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Journal articles on the topic 'GPU Accelerated'

To see the other types of publications on this topic, follow the link: GPU Accelerated.

Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Lu, Q., and J. Amundson. "Synergia CUDA: GPU-accelerated accelerator modeling package." Journal of Physics: Conference Series 513, no. 5 (June 11, 2014): 052021. http://dx.doi.org/10.1088/1742-6596/513/5/052021.

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2

Jiang, Hao, Chen-Wei Xu, Zhi-Yong Liu, and Li-Yan Yu. "GPU-Accelerated Apriori Algorithm." ITM Web of Conferences 12 (2017): 03046. http://dx.doi.org/10.1051/itmconf/20171203046.

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3

Endo, Yutaka, Tomoyoshi Shimobaba, Takashi Kakue, and Tomoyoshi Ito. "GPU-accelerated compressive holography." Optics Express 24, no. 8 (April 11, 2016): 8437. http://dx.doi.org/10.1364/oe.24.008437.

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4

Ferroni, Francesco, Edmund Tarleton, and Steven Fitzgerald. "GPU accelerated dislocation dynamics." Journal of Computational Physics 272 (September 2014): 619–28. http://dx.doi.org/10.1016/j.jcp.2014.04.052.

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5

Kilgard, Mark J., and Jeff Bolz. "GPU-accelerated path rendering." ACM Transactions on Graphics 31, no. 6 (November 2012): 1–10. http://dx.doi.org/10.1145/2366145.2366191.

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6

Zhu, Rui, Chang Nian Chen, and Lei Hua Qin. "An Transfer Latency Optimized Solution in GPU-Accelerated De-Duplication." Applied Mechanics and Materials 336-338 (July 2013): 2059–62. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.2059.

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Recently, GPU has been introduced as an important tool in general purpose programming due to its powerful computing capacity. In data de-duplication systems, GPU has been used to accelerate the chunking and hashing algorithms. However, the data transfer latency between the memories of CPU to GPU is one of the main challenges in GPU accelerated de-duplication. To alleviate this challenge, our solution strives to reduce the data transfer time between host and GPU memory on parallelized content-defined chunking and hashing algorithm. In our experiment, it has shown 15%~20% performance improvements over already accelerated baseline GPU implementation in data de-duplication.

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7

Wang, Qiao, and Chen Meng. "PhotoNs-GPU: A GPU accelerated cosmological simulation code." Research in Astronomy and Astrophysics 21, no. 11 (December 1, 2021): 281. http://dx.doi.org/10.1088/1674-4527/21/11/281.

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Abstract We present a GPU-accelerated cosmological simulation code, PhotoNs-GPU, based on an algorithm of Particle Mesh Fast Multipole Method (PM-FMM), and focus on the GPU utilization and optimization. A proper interpolated method for truncated gravity is introduced to speed up the special functions in kernels. We verify the GPU code in mixed precision and different levels of theinterpolated method on GPU. A run with single precision is roughly two times faster than double precision for current practical cosmological simulations. But it could induce an unbiased small noise in power spectrum. Compared with the CPU version of PhotoNs and Gadget-2, the efficiency of the new code is significantly improved. Activated all the optimizations on the memory access, kernel functions and concurrency management, the peak performance of our test runs achieves 48% of the theoretical speed and the average performance approaches to ∼35% on GPU.

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8

Fan, Mengran, Jian Wang, Huaipan Jiang, Yilin Feng, Mehrdad Mahdavi, Kamesh Madduri, Mahmut T. Kandemir, and Nikolay V. Dokholyan. "GPU-Accelerated Flexible Molecular Docking." Journal of Physical Chemistry B 125, no. 4 (January 26, 2021): 1049–60. http://dx.doi.org/10.1021/acs.jpcb.0c09051.

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9

Wang, Liang, Yi Sheng Zhang, Bin Zhu, Chi Xu, Xiao Wei Tian, Chao Wang, Jian Hua Mo, and Jian Li. "GPU Accelerated Parallel Cholesky Factorization." Applied Mechanics and Materials 148-149 (December 2011): 1370–73. http://dx.doi.org/10.4028/www.scientific.net/amm.148-149.1370.

