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Journal articles on the topic 'Accelerated Processing Unit'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Carpenter, Joel. "Graphics processing unit–accelerated holography by simulated annealing." Optical Engineering 49, no. 9 (2010): 095801. http://dx.doi.org/10.1117/1.3484950.

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2

Kang, Hoonjong, Fahri Yaraş, and Levent Onural. "Graphics processing unit accelerated computation of digital holograms." Applied Optics 48, no. 34 (2009): H137. http://dx.doi.org/10.1364/ao.48.00h137.

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3

Chapuis, Guillaume, Olivier Filangi, Jean-Michel Elsen, Dominique Lavenier, and Pascale Le Roy. "Graphics Processing Unit–Accelerated Quantitative Trait Loci Detection." Journal of Computational Biology 20, no. 9 (2013): 672–86. http://dx.doi.org/10.1089/cmb.2012.0136.

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4

Shaojing Li, Boris Livshitz, and Vitaliy Lomakin. "Graphics Processing Unit Accelerated $O(N)$ Micromagnetic Solver." IEEE Transactions on Magnetics 46, no. 6 (2010): 2373–75. http://dx.doi.org/10.1109/tmag.2010.2043504.

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5

Sunny Joseph, Ajai, and Elizabeth Isaac. "GPU Accelerated real-time Melanoma Detection." International Journal of Engineering & Technology 7, no. 3 (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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6

Said, Issam, Pierre Fortin, Jean–Luc Lamotte, and Henri Calandra. "Leveraging the accelerated processing units for seismic imaging: A performance and power efficiency comparison against CPUs and GPUs." International Journal of High Performance Computing Applications 32, no. 6 (2017): 819–37. http://dx.doi.org/10.1177/1094342017696562.

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Oil and gas companies rely on high performance computing to process seismic imaging algorithms such as reverse time migration. Graphics processing units are used to accelerate reverse time migration, but these deployments suffer from limitations such as the lack of high graphics processing unit memory capacity, frequent CPU-GPU communications that may be bottlenecked by the PCI bus transfer rate, and high power consumptions. Recently, AMD has launched the Accelerated Processing Unit (APU): a processor that merges a CPU and a graphics processing unit on the same die featuring a unified CPU-GPU memory. In this paper, we explore how efficiently may the APU be applicable to reverse time migration. Using OpenCL (along with MPI and OpenMP), a CPU/APU/GPU comparative study is conducted on a single node for the 3D acoustic reverse time migration, and then extended on up to 16 nodes. We show the relevance of overlapping the I/O and MPI communications with the computations for the APU and graphics processing unit clusters, that performance results of APUs range between those of CPUs and those of graphics processing units, and that the APU power efficiency is greater than or equal to the graphics processing unit one.

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Barca, Giuseppe M. J., Jorge L. Galvez-Vallejo, David L. Poole, Alistair P. Rendell, and Mark S. Gordon. "High-Performance, Graphics Processing Unit-Accelerated Fock Build Algorithm." Journal of Chemical Theory and Computation 16, no. 12 (2020): 7232–38. http://dx.doi.org/10.1021/acs.jctc.0c00768.

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8

Ni, Xiaolong, Zhi Liu, Chunyi Chen, et al. "Graphic processing unit accelerated real-time partially coherent beam generator." Optics and Lasers in Engineering 82 (July 2016): 62–69. http://dx.doi.org/10.1016/j.optlaseng.2016.02.005.

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9

Bouvier, Dan, Brad Cohen, Walter Fry, Sreekanth Godey, and Michael Mantor. "Kabini: An AMD Accelerated Processing Unit System on A Chip." IEEE Micro 34, no. 2 (2014): 22–33. http://dx.doi.org/10.1109/mm.2014.3.

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10

Gajic, Dusan, and Radomir Stankovic. "GPU accelerated computation of fast spectral transforms." Facta universitatis - series: Electronics and Energetics 24, no. 3 (2011): 483–99. http://dx.doi.org/10.2298/fuee1103483g.

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This paper discusses techniques for accelerated computation of several fast spectral transforms on graphics processing units (GPUs) using the Open Computing Language (OpenCL). We present a reformulation of fast algorithms which takes into account peculiar properties of transforms to make them suitable for the GPU implementation. A special attention is paid to the organization of computations, memory transfer reductions, impact of integer and Boolean arithmetic, different structure of algorithms, etc. Performance of the GPU implementations is compared with the classical C/C++ implementations for the central processing unit (CPU). Experiments confirm that, even though the spectral transforms considered involve only simple arithmetic, significant speedups are achieved by implementing the algorithms in OpenCL and performing them on the GPU.

