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Relevant bibliographies by topics / GPU Accelerated Algorithms

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Academic literature on the topic 'GPU Accelerated Algorithms'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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Journal articles on the topic "GPU Accelerated Algorithms"

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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 improvement

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Zang, Chuantao, and Koichi Hashimoto. "GPU Acceleration in a Visual Servo System." Journal of Robotics and Mechatronics 24, no. 1 (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 G

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Blanchard, Jeffrey D., and Jared Tanner. "GPU accelerated greedy algorithms for compressed sensing." Mathematical Programming Computation 5, no. 3 (2013): 267–304. http://dx.doi.org/10.1007/s12532-013-0056-5.

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Jin, Jing, Xianggao Cai, Guoming Lai, and Xiaola Lin. "GPU-accelerated parallel algorithms for linear rankSVM." Journal of Supercomputing 71, no. 11 (2015): 4141–71. http://dx.doi.org/10.1007/s11227-015-1509-6.

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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 fo

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Schock, Jonathan, Schulz Michael, and Franz Pfeiffer. "GPU Accelerated Image Processing in CCD-Based Neutron Imaging." Journal of Imaging 4, no. 9 (2018): 104. http://dx.doi.org/10.3390/jimaging4090104.

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Image processing is an important step in every imaging path in the scientific community. Especially in neutron imaging, image processing is very important to correct for image artefacts that arise from low light and high noise statistics. Due to the low global availability of neutron sources suitable for imaging, the development of these algorithms is not in the main scope of research work and once established, algorithms are not revisited for a long time and therefore not optimized for high throughput. This work shows the possible speed gain that arises from the usage of heterogeneous computi

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Chakroun, I., and N. Melab. "Operator-level GPU-Accelerated Branch and Bound Algorithms." Procedia Computer Science 18 (2013): 280–89. http://dx.doi.org/10.1016/j.procs.2013.05.191.

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Wang, Hongzhi, Zheng Wang, Ning Li, and Xinxin Kong. "Efficient OLAP algorithms on GPU-accelerated Hadoop clusters." Distributed and Parallel Databases 37, no. 4 (2018): 507–42. http://dx.doi.org/10.1007/s10619-018-7239-z.

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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 (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 contrib

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Date, Ketan, and Rakesh Nagi. "GPU-accelerated Hungarian algorithms for the Linear Assignment Problem." Parallel Computing 57 (September 2016): 52–72. http://dx.doi.org/10.1016/j.parco.2016.05.012.

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Dissertations / Theses on the topic "GPU Accelerated Algorithms"

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Renison, Carina Alicia. "The design and development of GPU accelerated algorithms for ab initio integrals and integral derivatives illustrated on ab initio quantum and hybrid QM/MM dynamics." Doctoral thesis, University of Cape Town, 2016. http://hdl.handle.net/11427/23054.

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Graphical Processing Units (GPUs) are highly parallel, programmable accelerators boasting high peak floating point performance. Over the last couple of years the use of GPUs for general purpose computing have revolutionized quantum chemistry. The computational bottleneck in an ab-initio quantum method is the calculation of a large number of twoelectron integrals. To date, a number of GPU accelerated two-electron integral implementations have been developed significantly improving the performance of a static quantum mechanical (QM) calculation. However, when performing an ab-initio QM gradient

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Rémy, Adrien. "Solving dense linear systems on accelerated multicore architectures." Thesis, Paris 11, 2015. http://www.theses.fr/2015PA112138/document.

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Dans cette thèse de doctorat, nous étudions des algorithmes et des implémentations pour accélérer la résolution de systèmes linéaires denses en utilisant des architectures composées de processeurs multicœurs et d'accélérateurs. Nous nous concentrons sur des méthodes basées sur la factorisation LU. Le développement de notre code s'est fait dans le contexte de la bibliothèque MAGMA. Tout d'abord nous étudions différents solveurs CPU/GPU hybrides basés sur la factorisation LU. Ceux-ci visent à réduire le surcoût de communication dû au pivotage. Le premier est basé sur une stratégie de pivotage di

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Bao, Xin. "Sketch-based intuitive 3D model deformations." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/sketchbased-intuitive-3d-model-deformations(2c12a1f9-cf0c-45d1-926e-a5f3db0d5acb).html.