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One of the fundamental problems in scientific computing is to find solutions for linear equation systems. For finite element problem, Cholesky factorization is often used to solve symmetric positive definite matrices. In this paper, Cholesky factorization is massively parallelized and three different optimization methods - highly parallel factorization, tile strategy and memory scheduling are used to accelerate Cholesky factorization effectively. A novel algorithm using OpenCL is implemented. Testing on GPU shows that performance of the algorithm increases with the dimension of matrix, reaching 785.41GFlops, about 50x times speedup. Cholesky factorization is remarkably improved with OpenCL on GPU.

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10

Sloup, Petr. "GPU-accelerated raster map reprojection." Geoinformatics FCE CTU 15, no. 1 (July 22, 2016): 61–68. http://dx.doi.org/10.14311/gi.15.1.5.

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<p>Reprojecting raster maps from one projection to another is an essential part of many cartographic processes (map comparison, overlays, data presentation, ...) and reducing the required computational time is desirable and often significantly decreases overall processing costs.</p><p>The raster reprojection process operates per-pixel and is, therefore, a good candidate for GPU-based parallelization where the large number of processors can lead to a very high degree of parallelism.</p><p>We have created an experimental implementation of the raster reprojection with GPU-based parallelization (using OpenCL API).<br />During the evaluation, we compared the performance of our implementation to the optimized GDAL and showed that there is a class of problems where GPU-based parallelization can lead to more than sevenfold speedup.</p>

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11

Yuan, Shinsheng, Guani Wu, Yu-Cheng Li, Yi-Chang Lu, and Ker-Chau Li. "GPU Accelerated Liquid Association (GALA)." Statistics and Its Interface 13, no. 1 (2020): 119–25. http://dx.doi.org/10.4310/sii.2020.v13.n1.a10.

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12

Li, Wenli, W. Randolph Franklin, Salles Viana Gomes de Magalhães, and Marcus V. A. Andrade. "GPU-Accelerated Multiple Observer Siting." Photogrammetric Engineering & Remote Sensing 83, no. 6 (June 1, 2017): 439–46. http://dx.doi.org/10.14358/pers.83.6.439.

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13

Denkowski, Marcin. "GPU Accelerated 3D Object Reconstruction." Procedia Computer Science 18 (2013): 290–98. http://dx.doi.org/10.1016/j.procs.2013.05.192.

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14

Tang, Min, Jie-yi Zhao, Ruo-feng Tong, and Dinesh Manocha. "GPU accelerated convex hull computation." Computers & Graphics 36, no. 5 (August 2012): 498–506. http://dx.doi.org/10.1016/j.cag.2012.03.015.

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15

Mohr, A., J. Wagner, K. Schubert, J. Debus, and F. Sterzing. "Accelerated GPU-Based Tomotherapy Planning." International Journal of Radiation Oncology*Biology*Physics 87, no. 2 (October 2013): S706. http://dx.doi.org/10.1016/j.ijrobp.2013.06.1870.

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16

Meister, Andreas, Sebastian Breß, and Gunter Saake. "Toward GPU-accelerated Database Optimization." Datenbank-Spektrum 15, no. 2 (April 21, 2015): 131–40. http://dx.doi.org/10.1007/s13222-015-0184-3.

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17

Zhou, Yanxiang, Juliane Liepe, Xia Sheng, Michael P. H. Stumpf, and Chris Barnes. "GPU accelerated biochemical network simulation." Bioinformatics 27, no. 6 (January 11, 2011): 874–76. http://dx.doi.org/10.1093/bioinformatics/btr015.

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18

Anthopoulos, Athanasios, Ian Grimstead, and Andrea Brancale. "GPU-accelerated molecular mechanics computations." Journal of Computational Chemistry 34, no. 26 (July 17, 2013): 2249–60. http://dx.doi.org/10.1002/jcc.23384.

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19

Lamas-Rodríguez, Julián, Dora B. Heras, Francisco Argüello, Dagmar Kainmueller, Stefan Zachow, and Montserrat Bóo. "GPU-accelerated level-set segmentation." Journal of Real-Time Image Processing 12, no. 1 (November 26, 2013): 15–29. http://dx.doi.org/10.1007/s11554-013-0378-6.