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11

Stock, Mark J., and Adrin Gharakhani. "Graphics Processing Unit-Accelerated Boundary Element Method and Vortex Particle Method." Journal of Aerospace Computing, Information, and Communication 8, no. 7 (2011): 224–36. http://dx.doi.org/10.2514/1.52938.

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12

Zhou, Guoqing, Ben Nebgen, Nicholas Lubbers, Walter Malone, Anders M. N. Niklasson, and Sergei Tretiak. "Graphics Processing Unit-Accelerated Semiempirical Born Oppenheimer Molecular Dynamics Using PyTorch." Journal of Chemical Theory and Computation 16, no. 8 (2020): 4951–62. http://dx.doi.org/10.1021/acs.jctc.0c00243.

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13

Li, Guanhua, Zongying Ou, Tieming Su, and Jun Han. "Graphic processing unit-accelerated mutual information-based 3D image rigid registration." Transactions of Tianjin University 15, no. 5 (2009): 375–80. http://dx.doi.org/10.1007/s12209-009-0066-6.

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14

Sunseri, Jocelyn, and David R. Koes. "libmolgrid: Graphics Processing Unit Accelerated Molecular Gridding for Deep Learning Applications." Journal of Chemical Information and Modeling 60, no. 3 (2020): 1079–84. http://dx.doi.org/10.1021/acs.jcim.9b01145.

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15

Groß, Felix, José Carlos Martínez-García, Sven Erik Ilse, et al. "gFORC: A graphics processing unit accelerated first-order reversal-curve calculator." Journal of Applied Physics 126, no. 16 (2019): 163901. http://dx.doi.org/10.1063/1.5120495.

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16

Fang, Qianqian, and Shijie Yan. "Graphics processing unit-accelerated mesh-based Monte Carlo photon transport simulations." Journal of Biomedical Optics 24, no. 11 (2019): 1. http://dx.doi.org/10.1117/1.jbo.24.11.115002.

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17

Mauch, Florian, Marc Gronle, Wolfram Lyda, and Wolfgang Osten. "Open-source graphics processing unit–accelerated ray tracer for optical simulation." Optical Engineering 52, no. 5 (2013): 053004. http://dx.doi.org/10.1117/1.oe.52.5.053004.

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18

McLeod, Adam, Scott Bai, and Joseph Meyer. "Graphical Processing Unit accelerated radio path-loss estimation with neural networks." Journal of Defense Modeling and Simulation: Applications, Methodology, Technology 10, no. 2 (2012): 117–30. http://dx.doi.org/10.1177/1548512912458482.

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19

Lee, S., and W. W. Ro. "Accelerated Network Coding with Dynamic Stream Decomposition on Graphics Processing Unit." Computer Journal 55, no. 1 (2010): 21–34. http://dx.doi.org/10.1093/comjnl/bxq087.

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20

Dai, Yuan, Yong Fang, Long Yang, and Gwanggil Jeon. "Graphics processing unit-accelerated joint-bitplane belief propagation algorithm in DSC." Journal of Supercomputing 72, no. 6 (2016): 2351–75. http://dx.doi.org/10.1007/s11227-016-1736-5.

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21

Zimmerman, Ben J., and Bong Wie. "Graphics-Processing-Unit-Accelerated Multiphase Computational Tool for Asteroid Fragmentation/Pulverization Simulation." AIAA Journal 55, no. 2 (2017): 599–609. http://dx.doi.org/10.2514/1.j055163.

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22

Nguyen, Van-Giang, and Soo-Jin Lee. "Graphics processing unit-accelerated iterative tomographic reconstruction with strip-integral system model." Optical Engineering 51, no. 9 (2012): 093203–1. http://dx.doi.org/10.1117/1.oe.51.9.093203.

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23

Lee, Jieun, Faliu Yi, Rao Saifullah, and Inkyu Moon. "Graphics processing unit–accelerated double random phase encoding for fast image encryption." Optical Engineering 53, no. 11 (2014): 112308. http://dx.doi.org/10.1117/1.oe.53.11.112308.

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24

Zhang, Jiale, Hongquan Chen, and Cheng Cao. "A graphics processing unit-accelerated meshless method for two-dimensional compressible flows." Engineering Applications of Computational Fluid Mechanics 11, no. 1 (2017): 526–43. http://dx.doi.org/10.1080/19942060.2017.1317027.