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In 3D modelling software, deformations are used to add, to remove, or to modify geometric features of existing 3D models to create new models with similar but slightly different details. Traditional techniques for deforming virtual 3D models require users to explicitly define control points and regions of interest (ROIs), and to define precisely how to deform ROIs using control points. The awkwardness of defining these factors in traditional 3D modelling software makes it difficult for people with limited experience of 3D modelling to deform existing 3D models as they expect. As applications w

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Wang, Kaibo. "Algorithmic and Software System Support to Accelerate Data Processing in CPU-GPU Hybrid Computing Environments." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1447685368.

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Öhberg, Tomas. "Auto-tuning Hybrid CPU-GPU Execution of Algorithmic Skeletons in SkePU." Thesis, Linköpings universitet, Programvara och system, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-149605.

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The trend in computer architectures has for several years been heterogeneous systems consisting of a regular CPU and at least one additional, specialized processing unit, such as a GPU.The different characteristics of the processing units and the requirement of multiple tools and programming languages makes programming of such systems a challenging task. Although there exist tools for programming each processing unit, utilizing the full potential of a heterogeneous computer still requires specialized implementations involving multiple frameworks and hand-tuning of parameters.To fully exploit t

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Jamal, Aygul. "A parallel iterative solver for large sparse linear systems enhanced with randomization and GPU accelerator, and its resilience to soft errors." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS269/document.

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Dans cette thèse de doctorat, nous abordons trois défis auxquels sont confrontés les solveurs d'algèbres linéaires dans la perspective des futurs systèmes exascale: accélérer la convergence en utilisant des techniques innovantes au niveau algorithmique, en profitant des accélérateurs GPU (Graphics Processing Units) pour améliorer le calcul sur plusieurs systèmes, en évaluant l'impact des erreurs due à l'augmentation du parallélisme dans les superordinateurs. Nous nous intéressons à l'étude des méthodes permettant d'accélérer la convergence et le temps d'exécution des solveurs itératifs pour le

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Pai, Nithish. "A GPU Accelerated Tensor Spectral Method for Subspace Clustering." Thesis, 2016. http://etd.iisc.ernet.in/handle/2005/2837.

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In this thesis we consider the problem of clustering the data lying in a union of subspaces using spectral methods. Though the data generated may have high dimensionality, in many of the applications, such as motion segmentation and illumination invariant face clustering, the data resides in a union of subspaces having small dimensions. Furthermore, for a number of classification and inference problems, it is often useful to identify these subspaces and work with data in this smaller dimensional manifold. If the observations in each cluster were to be distributed around a centric, applying spe

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Jhih-MingLin and 林志明. "Inpainting-based Multi-view Synthesis Algorithms and Its GPU Accelerated Implementation." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/38951848542854512448.

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碩士<br>國立成功大學<br>電機工程學系碩博士班<br>100<br>In this thesis, inpainting-based multi-view synthesis algorithms for 2-views glasses stereo or 9-views naked-eye display systems are proposed when their inputs are with one color image and depth map. Mostly, the depth-image-based rendering (DIBR) algorithms will produce some holes and cracks in the multi-view synthesized images. How to fill up holes and crack in multi-view synthesized image becomes an important issue for providing high quality 3D views. This work proposes a texture-based interpolation method, which can fix cracks in the image based on textu

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Tsai, Pin-Yi, and 蔡品儀. "A GPU-Accelerated Object Recognition System Using Adaboost Algorithm." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/11715804199876439565.