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20

Gremse, Felix, Andreas Höfter, Lukas Razik, Fabian Kiessling, and Uwe Naumann. "GPU-accelerated adjoint algorithmic differentiation." Computer Physics Communications 200 (March 2016): 300–311. http://dx.doi.org/10.1016/j.cpc.2015.10.027.

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21

Barash, Lev Yu, Martin Weigel, Michal Borovský, Wolfhard Janke, and Lev N. Shchur. "GPU accelerated population annealing algorithm." Computer Physics Communications 220 (November 2017): 341–50. http://dx.doi.org/10.1016/j.cpc.2017.06.020.

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22

Wang, T., Z. Jiang, Q. Kemao, F. Lin, and S. H. Soon. "GPU Accelerated Digital Volume Correlation." Experimental Mechanics 56, no. 2 (October 6, 2015): 297–309. http://dx.doi.org/10.1007/s11340-015-0091-4.

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23

Zang, Chuantao, and Koichi Hashimoto. "GPU Acceleration in a Visual Servo System." Journal of Robotics and Mechatronics 24, no. 1 (February 20, 2012): 105–14. http://dx.doi.org/10.20965/jrm.2012.p0105.

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In this paper we present our novel work of using the Graphic Processing Unit (GPU) to improve the performance of a homography-based visual servo system. We propose a GPU accelerated Efficient Second-order Minimization (GPU-ESM) algorithm to ensure a fast and stable homography solution, approximately 20 times faster than its CPU implementation. To enhance the system stability, we adopt a GPU accelerated Scale Invariant Feature Transform (SIFT) algorithm to deal with those cases where GPU-ESM algorithm performs poor, such as large image differences, occlusion and so on. The combination of both GPU accelerated algorithms is described in detail. The effectiveness of our GPU accelerated system is evaluated with experimental data. The key optimization techniques in our GPU applications are presented as a reference for other researchers.

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24

Chai, Ya Hui, Wen Feng Shen, Wei Min Xu, and Yan Heng Zheng. "Computing Acceleration of FMM Algorithm on the Basis of FPGA and GPU." Advanced Materials Research 291-294 (July 2011): 3272–77. http://dx.doi.org/10.4028/www.scientific.net/amr.291-294.3272.

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FMM is an efficient algorithm in computing N-body problem. This paper firstly subdivides the FMM into 10 procedures. Based on the analysis the computing type of each procedure, we choose key procedures accelerated on FPGA, GPU and Cell BE. And then we present the speedup ratio of each accelerated procedure through experiments. Finally we analyze the computing characteristic of FMM on the computing architecture on accelerator FPGA and GPU on the side of P, M and C.

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25

Liu, Qingjun, Weiqin Zhao, Fang Liu, Ningming Nie, and Chunbao Zhou. "GPU-Accelerated Parton Cascade in Heavy-Ion Collisions." International Journal of Computer Theory and Engineering 8, no. 6 (December 2016): 439–43. http://dx.doi.org/10.7763/ijcte.2016.v8.1086.

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26

Zuo, Yingtao, Pingjian Chen, Lin Fu, Zhenghong Gao, and Gang Chen. "Advanced Aerostructural Optimization Techniques for Aircraft Design." Mathematical Problems in Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/753042.

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Traditional coupled aerostructural design optimization (ASDO) of aircraft based on high-fidelity models is computationally expensive and inefficient. To improve the efficiency, the key is to predict aerostructural performance of the aircraft efficiently. The cruise shape of the aircraft is parameterized and optimized in this paper, and a methodology named reverse iteration of structural model (RISM) is adopted to get the aerostructural performance of cruise shape efficiently. A new mathematical explanation of RISM is presented in this paper. The efficiency of RISM can be improved by four times compared with traditional static aeroelastic analysis. General purpose computing on graphical processing units (GPGPU) is adopted to accelerate the RISM further, and GPU-accelerated RISM is constructed. The efficiency of GPU-accelerated RISM can be raised by about 239 times compared with that of the loosely coupled aeroelastic analysis. Test shows that the fidelity of GPU-accelerated RISM is high enough for optimization. Optimization framework based on Kriging model is constructed. The efficiency of the proposed optimization system can be improved greatly with the aid of GPU-accelerated RISM. An unmanned aerial vehicle (UAV) is optimized using this framework and the range is improved by 4.67% after optimization, which shows effectiveness and efficiency of this framework.