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25

Schive, Hsi-Yu, Yu-Chih Tsai, and Tzihong Chiueh. "GAMER : A GRAPHIC PROCESSING UNIT ACCELERATED ADAPTIVE-MESH-REFINEMENT CODE FOR ASTROPHYSICS." Astrophysical Journal Supplement Series 186, no. 2 (2010): 457–84. http://dx.doi.org/10.1088/0067-0049/186/2/457.

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26

Krishnan, Guhan, Dan Bouvier, and Samuel Naffziger. "Energy-Efficient Graphics and Multimedia in 28-nm Carrizo Accelerated Processing Unit." IEEE Micro 36, no. 2 (2016): 22–33. http://dx.doi.org/10.1109/mm.2016.24.

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27

NI Xiao-long, 倪小龙, 刘智 LIU Zhi, 姜会林 JIANG Hui-lin, et al. "Partially Coherent Beam Real-Time Generation Method Accelerated by Graphic Processing Unit." ACTA PHOTONICA SINICA 45, no. 3 (2016): 310001. http://dx.doi.org/10.3788/gzxb20164503.0310001.

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28

Liew, Andrew, Tom VanMele, and Philippe Block. "Vectorised graphics processing unit accelerated dynamic relaxation for bar and beam elements." Structures 8 (November 2016): 111–20. http://dx.doi.org/10.1016/j.istruc.2016.09.002.

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29

Kang, Minseon, Yongseok Lee, and Moonju Park. "Energy Efficiency of Machine Learning in Embedded Systems Using Neuromorphic Hardware." Electronics 9, no. 7 (2020): 1069. http://dx.doi.org/10.3390/electronics9071069.

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Recently, the application of machine learning on embedded systems has drawn interest in both the research community and industry because embedded systems located at the edge can produce a faster response and reduce network load. However, software implementation of neural networks on Central Processing Units (CPUs) is considered infeasible in embedded systems due to limited power supply. To accelerate AI processing, the many-core Graphics Processing Unit (GPU) has been a preferred device to the CPU. However, its energy efficiency is not still considered to be good enough for embedded systems. Among other approaches for machine learning on embedded systems, neuromorphic processing chips are expected to be less power-consuming and overcome the memory bottleneck. In this work, we implemented a pedestrian image detection system on an embedded device using a commercially available neuromorphic chip, NM500, which is based on NeuroMem technology. The NM500 processing time and the power consumption were measured as the number of chips was increased from one to seven, and they were compared to those of a multicore CPU system and a GPU-accelerated embedded system. The results show that NM500 is more efficient in terms of energy required to process data for both learning and classification than the GPU-accelerated system or the multicore CPU system. Additionally, limits and possible improvement of the current NM500 are identified based on the experimental results.

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30

LI, XIQI, GUOHUA SHI, and YUDONG ZHANG. "TIME-DOMAIN INTERPOLATION ON GRAPHICS PROCESSING UNIT." Journal of Innovative Optical Health Sciences 04, no. 01 (2011): 89–95. http://dx.doi.org/10.1142/s1793545811001277.

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The signal processing speed of spectral domain optical coherence tomography (SD-OCT) has become a bottleneck in a lot of medical applications. Recently, a time-domain interpolation method was proposed. This method can get better signal-to-noise ratio (SNR) but much-reduced signal processing time in SD-OCT data processing as compared with the commonly used zero-padding interpolation method. Additionally, the resampled data can be obtained by a few data and coefficients in the cutoff window. Thus, a lot of interpolations can be performed simultaneously. So, this interpolation method is suitable for parallel computing. By using graphics processing unit (GPU) and the compute unified device architecture (CUDA) program model, time-domain interpolation can be accelerated significantly. The computing capability can be achieved more than 250,000 A-lines, 200,000 A-lines, and 160,000 A-lines in a second for 2,048 pixel OCT when the cutoff length is L = 11, L = 21, and L = 31, respectively. A frame SD-OCT data (400A-lines × 2,048 pixel per line) is acquired and processed on GPU in real time. The results show that signal processing time of SD-OCT can be finished in 6.223 ms when the cutoff length L = 21, which is much faster than that on central processing unit (CPU). Real-time signal processing of acquired data can be realized.

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31

Manglayev, Talgat, Refik Kizilirmak, and Nor Hamid. "GPU Accelerated PIC and SIC for OFDM-NOMA." Electronics 8, no. 3 (2019): 257. http://dx.doi.org/10.3390/electronics8030257.