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碩士<br>國立清華大學<br>資訊系統與應用研究所<br>103<br>Recently, programming on GPU has become the general solution of high performance computing. Various applications and frameworks are developed to utilize the power of GPU. CUDA, proposed by Nvidia, enables programmers who are not major in computer vision also benefit from GPU easily. CUDA is only available on Nvidia’s GPU; for other GPUs, OpenCL can be applied to deal with the similar work. OpenCL supports cross-platform programming and also can cooperate with CPU. However, the powerful advantage is obvious only when the data is massive. If the data is n

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李智偉. "Using CUDA GPU to Accelerate the Longest Common Subsequence Algorithm." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/22741243027393456051.

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碩士<br>國立彰化師範大學<br>資訊工程學系<br>102<br>This thesis is to study how to use the emerging graphics processing units (GPU) to improve the longest common subsequence (LCS) problem, which widely used in the field of bioinformatics. To parallelize the LCS problem, the diagonal method suffers the problem that the parallelism is not uniformly distributed across different phases while the row-major method requires a high overhead synchronization mechanism to enforce the data consistency between thread blocks. To address these problems, we propose adopting the inter-block synchronization method to reduce the

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Book chapters on the topic "GPU Accelerated Algorithms"

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Osama, Muhammad, Anton Wijs, and Armin Biere. "SAT Solving with GPU Accelerated Inprocessing." In Tools and Algorithms for the Construction and Analysis of Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72016-2_8.

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AbstractSince 2013, the leading SAT solvers in the SAT competition all use inprocessing, which unlike preprocessing, interleaves search with simplifications. However, applying inprocessing frequently can still be a bottle neck, i.e., for hard or large formulas. In this work, we introduce the first attempt to parallelize inprocessing on GPU architectures. As memory is a scarce resource in GPUs, we present new space-efficient data structures and devise a data-parallel garbage collector. It runs in parallel on the GPU to reduce memory consumption and improves memory access locality. Our new parallel variable elimination algorithm is twice as fast as previous work. In experiments our new solver ParaFROST solves many benchmarks faster on the GPU than its sequential counterparts.

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Huang, Haibing, Mingming Ren, Yue Zhao, et al. "GPU-Accelerated Block-Max Query Processing." In Algorithms and Architectures for Parallel Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65482-9_15.

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Honda, Takumi, Yasuaki Ito, and Koji Nakano. "GPU-Accelerated Verification of the Collatz Conjecture." In Algorithms and Architectures for Parallel Processing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11197-1_37.

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Cai, Xianggao, Zhanpeng Xu, Guoming Lai, Chengwei Wu, and Xiaola Lin. "GPU-Accelerated Restricted Boltzmann Machine for Collaborative Filtering." In Algorithms and Architectures for Parallel Processing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33078-0_22.

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Schmidt, Christopher, Johannes Huegle, Siegfried Horschig, and Matthias Uflacker. "Out-of-Core GPU-Accelerated Causal Structure Learning." In Algorithms and Architectures for Parallel Processing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38991-8_7.

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Wijs, Anton. "GPU Accelerated Strong and Branching Bisimilarity Checking." In Tools and Algorithms for the Construction and Analysis of Systems. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46681-0_29.

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Altuntaş, Serkan, Zeki Bozkus, and Basilio B. Fraguela. "GPU Accelerated Molecular Docking Simulation with Genetic Algorithms." In Applications of Evolutionary Computation. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31153-1_10.

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Mathieu, Christian, and Matthias Klusch. "Accelerated Steiner Tree Problem Solving on GPU with CUDA." In Algorithms and Architectures for Parallel Processing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27122-4_31.

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Deveci, Mehmet, Kamer Kaya, Bora Uçar, and Ümit V. Çatalyürek. "GPU Accelerated Maximum Cardinality Matching Algorithms for Bipartite Graphs." In Euro-Par 2013 Parallel Processing. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40047-6_84.

