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Montserrat Parrilla

dblp:53/8914 · also Montserrat Parrilla-Romero · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2016
0000-0003-0938-7478ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Parallel and multicore computing · 50% GPUs and heterogeneous computing · 50%
Computer graphics and multimedia
1 paper
Computational photography and imaging · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
GPUs and heterogeneous computing
GPU computing		 0.212016
Analysis of Parallel Computing Strategies to Accelerate Ultrasound Imaging Processes · IEEE Trans. Parallel Distributed Syst. 2016
Parallel and multicore computing
parallelization strategies		 0.212016
Analysis of Parallel Computing Strategies to Accelerate Ultrasound Imaging Processes · IEEE Trans. Parallel Distributed Syst. 2016
Computational photography and imaging › multi-perspective imaging
synthetic aperture imaging		 0.112016
Analysis of Parallel Computing Strategies to Accelerate Ultrasound Imaging Processes · IEEE Trans. Parallel Distributed Syst. 2016

Methods — techniques the papers use, named apart from their topics

total focusing method · 0.5parallel processing · 0.5
YearPublicationVenuePosition
2016 Analysis of Parallel Computing Strategies to Accelerate Ultrasound Imaging Processes
abstract
This work analyses the use of parallel processing techniques in synthetic aperture ultrasonic imaging applications. In particular, the Total Focussing Method, which is a O(N2x P) problem, is studied. This work presents different parallelization strategies for multicore CPU and GPU architectures. The parallelization processes on both platforms are discussed and optimized in order to achieve real-time performance.
David Romero-Laorden, Javier Villazón-Terrazas, Oscar Martinez-Graullera, Alberto Ibañez, Montserrat Parrilla, Matilde Santos Peñas
 IEEE Trans. Parallel Distributed Syst.5