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27

Schnös, Florian, Dirk Hartmann, Birgit Obst, and Glenn Glashagen. "GPU accelerated voxel-based machining simulation." International Journal of Advanced Manufacturing Technology 115, no. 1-2 (May 8, 2021): 275–89. http://dx.doi.org/10.1007/s00170-021-07001-w.

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AbstractThe simulation of subtractive manufacturing processes has a long history in engineering. Corresponding predictions are utilized for planning, validation and optimization, e.g., of CNC-machining processes. With the up-rise of flexible robotic machining and the advancements of computational and algorithmic capability, the simulation of the coupled machine-process behaviour for complex machining processes and large workpieces is within reach. These simulations require fast material removal predictions and analysis with high spatial resolution for multi-axis operations. Within this contribution, we propose to leverage voxel-based concepts introduced in the computer graphics industry to accelerate material removal simulations. Corresponding schemes are well suited for massive parallelization. By leveraging the computational power offered by modern graphics hardware, the computational performance of high spatial accuracy volumetric voxel-based algorithms is further improved. They now allow for very fast and accurate volume removal simulation and analysis of machining processes. Within this paper, a detailed description of the data structures and algorithms is provided along a detailed benchmark for common machining operations.

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28

SUZUKI, Yasuko, and Yuriko TAKESHIMA. "Accelerated Diffusion-Based Tractography on GPU." Journal of the Visualization Society of Japan 28-1, no. 1 (2008): 83. http://dx.doi.org/10.3154/jvs.28.83.

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29

Sreenivasan, Varsha, Sawan Kumar, Franco Pestilli, Partha Talukdar, and Devarajan Sridharan. "GPU-accelerated connectome discovery at scale." Nature Computational Science 2, no. 5 (May 2022): 298–306. http://dx.doi.org/10.1038/s43588-022-00250-z.

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AbstractDiffusion magnetic resonance imaging and tractography enable the estimation of anatomical connectivity in the human brain, in vivo. Yet, without ground-truth validation, different tractography algorithms can yield widely varying connectivity estimates. Although streamline pruning techniques mitigate this challenge, slow compute times preclude their use in big-data applications. We present ‘Regularized, Accelerated, Linear Fascicle Evaluation’ (ReAl-LiFE), a GPU-based implementation of a state-of-the-art streamline pruning algorithm (LiFE), which achieves >100× speedups over previous CPU-based implementations. Leveraging these speedups, we overcome key limitations with LiFE’s algorithm to generate sparser and more accurate connectomes. We showcase ReAl-LiFE’s ability to estimate connections with superlative test–retest reliability, while outperforming competing approaches. Moreover, we predicted inter-individual variations in multiple cognitive scores with ReAl-LiFE connectome features. We propose ReAl-LiFE as a timely tool, surpassing the state of the art, for accurate discovery of individualized brain connectomes at scale. Finally, our GPU-accelerated implementation of a popular non-negative least-squares optimization algorithm is widely applicable to many real-world problems.

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30

Zhao Yalong, 赵亚龙, 刘守起 Liu Shouqi, and 张启灿 Zhang Qican. "3D shape measurement accelerated by GPU." Infrared and Laser Engineering 47, no. 3 (2018): 317003. http://dx.doi.org/10.3788/irla201847.0317003.

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31

Chen, Renjie, and Ofir Weber. "GPU-accelerated locally injective shape deformation." ACM Transactions on Graphics 36, no. 6 (November 20, 2017): 1–13. http://dx.doi.org/10.1145/3130800.3130843.

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32

Qi, Ji, Kuan Ching Li, Hai Jiang, Qingguo Zhou, and Lei Yang. "GPU-accelerated DEM implementation with CUDA." International Journal of Computational Science and Engineering 11, no. 3 (2015): 330. http://dx.doi.org/10.1504/ijcse.2015.072653.

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33

Shen, Minghua, Guojie Luo, and Nong Xiao. "Exploring GPU-Accelerated Routing for FPGAs." IEEE Transactions on Parallel and Distributed Systems 30, no. 6 (June 1, 2019): 1331–45. http://dx.doi.org/10.1109/tpds.2018.2885745.

