KARAS, Pavel, Michal KUDERJAVÝ and David SVOBODA. Deconvolution of huge 3-D images: Parallelization strategies on a multi-GPU system. In Kołodziej, Joanna and Martino, Beniamino and Talia, Domenico and Xiong, Kaiqi. Algorithms and Architectures for Parallel Processing. Neuveden: Springer International Publishing, 2013, p. 279-290. ISBN 978-3-319-03858-2. Available from: https://dx.doi.org/10.1007/978-3-319-03859-9_24.
Other formats:   BibTeX LaTeX RIS
Basic information
Original name Deconvolution of huge 3-D images: Parallelization strategies on a multi-GPU system
Name in Czech Dekonvoluce velkých 3-D obrazů: Strategie pro paralelizaci na multi-GPU systému
Authors KARAS, Pavel (203 Czech Republic, guarantor, belonging to the institution), Michal KUDERJAVÝ (203 Czech Republic, belonging to the institution) and David SVOBODA (703 Slovakia, belonging to the institution).
Edition Neuveden, Algorithms and Architectures for Parallel Processing, p. 279-290, 12 pp. 2013.
Publisher Springer International Publishing
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Italy
Confidentiality degree is not subject to a state or trade secret
Publication form printed version "print"
WWW URL
Impact factor Impact factor: 0.402 in 2005
RIV identification code RIV/00216224:14330/13:00066399
Organization unit Faculty of Informatics
ISBN 978-3-319-03858-2
ISSN 0302-9743
Doi http://dx.doi.org/10.1007/978-3-319-03859-9_24
Keywords (in Czech) dekonvoluce; gpu; multi-gpu; paralelizace; implementace; algoritmus; em-mle; richardson-lucy; ictm; wiener
Keywords in English deconvolution; gpu; multi-gpu; parallelization; implementation; algorithm; em-mle; richardson-lucy; ictm; wiener
Tags cbia-web, firank_B
Tags International impact, Reviewed
Changed by Changed by: RNDr. Pavel Karas, Ph.D., učo 106808. Changed: 29/12/2013 12:04.
Abstract
In this paper, we discuss strategies to parallelize selected deconvolution methods on a multi-GPU system. We provide a comparison of several approaches to split the deconvolution into subtasks while keeping the amount of costly data transfers as low as possible, and propose own implementation of three deconvolution methods which achieves up to 65x speedup over the CPU one. In the experimental part, we analyse how the individual stages of the computation contribute to the overall computation time as well as how the multi-GPU implementation scales in various setups. Finally, we identify bottlenecks of the system.
Links
GBP302/12/G157, research and development projectName: Dynamika a organizace chromosomů během buněčného cyklu a při diferenciaci v normě a patologii
Investor: Czech Science Foundation
MUNI/A/0760/2012, interní kód MUName: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace II. (Acronym: FI MAV II.)
Investor: Masaryk University, Category A
PrintDisplayed: 13/5/2024 20:19