Analysis of Parallel Computing Strategies to Accelerate Ultrasound Imaging Processes

Romero-Laorden, David; Villazón-Terrazas, Javier; Martínez-Graullera, Óscar; Ibanez, A.; Parrilla, M.; Peñas, Matilde Santos

doi:10.1109/tpds.2016.2544312

Cited by 13 publications

(7 citation statements)

References 22 publications

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“…The first sub-category of materials-related applications covers ultrasonic imaging, namely those of Sutcliffe et al [60] and Romero-Laorden et al [61]. The second sub-category of materials-related applications was the use of Finite Element methods for the analysis of soft tissues; papers were from Strbac et al [62] and Strbac et al [63].…”

Section: Materials-relatedmentioning

confidence: 99%

Towards extending the SWITCH platform for time-critical, cloud-based CUDA applications: Job scheduling parameters influencing performance

Knight

Štefanič

Cigale

et al. 2019

Future Generation Computer Systems

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SWITCH (Software Workbench for Interactive, Time Critical and Highly self-adaptive cloud applications) allows for the development and deployment of real-time applications in the cloud, but it does not yet support instances backed by Graphics Processing Units (GPUs). Wanting to explore how SWITCH might support CUDA (a GPU architecture) in the future, we have undertaken a review of time-critical CUDA applications, discovering that run-time requirements (which we call 'wall time') are in many cases regarded as the most important. We have performed experiments to investigate which parameters have the greatest impact on wall time when running multiple Amazon Web Services GPU-backed instances. Although a maximum of 8 single-GPU instances can be launched in a single Amazon Region, launching just 2 instances rather than 1 gives a 42% decrease in wall time. Also, instances are often wasted doing nothing, and there is a moderatelystrong relationship between how problems are distributed across instances and wall time. These findings can be used to enhance the SWITCH provision for specifying Non-Functional Requirements (NFRs); in the future, GPU-backed instances could be supported. These findings can also be used more generally, to optimise the balance between the computational resources needed and the resulting wall time to obtain results.

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Section: Materials-relatedmentioning

confidence: 99%

Towards extending the SWITCH platform for time-critical, cloud-based CUDA applications: Job scheduling parameters influencing performance

Knight

Štefanič

Cigale

et al. 2019

Future Generation Computer Systems

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“…La imagenología ultrasónica es uno de los métodos de visualización más populares para examinar el interior de objetos opacos, en (Romero-Laorden et al, 2016) y (Chen, Chen and Huang, 2016) se realiza el análisis de estrategias de cómputo paralelo para acelerar el procesamiento de imagenología de ultrasonido mediante una GPU (Romero-Laorden et al, 2016). La delineación del ventrículo derecho cardíaco es esencial para generar mediciones clínicas, como la fracción de eyección y el volumen sistólico; en (Punithakumar, Boulanger and Noga, 2017) utilizan imágenes MRI para este procedimiento y una plataforma GPU, se observa que se pudo mejorar 19x la velocidad de este tipo de procedimiento.…”

Section: Aplicaciones Médicasunclassified

Utilización de GPU-CUDA en el Procesamiento Digital de Imágenes

Aguilera

Aceves

Argüelles

et al. 2018

CULCyT

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RESUMENEL procesamiento de imágenes es una herramienta de gran utilidad en diversas aplicaciones como video vigilancia, reconstrucción de imágenes, información geográfica y médica. Sin embargo, estas aplicaciones requieren una gran demanda computacional para ser llevadas a cabo en el menor tiempo posible, aún y que se desarrollan nuevos algoritmos, suelen ser restrictivos para implementarse en tiempo real en sistemas que solo se basan en CPU. Afortunadamente, estos algoritmos pueden ser analizados para llevarse a cabo en plataformas de cómputo paralelo, como las GPU-CUDA. En este trabajo se analizan diferentes revistas de la IEEE desde el 2013, donde se paralelizaron algoritmos con estas aplicaciones y se implementaron con ayuda de GPU´s. Se destaca que el uso de esta herramienta ha ido en crecimiento en el ámbito científico, en diferentes ramas de la ciencia.

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“…The capability of this analysis to provide new layers of information over conventional ultrasound images is an exciting field of study [ 8 ], with special interest for quantitative ultrasound medical diagnosis. However, in volumetric imaging, these techniques introduce challenges at several levels [ 9 , 10 , 11 ], including signal processing, system architecture, etc.…”

Section: Introductionmentioning

confidence: 99%

Design of 2D Planar Sparse Binned Arrays Based on the Coarray Analysis

Martínez-Graullera

Souza

Parrilla

et al. 2021

Sensors

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The analysis of the beampattern is the base of sparse arrays design process. However, in the case of bidimensional arrays, this analysis has a high computational cost, turning the design process into a long and complex task. If the imaging system development is considered a holistic process, the aperture is a sampling grid that must be considered in the spatial domain through the coarray structure. Here, we propose to guide the aperture design process using statistical parameters of the distribution of the weights in the coarray. We have studied three designs of sparse matrix binned arrays with different sparseness degrees. Our results prove that there is a relationship between these parameters and the beampattern, which is valuable and improves the array design process. The proposed methodology reduces the computational cost up to 58 times with respect to the conventional fitness function based on the beampattern analysis.

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Analysis of Parallel Computing Strategies to Accelerate Ultrasound Imaging Processes

Cited by 13 publications

References 22 publications

Towards extending the SWITCH platform for time-critical, cloud-based CUDA applications: Job scheduling parameters influencing performance

Towards extending the SWITCH platform for time-critical, cloud-based CUDA applications: Job scheduling parameters influencing performance

Utilización de GPU-CUDA en el Procesamiento Digital de Imágenes

Design of 2D Planar Sparse Binned Arrays Based on the Coarray Analysis

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