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34

Yan, C. Q., T. X. Yue, and G. Zhao. "GPU Accelerated High Accuracy Surface Modelling." Procedia Environmental Sciences 13 (2012): 555–64. http://dx.doi.org/10.1016/j.proenv.2012.01.046.

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35

Rivi, M., C. Gheller, T. Dykes, M. Krokos, and K. Dolag. "GPU accelerated particle visualization with Splotch." Astronomy and Computing 5 (July 2014): 9–18. http://dx.doi.org/10.1016/j.ascom.2014.03.001.

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36

Chentanez, Nuttapong, Matthias Müller, and Miles Macklin. "GPU accelerated grid-free surface tracking." Computers & Graphics 57 (June 2016): 1–11. http://dx.doi.org/10.1016/j.cag.2016.03.002.

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37

Li, Bo, Ge Chen, Fenglin Tian, Baomin Shao, and Pengbo Ji. "GPU accelerated marine data visualization method." Journal of Ocean University of China 13, no. 6 (November 9, 2014): 964–70. http://dx.doi.org/10.1007/s11802-014-2304-3.

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38

Li, Ruipeng, and Yousef Saad. "GPU-accelerated preconditioned iterative linear solvers." Journal of Supercomputing 63, no. 2 (October 5, 2012): 443–66. http://dx.doi.org/10.1007/s11227-012-0825-3.

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39

Wang, H., and Y. Cao. "GPU-accelerated voxelwise hepatic perfusion quantification." Physics in Medicine and Biology 57, no. 17 (August 14, 2012): 5601–16. http://dx.doi.org/10.1088/0031-9155/57/17/5601.

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40

Kinsner, Michael, Allan Spence, and David Capson. "GPU Accelerated Sheet Forming Grid Measurement." Computer-Aided Design and Applications 7, no. 5 (January 2010): 675–84. http://dx.doi.org/10.3722/cadaps.2010.675-684.

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41

Sunny Joseph, Ajai, and Elizabeth Isaac. "GPU Accelerated real-time Melanoma Detection." International Journal of Engineering & Technology 7, no. 3 (June 27, 2018): 1208. http://dx.doi.org/10.14419/ijet.v7i3.13169.

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Melanoma is recognized as one of the most dangerous type of skin cancer. A novel method to detect melanoma in real time with the help of Graphical Processing Unit (GPU) is proposed. Existing systems can process medical images and perform a diagnosis based on Image Processing technique and Artiﬁcial Intelligence. They are also able to perform video processing with the help of large hardware resources at the backend. This incurs signiﬁcantly higher costs and space and are complex by both software and hardware. Graphical Processing Units have high processing capabilities compared to a Central Processing Unit of a system. Various approaches were used for implementing real time detection of Melanoma. The results and analysis based on various approaches and the best approach based on our study is discussed in this work. A performance analysis for the approaches on the basis of CPU and GPU environment is also discussed. The proposed system will perform real-time analysis of live medical video data and performs diagnosis. The system when implemented yielded an accuracy of 90.133% which is comparable to existing systems.

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42

Zhou, Chunbao, Xianyu Lang, Yangang Wang, and Chaodong Zhu. "gPGA: GPU Accelerated Population Genetics Analyses." PLOS ONE 10, no. 8 (August 6, 2015): e0135028. http://dx.doi.org/10.1371/journal.pone.0135028.

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43

Höfinger, Siegfried, Angela Acocella, Sergiu C. Pop, Tetsu Narumi, Kenji Yasuoka, Titus Beu, and Francesco Zerbetto. "GPU-accelerated computation of electron transfer." Journal of Computational Chemistry 33, no. 29 (July 30, 2012): 2351–56. http://dx.doi.org/10.1002/jcc.23082.

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44

Zigon, Bob, Huang Li, Xiaohui Yao, Shiaofen Fang, Mohammad Al Hasan, Jingwen Yan, Jason H. Moore, Andrew J. Saykin, and Li Shen. "GPU Accelerated Browser for Neuroimaging Genomics." Neuroinformatics 16, no. 3-4 (April 25, 2018): 393–402. http://dx.doi.org/10.1007/s12021-018-9376-y.

