Gpufit: An open-source toolkit for GPU-accelerated curve fitting
We present a general purpose, open-source software library for estimation of non-linear parameters by the Levenberg-Marquardt algorithm. The software, Gpufit, runs on a Graphics Processing Unit (GPU) and executes computations in parallel, resulting in a significant gain in performance. We measured a...
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| Published in | Scientific reports Vol. 7; no. 1; pp. 15722 - 9 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
16.11.2017
Nature Publishing Group |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2045-2322 2045-2322 |
| DOI | 10.1038/s41598-017-15313-9 |
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| Abstract | We present a general purpose, open-source software library for estimation of non-linear parameters by the Levenberg-Marquardt algorithm. The software, Gpufit, runs on a Graphics Processing Unit (GPU) and executes computations in parallel, resulting in a significant gain in performance. We measured a speed increase of up to 42 times when comparing Gpufit with an identical CPU-based algorithm, with no loss of precision or accuracy. Gpufit is designed such that it is easily incorporated into existing applications or adapted for new ones. Multiple software interfaces, including to C, Python, and Matlab, ensure that Gpufit is accessible from most programming environments. The full source code is published as an open source software repository, making its function transparent to the user and facilitating future improvements and extensions. As a demonstration, we used Gpufit to accelerate an existing scientific image analysis package, yielding significantly improved processing times for super-resolution fluorescence microscopy datasets. |
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| AbstractList | We present a general purpose, open-source software library for estimation of non-linear parameters by the Levenberg-Marquardt algorithm. The software, Gpufit, runs on a Graphics Processing Unit (GPU) and executes computations in parallel, resulting in a significant gain in performance. We measured a speed increase of up to 42 times when comparing Gpufit with an identical CPU-based algorithm, with no loss of precision or accuracy. Gpufit is designed such that it is easily incorporated into existing applications or adapted for new ones. Multiple software interfaces, including to C, Python, and Matlab, ensure that Gpufit is accessible from most programming environments. The full source code is published as an open source software repository, making its function transparent to the user and facilitating future improvements and extensions. As a demonstration, we used Gpufit to accelerate an existing scientific image analysis package, yielding significantly improved processing times for super-resolution fluorescence microscopy datasets. We present a general purpose, open-source software library for estimation of non-linear parameters by the Levenberg-Marquardt algorithm. The software, Gpufit, runs on a Graphics Processing Unit (GPU) and executes computations in parallel, resulting in a significant gain in performance. We measured a speed increase of up to 42 times when comparing Gpufit with an identical CPU-based algorithm, with no loss of precision or accuracy. Gpufit is designed such that it is easily incorporated into existing applications or adapted for new ones. Multiple software interfaces, including to C, Python, and Matlab, ensure that Gpufit is accessible from most programming environments. The full source code is published as an open source software repository, making its function transparent to the user and facilitating future improvements and extensions. As a demonstration, we used Gpufit to accelerate an existing scientific image analysis package, yielding significantly improved processing times for super-resolution fluorescence microscopy datasets.We present a general purpose, open-source software library for estimation of non-linear parameters by the Levenberg-Marquardt algorithm. The software, Gpufit, runs on a Graphics Processing Unit (GPU) and executes computations in parallel, resulting in a significant gain in performance. We measured a speed increase of up to 42 times when comparing Gpufit with an identical CPU-based algorithm, with no loss of precision or accuracy. Gpufit is designed such that it is easily incorporated into existing applications or adapted for new ones. Multiple software interfaces, including to C, Python, and Matlab, ensure that Gpufit is accessible from most programming environments. The full source code is published as an open source software repository, making its function transparent to the user and facilitating future improvements and extensions. As a demonstration, we used Gpufit to accelerate an existing scientific image analysis package, yielding significantly improved processing times for super-resolution fluorescence microscopy datasets. |
| ArticleNumber | 15722 |
| Author | Przybylski, Adrian Bates, Mark Keller-Findeisen, Jan Thiel, Björn Stock, Bernd |
| Author_xml | – sequence: 1 givenname: Adrian orcidid: 0000-0002-7306-975X surname: Przybylski fullname: Przybylski, Adrian organization: Department of NanoBiophotonics, Max Planck Institute for Biophysical Chemistry – sequence: 2 givenname: Björn surname: Thiel fullname: Thiel, Björn organization: Department of NanoBiophotonics, Max Planck Institute for Biophysical Chemistry – sequence: 3 givenname: Jan orcidid: 0000-0003-2753-0848 surname: Keller-Findeisen fullname: Keller-Findeisen, Jan organization: Department of NanoBiophotonics, Max Planck Institute for Biophysical Chemistry – sequence: 4 givenname: Bernd surname: Stock fullname: Stock, Bernd organization: Faculty of Natural Sciences and Technology, University of Applied Sciences and Arts – sequence: 5 givenname: Mark orcidid: 0000-0003-0668-5277 surname: Bates fullname: Bates, Mark email: mark.bates@mpibpc.mpg.de organization: Department of NanoBiophotonics, Max Planck Institute for Biophysical Chemistry |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29146965$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1038/nmeth0510-338 10.1371/journal.pone.0076665 10.1038/nmeth.1449 10.1038/nmeth.3797 10.1364/OE.17.023352 10.1016/j.cbpa.2008.08.008 10.1038/nprot.2017.024 10.1137/0111030 10.1371/journal.pcbi.1002598 10.1038/nmeth.2488 10.1016/j.parco.2011.10.002 10.1038/s41598-017-00622-w 10.1007/978-3-319-06548-9_1 10.1126/science.aak9913 10.1007/BFb0067700 10.2172/6997568 |
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| References | Smith, Joseph, Rieger, Lidke (CR10) 2010; 7 Schnitzbauer, Strauss, Schlichthaerle, Schueder, Jungmann (CR12) 2017; 12 CR2 CR3 CR5 CR19 CR7 CR18 CR15 Abraham, Ram, Chao, Ward, Ober (CR9) 2009; 17 Legant (CR13) 2016; 13 Babcock, Zhuang (CR14) 2017; 7 CR20 Huang (CR16) 2013; 10 Morin, Urban, Sliz (CR17) 2012; 8 Marquardt (CR1) 1963; 11 Zhu, Zhang (CR8) 2013; 8 Du (CR4) 2012; 38 Laurence, Chromy (CR6) 2010; 7 Bates, Huang, Zhuang (CR11) 2008; 12 TA Laurence (15313_CR6) 2010; 7 P Du (15313_CR4) 2012; 38 AV Abraham (15313_CR9) 2009; 17 DW Marquardt (15313_CR1) 1963; 11 15313_CR15 F Huang (15313_CR16) 2013; 10 15313_CR18 15313_CR19 CS Smith (15313_CR10) 2010; 7 M Bates (15313_CR11) 2008; 12 J Schnitzbauer (15313_CR12) 2017; 12 15313_CR20 A Morin (15313_CR17) 2012; 8 15313_CR5 X Zhu (15313_CR8) 2013; 8 15313_CR7 15313_CR2 15313_CR3 WR Legant (15313_CR13) 2016; 13 HP Babcock (15313_CR14) 2017; 7 |
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| Title | Gpufit: An open-source toolkit for GPU-accelerated curve fitting |
| URI | https://link.springer.com/article/10.1038/s41598-017-15313-9 https://www.ncbi.nlm.nih.gov/pubmed/29146965 https://www.proquest.com/docview/1965161312 https://www.proquest.com/docview/1966240327 https://pubmed.ncbi.nlm.nih.gov/PMC5691161 https://www.nature.com/articles/s41598-017-15313-9.pdf |
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