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Wang, Jingpeng, Jie Huang, Kaijie Xiao, and Zhixin Tian. "GPU-Accelerated Algorithm for Fast Computation of Biomolecular Isotopic Envelopes." In Algorithms and Architectures for Parallel Processing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27122-4_40.

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Conference papers on the topic "GPU Accelerated Algorithms"

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Konobrytskyi, Dmytro, Thomas Kurfess, Joshua Tarbutton, and Tommy Tucker. "GPGPU Accelerated 3-Axis CNC Machining Simulation." In ASME 2013 International Manufacturing Science and Engineering Conference collocated with the 41st North American Manufacturing Research Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/msec2013-1096.

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GPUs (Graphics Processing Units), traditionally used for 3D graphics calculations, have recently got an ability to perform general purpose calculations with a GPGPU (General Purpose GPU) technology. Moreover, GPUs can be much faster than CPUs (Central Processing Units) by performing hundreds or even thousands commands concurrently. This parallel processing allows the GPU achieving the extremely high performance but also requires using only highly parallel algorithms which can provide enough commands on each clock cycle. This work formulates a methodology for selection of a right geometry repre

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Jianbo Zhang, Wenxin Yang, Jing Sun, and Yonghong Lv. "GPU-accelerated parallel algorithms for map algebra." In 2010 2nd Conference on Environmental Science and Information Application Technology (ESIAT). IEEE, 2010. http://dx.doi.org/10.1109/esiat.2010.5567202.

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Wang, Liang, and Gang Wu. "GPU CUDA accelerated video inpainting using synthetic algorithms." In 2017 14th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). IEEE, 2017. http://dx.doi.org/10.1109/iccwamtip.2017.8301466.

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Blaß, Thorsten, and Michael Philippsen. "GPU-accelerated fixpoint algorithms for faster compiler analyses." In the 28th International Conference. ACM Press, 2019. http://dx.doi.org/10.1145/3302516.3307352.

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Jianqiang Dong and Bo Yuan. "GPU-Accelerated Standard and Multi-population Cultural Algorithms." In 2013 International Conference on Service Sciences (ICSS 2013). IEEE, 2013. http://dx.doi.org/10.1109/icss.2013.39.

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Kurella, Venu, Bob Stone, and Allan Spence. "GPU Accelerated Algorithms for CAD / Point Cloud Digitizer Data Registration." In CAD'16. CAD Solutions LLC, 2016. http://dx.doi.org/10.14733/cadconfp.2016.327-331.

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Yang, Chao-Tung, Jung-Chun Liu, Jheng-Yue Lee, Chih-Hung Chang, Chuan-Lin Lai, and Chia-Chen Kuo. "The Implementation of a Virtual Desktop Infrastructure with GPU Accelerated on OpenStack." In 2018 15th International Symposium on Pervasive Systems, Algorithms and Networks (I-SPAN). IEEE, 2018. http://dx.doi.org/10.1109/i-span.2018.00069.

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Pin-Chen Kuo, Jhih-Ming Lin, Bin-Da Liu, and Jar-Ferr Yang. "Inpainting-based multi-view synthesis algorithms and its GPU accelerated implementation." In 2013 9th International Conference on Information, Communications & Signal Processing (ICICS). IEEE, 2013. http://dx.doi.org/10.1109/icics.2013.6782828.

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Nesi, Lucas Leandro, Mauricio Aronne Pillon, Marcos Dias de Assuncao, and Guilherme Piegas Koslovski. "GPU-Accelerated Algorithms for Allocating Virtual Infrastructure in Cloud Data Centers." In 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). IEEE, 2018. http://dx.doi.org/10.1109/ccgrid.2018.00057.

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Herault, Thomas, Yves Robert, George Bosilca, and Jack Dongarra. "Generic Matrix Multiplication for Multi-GPU Accelerated Distributed-Memory Platforms over PaRSEC." In 2019 IEEE/ACM 10th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA). IEEE, 2019. http://dx.doi.org/10.1109/scala49573.2019.00010.

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