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45

Das, Subhra Kanti, Chandan Mazumdar, and Kumardeb Banerjee. "GPU accelerated novel particle filtering method." Computing 96, no. 8 (April 28, 2014): 749–73. http://dx.doi.org/10.1007/s00607-014-0400-2.

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46

Harris, Chris, Karen Haines, and Lister Staveley-Smith. "GPU accelerated radio astronomy signal convolution." Experimental Astronomy 22, no. 1-2 (July 31, 2008): 129–41. http://dx.doi.org/10.1007/s10686-008-9114-9.

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47

Fischer, Katrin, and Georg-Peter Ostermeyer. "Hybrid GPU Accelerated Mesoscopic Particle Simulation." PAMM 12, no. 1 (December 2012): 749–50. http://dx.doi.org/10.1002/pamm.201210363.

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48

Jiang, Ronglin, Shugang Jiang, Yu Zhang, Ying Xu, Lei Xu, and Dandan Zhang. "GPU-Accelerated Parallel FDTD on Distributed Heterogeneous Platform." International Journal of Antennas and Propagation 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/321081.

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This paper introduces a (finite difference time domain) FDTD code written in Fortran and CUDA for realistic electromagnetic calculations with parallelization methods of Message Passing Interface (MPI) and Open Multiprocessing (OpenMP). Since both Central Processing Unit (CPU) and Graphics Processing Unit (GPU) resources are utilized, a faster execution speed can be reached compared to a traditional pure GPU code. In our experiments, 64 NVIDIA TESLA K20m GPUs and 64 INTEL XEON E5-2670 CPUs are used to carry out the pure CPU, pure GPU, and CPU + GPU tests. Relative to the pure CPU calculations for the same problems, the speedup ratio achieved by CPU + GPU calculations is around 14. Compared to the pure GPU calculations for the same problems, the CPU + GPU calculations have 7.6%–13.2% performance improvement. Because of the small memory size of GPUs, the FDTD problem size is usually very small. However, this code can enlarge the maximum problem size by 25% without reducing the performance of traditional pure GPU code. Finally, using this code, a microstrip antenna array with16×18elements is calculated and the radiation patterns are compared with the ones of MoM. Results show that there is a well agreement between them.

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49

Yang, Zhang, Chen Wen Bo, Bai Qi Feng, and Lian Li. "Test and Analysis GPU-Accelerated in Molecular Dynamics Simulation." Applied Mechanics and Materials 380-384 (August 2013): 1652–55. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1652.

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GPU computing is the use of a graphics processing unit together with a CPU to accelerate large scale scientific and engineering applications, such as molecule simulation. The paper use NVIDIA Tesla C2050NVIDIA GTX580 and NAMD 2.9 simulates three differences molecule systems: Beta2,SET9 and Ubiquitin. We compared and analyzed the results of the simulations experiment, and come to conclusion that the difference molecule systems will get the difference speed accelerated. The computing times of four GPU is nearly half of the time used by one GPU; and this is especially in the case of macromolecules system. Furthermore, from the GPUs memory utilization rate, the larger the protein system is, the higher the memory use of the GPU is. The performance of NVIDIA GTX580 is only half of the NVIDIAC2050. NVIDIA Tesla C2050 is can satisfy an even larger system simulation.

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50

Chen, Yu Min, Fei Zeng, Jing Yang Wu, Qiao Wan, and Zhi Jun Su. "GPU-Accelerated Discrete Wavelet Transform for Images." Advanced Materials Research 718-720 (July 2013): 2086–91. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.2086.

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Discrete Wavelet Transform (DWT) has been brought into wide use in image processing, but it cant fit the demand of the hugeimage data because the time of computing is vast. The GPU is an attractive platform for a broad fieldof applications,which remains asignificanthigharithmetic processingcapability. Therefore itcan beusedasa powerful accelerator without extra cost.CUDA(computeunifieddevicearchitecture) providesahardwareandsoftwareenvironment touse the GPU to accelerate the DWT for images. In this paper, we use the NVIDIA GeForce GT 650M that complies with the CUDA to improvethe execution time of theDiscrete Wavelet Transformfor images. TheresultofexperimentsindicatesthattheCUDAtechnology hastheadvantagesof parallel processingandtheefficiencyofimagetransform isimprovedgreatly. Whats more, it performs better on the larger size image (the max speedup is 15.9).

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