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Non-orthogonal multiple access (NOMA) is a candidate multiple access scheme for the fifth-generation (5G) cellular networks. In NOMA systems, all users operate at the same frequency and time, which poses a challenge in the decoding process at the receiver side. In this work, the two most popular receiver structures, successive interference cancellation (SIC) and parallel interference cancellation (PIC) receivers, for NOMA reverse channel are implemented on a graphics processing unit (GPU) and compared. Orthogonal frequency division multiplexing (OFDM) is considered. The high computational complexity of interference cancellation receivers undermines the potential deployment of NOMA systems. GPU acceleration, however, challenges this weakness, and our numerical results show speedups of about from 75–220-times as compared to a multi-thread implementation on a central processing unit (CPU). SIC and PIC multi-thread execution time on different platforms reveals the potential of GPU in wireless communications. Furthermore, the successful decoding rates of the SIC and PIC are evaluated and compared in terms of bit error rate.

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32

Peng, Kuan, Ling He, Ziqiang Zhu, Jingtian Tang, and Jiaying Xiao. "Three-dimensional photoacoustic tomography based on graphics-processing-unit-accelerated finite element method." Applied Optics 52, no. 34 (2013): 8270. http://dx.doi.org/10.1364/ao.52.008270.

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33

Eghtesad, Adnan, Kai Germaschewski, Irene J. Beyerlein, Abigail Hunter, and Marko Knezevic. "Graphics processing unit accelerated phase field dislocation dynamics: Application to bi-metallic interfaces." Advances in Engineering Software 115 (January 2018): 248–67. http://dx.doi.org/10.1016/j.advengsoft.2017.09.010.

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34

Wu, Xianyun, Keyan Wang, Yunsong Li, Kai Liu, and Bormin Huang. "Accelerating Haze Removal Algorithm Using CUDA." Remote Sensing 13, no. 1 (2020): 85. http://dx.doi.org/10.3390/rs13010085.

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The dark channel prior (DCP)-based single image removal algorithm achieved excellent performance. However, due to the high complexity of the algorithm, it is difficult to satisfy the demands of real-time processing. In this article, we present a Graphics Processing Unit (GPU) accelerated parallel computing method for the real-time processing of high-definition video haze removal. First, based on the memory access pattern, we propose a simple but effective filter method called transposed filter combined with the fast local minimum filter algorithm and integral image algorithm. The proposed method successfully accelerates the parallel minimum filter algorithm and the parallel mean filter algorithm. Meanwhile, we adopt the inter-frame atmospheric light constraint to suppress the flicker noise in the video haze removal and simplify the estimation of atmospheric light. Experimental results show that our implementation can process the 1080p video sequence with 167 frames per second. Compared with single thread Central Processing Units (CPU) implementation, the speedup is up to 226× with asynchronous stream processing and qualified for the real-time high definition video haze removal.

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35

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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36

Shi, Yulin, Alexander V. Veidenbaum, Alex Nicolau, and Xiangmin Xu. "Large-scale neural circuit mapping data analysis accelerated with the graphical processing unit (GPU)." Journal of Neuroscience Methods 239 (January 2015): 1–10. http://dx.doi.org/10.1016/j.jneumeth.2014.09.022.

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37

Yu, Fengchao, Huafeng Liu, Zhenghui Hu, and Pengcheng Shi. "Graphics processing unit (GPU)-accelerated particle filter framework for positron emission tomography image reconstruction." Journal of the Optical Society of America A 29, no. 4 (2012): 637. http://dx.doi.org/10.1364/josaa.29.000637.

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38

Zhang Pingyu, 张萍宇. "Parallel Accelerated Reconstruction Method for Dual-Energy Computed Tomography Based on Graphics Processing Unit." Laser & Optoelectronics Progress 57, no. 12 (2020): 121001. http://dx.doi.org/10.3788/lop57.121001.

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39

Chessa, Manuela, and Giulia Pasquale. "Graphics processing unit-accelerated techniques for bio-inspired computation in the primary visual cortex." Concurrency and Computation: Practice and Experience 26, no. 10 (2013): 1799–818. http://dx.doi.org/10.1002/cpe.3118.

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40

Merelli, Ivan, Horacio Pérez-Sánchez, Sandra Gesing, and Daniele D'Agostino. "Latest advances in distributed, parallel, and graphic processing unit accelerated approaches to computational biology." Concurrency and Computation: Practice and Experience 26, no. 10 (2013): 1699–704. http://dx.doi.org/10.1002/cpe.3111.

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41

Rymut, Boguslaw, and Bogdan Kwolek. "Real-time multiview human pose tracking using graphics processing unit-accelerated particle swarm optimization." Concurrency and Computation: Practice and Experience 27, no. 6 (2014): 1551–63. http://dx.doi.org/10.1002/cpe.3329.

