The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Performance improvements for iterative electron tomography reconstruction using graphics processing units (GPUs)
Open accessAbstract
Iterative reconstruction algorithms are becoming increasingly important in electron tomography of biological samples. These algorithms, however, impose major computational demands. Parallelization must be employed to maintain acceptable running times. Graphics Processing Units (GPUs) have been demonstrated to be highly cost-effective for carrying out these computations with a high degree of parallelism. In a recent paper by Xu et al. (2010), a GPU implementation strategy was presented that obtains a speedup of an order of magnitude over a previously proposed GPU-based electron tomography implementation. In this technical note, we demonstrate that by making alternative design decisions in the GPU implementation, an additional speedup can be obtained, again of an order of magnitude. By carefully considering memory access locality when dividing the workload among blocks of threads, the GPU's cache is used more efficiently, making more effective use of the available memory bandwidth.
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Xu ve diğerleri [27] görüntü geriçatmanın paralelleştirilmesini GPU ile gerçekleştirmiş ancak farklı bir algoritma kullanmışlardır.
“Başka bir çalışmada ise Xu ve diğerleri [27] yine görüntü geriçatmanın paralelleştirilmesini GPU ile gerçekleştirmiş ancak farklı bir algoritma kullanmışlardır.”
From GPU Programlamada CUDA Platformu Kullanılan Paralel Görüntü İşleme Çalışmalarının İncelenmesi (2020) · cites this paper as [27]
The Abstract (page unavailable) reports that Xu et al. used a GPU implementation strategy for electron tomography reconstruction. It describes a different implementation strategy, not a different reconstruction algorithm; the Experiments and results section compares both implementations using SIRT. The cited paper also reports Xu et al.’s work rather than being their study. (LLM, conf=0.85)
Experimental — accuracy not yet measuredXu ve diğerlerinin [27] çalışmasında eşzamanlı iteratif geriçatma tekniği GPU ile hızlandırılarak gerçek zamanlı uygulamalarda kullanılabilirliği arttırılmış ve optimize edilmiştir.
“Bu çalışmada eşzamanlı iteratif geriçatma tekniği GPU ile hızlandırılarak gerçek zamanlı uygulamalarda kullanılabilirliği arttırılmış ve optimize edilmiştir.”
From GPU Programlamada CUDA Platformu Kullanılan Paralel Görüntü İşleme Çalışmalarının İncelenmesi (2020) · cites this paper as [27]
The Abstract reports that GPU implementation strategies accelerated iterative electron tomography, including SIRT, and that Xu et al. reported an earlier speedup. The Experiments and results section attributes the further optimization and roughly tenfold improvement over Xu et al. to this paper’s own implementation. Our copy does not establish improved suitability for real-time applications. (LLM, conf=0.86)
Experimental — accuracy not yet measured
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Citations
Cites
Principles of Computerized Tomographic Imaging
Kak, A. C., & Slaney, M. (2001) · doi:10.1137/1.9780898719277 · Cited by 5,809
Image reconstruction from projections : the fundamentals of computerized tomography
Herman, G. T. (1980) · Academic Press eBooks · Cited by 2,011
STRUCTURAL STUDIES BY ELECTRON TOMOGRAPHY: From Cells to Molecules
Lučić, V., Förster, F. G., & Baumeister, W. P. (2005) · Annual Review of Biochemistry · doi:10.1146/annurev.biochem.73.011303.074112 · Cited by 703
Purdum, J. (1983) · Medical Entomology and Zoology · Cited by 477
Computational Analysis and Improvement of SIRT
Gregor, J., & Benson, T. M. (2008) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2008.923696 · Cited by 207
Xu, W., Xu, F., Jones, M., Keszthelyi, B., Sedat, J., Agard, D., & Mueller, K. (2010) · Journal of Structural Biology · doi:10.1016/j.jsb.2010.03.018 · Cited by 71
Implementation and performance evaluation of reconstruction algorithms on graphics processors
Castaño Díez, D., Mueller, H., & Frangakis, A. S. (2007) · Journal of Structural Biology · doi:10.1016/j.jsb.2006.08.010 · Cited by 68
A Comparative Study of Popular Interpolation and Integration Methods for Use in Computed Tomography
Xu, F., & Mueller, K. D. (2006) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2006.1625152 · Cited by 36
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Hybrid computing: CPU+GPU co-processing and its application to tomographic reconstruction
Agulleiro, J. I., Vázquez, F., Garzón, E. M., & Fernández, J. J. (2012) · Ultramicroscopy · doi:10.1016/j.ultramic.2012.02.003 · Cited by 34
GPU Programlamada CUDA Platformu Kullanılan Paralel Görüntü İşleme Çalışmalarının İncelenmesi
AYDIN, S., SAMET, R., & BAY, Ö. F. (2020) · Politeknik Dergisi · doi:10.2339/politeknik.563767 · Cited by 3
“Palenstijn ve diğerleri [28] 2011 yılında yaptıkları çalışmada Xu ve diğerlerinin [26] çalışmasını iyileştirmişlerdir.”
Metadata
- Authors
- Palenstijn, W. J., Batenburg, K. J., & Sijbers, J.
- Affiliations
- University of Antwerp (BE)
- Centrum Wiskunde & Informatica (NL)
- Corresponding author
- Palenstijn, W.J.
- Venue
- Journal of Structural Biology
- Publisher
- Elsevier BV
- Published
- 2011-11
- Volume
- 176
- Issue
- 2
- Pages
- 250-253
- ISSN
- 1047-8477 (ISSN-L)
- OpenAlex
- W2170660817
- Cited by
- 384
- References
- 8
- Citations by year
- 2026: 15 · 2025: 20 · 2024: 30 · 2023: 31 · 2022: 31 · 2021: 43 · 2020: 23 · 2019: 21 · 2018: 28 · 2017: 34 · 2016: 37 · 2015: 39 · 2014: 19 · 2013: 8 · 2012: 5
- FWCI
- 6.64
- Type
- journal article
- Language
- en
- Open access
- green
- ir.cwi.nl
- Licence
- Locations
- Journal of Structural Biology (publishedVersion)
- PubMed (publishedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands (submittedVersion)
- Funders
- Interdisciplinary institute for BroadBand Technology
- Vlaamse regering
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Advanced Electron Microscopy Techniques and Applications
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Computer Graphics and Visualization Techniques
- Keywords
- electron tomography, iterative reconstruction, GPU acceleration, parallel computing, memory locality, cache efficiency, memory bandwidth, speedup
- MeSH terms
- Algorithms, Electronic Data Processing, Hemocyanins, Image Processing, Computer-Assisted, Computer Storage Devices, Electron Microscope Tomography
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