or graphics processing units (GPU). [5][6][7][8][9] Specifically, GPUs have proven to be promising candidates as hardware platforms for accelerating hyperspectral processing tasks due to its highly parallel structure and the high computational capabilities that can be achieved at relative low costs. 10,11 However, since the GPU architecture is optimized for data-parallel processing, (i.e., tasks where the same computation is repeated many times over different data elements), only hyperspectral algorithms that show this data-parallel structure can significantly benefit from GPU-based implementations.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the graphics processing unit architecture for the implementation of target detection algorithms for hyperspectral imagery

Trigueros-Espinosa

Vélez-Reyes

Santiago

et al. 2012

J. Appl. Remote Sens

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Abstract. Recent advances in hyperspectral imaging sensors allow the acquisition of images of a scene at hundreds of contiguous narrow spectral bands. Target detection algorithms try to exploit this high-resolution spectral information to detect target materials present in a scene, but this process may be computationally intensive due to the large data volumes generated by the hyperspectral sensors, typically hundreds of megabytes. Previous works have shown that hyperspectral data processing can significantly benefit from the parallel computing resources of graphics processing units (GPUs), due to their highly parallel structure and the high computational capabilities that can be achieved at relative low costs. We studied the parallel implementation of three target detection algorithms (RX algorithm, matched filter, and adaptive matched subspace detector) for hyperspectral images in order to identify the aspects in the structure of these algorithms that can exploit the CUDA™ architecture of NVIDIA ® GPUs. A data set was generated using a SOC-700 hyperspectral imager to evaluate the performance and detection accuracy of the parallel implementations on a NVIDIA ® Tesla™ C1060 graphics card, achieving real-time performance in the GPU implementations based on global statistics.

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Abundance estimation algorithms using NVIDIA CUDA technology

Cited by 5 publications

References 7 publications

Facilitating the learning process in parallel computing by using instant messaging

Facilitating the learning process in parallel computing by using instant messaging

Towards real-time medical diagnostics using hyperspectral imaging technology

Evaluation of the graphics processing unit architecture for the implementation of target detection algorithms for hyperspectral imagery

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