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42

Itu, Lucian, Puneet Sharma, Ali Kamen, Constantin Suciu, and Dorin Comaniciu. "Graphics processing unit accelerated one-dimensional blood flow computation in the human arterial tree." International Journal for Numerical Methods in Biomedical Engineering 29, no. 12 (2013): 1428–55. http://dx.doi.org/10.1002/cnm.2585.

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43

Yang, Owen, and Bernard Choi. "Accelerated rescaling of single Monte Carlo simulation runs with the Graphics Processing Unit (GPU)." Biomedical Optics Express 4, no. 11 (2013): 2667. http://dx.doi.org/10.1364/boe.4.002667.

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44

Leung, Chi-Sing, Ping-Man Lam, P. W. M. Tsang, and Wuchao Situ. "A Graphics Processing Unit Accelerated Genetic Algorithm for Affine Invariant Matching of Broken Contours." Journal of Signal Processing Systems 66, no. 2 (2011): 105–11. http://dx.doi.org/10.1007/s11265-011-0582-1.

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45

Shrikant, Pawar, Stanam Aditya, and Zhu Ying. "Evaluating the computing efficiencies (specificity and sensitivity) of graphics processing unit (GPU)-accelerated DNA sequence alignment tools against central processing unit (CPU) alignment tool." Journal of Bioinformatics and Sequence Analysis 9, no. 2 (2018): 10–14. http://dx.doi.org/10.5897/jbsa2018.0109.

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46

JIANG, CHAO, HENG HE, PENGCHENG LI, and QINGMING LUO. "GRAPHICS PROCESSING UNIT CLUSTER ACCELERATED MONTE CARLO SIMULATION OF PHOTON TRANSPORT IN MULTI-LAYERED TISSUES." Journal of Innovative Optical Health Sciences 05, no. 02 (2012): 1250004. http://dx.doi.org/10.1142/s1793545812500046.

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We present a graphics processing unit (GPU) cluster-based Monte Carlo simulation of photon transport in multi-layered tissues. The cluster is composed of multiple computing nodes in a local area network where each node is a personal computer equipped with one or several GPU(s) for parallel computing. In this study, the MPI (Message Passing Interface), the OpenMP (Open Multi-Processing) and the CUDA (Compute Unified Device Architecture) technologies are employed to develop the program. It is demonstrated that this designing runs roughly N times faster than that using single GPU when the GPUs within the cluster are of the same type, where N is the total number of the GPUs within the cluster.

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47

DAI, ZIBIN, LONGMEI NAN, XUAN YANG, and XIAONAN LI. "DESIGN AND IMPLEMENTATION OF CONFIGURABLE LFSR INSTRUCTIONS TARGETED AT STREAM CIPHER PROCESSING." Journal of Circuits, Systems and Computers 22, no. 10 (2013): 1340036. http://dx.doi.org/10.1142/s0218126613400367.

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By analyzing the operation characteristic of linear feedback shifter registers (LFSRs) in many public stream cipher algorithms and its bottleneck realized by general processor, each specific instruction and reconfigurable hardware cell are proposed in this paper, which can neatly execute LFSR computing operation in parallel with high performance. The LFSR instructions can sustain different operation data widths, different operating models. Instruction-level parallelism based on VLIW system structure and instruction inner parallelism by operating several steps at one time are exploited too. Corresponding reconfigurable hardware units to sustain the implementation of each instruction forcefully by configurating is also developed. The circuit can be used as an important accelerated unit in special processing for stream cipher.

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48

Gupta, Mayank, Anagha Choudhhari, and N. A. Pande. "Accelerated Electromagnetic Field Simulation on Graphical Processing Unit by the Finite Difference Time Domain Method." International Journal of Computer Applications 70, no. 27 (2013): 34–36. http://dx.doi.org/10.5120/12242-8505.

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49

Sisto, Aaron, David R. Glowacki, and Todd J. Martinez. "Ab Initio Nonadiabatic Dynamics of Multichromophore Complexes: A Scalable Graphical-Processing-Unit-Accelerated Exciton Framework." Accounts of Chemical Research 47, no. 9 (2014): 2857–66. http://dx.doi.org/10.1021/ar500229p.

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50

Fierro, Andrew, James Dickens, and Andreas Neuber. "Graphics processing unit accelerated three-dimensional model for the simulation of pulsed low-temperature plasmas." Physics of Plasmas 21, no. 12 (2014): 123504. http://dx.doi.org/10.1063/1.4903330.

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