Relevant bibliographies by topics / GPU Accelerated

Academic literature on the topic 'GPU Accelerated'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "GPU Accelerated"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "GPU Accelerated"

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Lionetti, Fred. "GPU accelerated cardiac electrophysiology." Diss., [La Jolla] : University of California, San Diego, 2010. http://wwwlib.umi.com/cr/ucsd/fullcit?p1474756.

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Thesis (M.S.)--University of California, San Diego, 2010.
Title from first page of PDF file (viewed April 14, 2010). Available via ProQuest Digital Dissertations. Includes bibliographical references (p. 85-89).
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Mäkelä, J. (Jussi). "GPU accelerated face detection." Master's thesis, University of Oulu, 2013. http://urn.fi/URN:NBN:fi:oulu-201303181103.

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Graphics processing units have massive parallel processing capabilities, and there is a growing interest in utilizing them for generic computing. One area of interest is computationally heavy computer vision algorithms, such as face detection and recognition. Face detection is used in a variety of applications, for example the autofocus on cameras, face and emotion recognition, and access control. In this thesis, the face detection algorithm was accelerated with GPU using OpenCL. The goal was to gain performance benefit while keeping the implementations functionally equivalent. The OpenCL version was based on optimized reference implementation. The possibilities and challenges in accelerating different parts of the algorithm were studied. The reference and the accelerated implementations are depicted in detail, and performance is compared. The performance was evaluated by runtimes with three sets of four different sized images, and three additional images presenting special cases. The tests were run with two differently set-up computers. From the results, it can be seen that face detection is well suited for GPU acceleration; that is the algorithm is well parallelizable and can utilize efficient texture processing hardware. There are delays related in initializing the OpenCL platform which mitigate the benefit to some degree. The accelerated implementation was found to deliver equal or lower performance when there was little computation; that is the image was small or easily analyzed. With bigger and more complex images, the accelerated implementation delivered good performance compared to reference implementation. In future work, there should be some method of mitigating delays introduced by the OpenCL initialization. This work will have interest in the future when OpenCL acceleration becomes available on mobile phones
Grafiikkaprosessorit kykenevät massiiviseen rinnakkaislaskentaan ja niiden käyttö yleiseen laskentaan on kasvava kiinnostuksen aihe. Eräs alue missä kiihdytyksen käytöstä on kiinnostuttu on laskennallisesti raskaat konenäköalgoritmit kuten kasvojen ilmaisu ja tunnistus. Kasvojen ilmaisua käytetään useissa sovelluksissa, kuten kameroiden automaattitarkennuksessa, kasvojen ja tunteiden tunnistuksessa sekä kulun valvonnassa. Tässä työssä kasvojen ilmaisualgoritmia kiihdytettiin grafiikkasuorittimella käyttäen OpenCL-rajapintaa. Työn tavoite oli parantunut suorituskyky kuitenkin niin että implementaatiot pysyivät toiminnallisesti samanlaisina. OpenCL-versio perustui optimoituun verrokki-implementaatioon. Algoritmin eri vaiheiden kiihdytyksen mahdollisuuksia ja haasteita on tutkittu. Kiihdytetty- ja verrokki-implementaatio kuvaillaan ja niiden välistä suorituskykyeroa vertaillaan. Suorituskykyä arvioitiin ajoaikojen perusteella. Testeissä käytettiin kolmea kuvasarjaa joissa jokaisessa oli neljä eri kokoista kuvaa sekä kolmea lisäkuvaa jotka kuvastivat erikoistapauksia. Testit ajettiin kahdella erilailla varustellulla tietokoneella. Tuloksista voidaan nähdä että kasvojen ilmaisu soveltuu hyvin GPU kiihdytykseen, sillä algoritmin pystyy rinnakkaistamaan ja siinä pystyy käyttämään tehokasta tekstuurinkäsittelylaitteistoa. OpenCL-ympäristön alustaminen aiheuttaa viivettä joka vähentää jonkin verran suorituskykyetua. Testeissä todettiin kiihdytetyn implementaation antavan saman suuruisen tai jopa pienemmän suorituskyvyn kuin verrokki-implementaatio sellaisissa tapauksissa, joissa laskentaa oli vähän johtuen joko pienestä tai helposti käsiteltävästä kuvasta. Toisaalta kiihdytetyn implementaation suorituskyky oli hyvä verrattuna verrokki-implementaatioon kun käytettiin suuria ja monimutkaisia kuvia. Tulevaisuudessa OpenCL-ympäristön alustamisen aiheuttamat viivettä tulisi saada vähennettyä. Tämä työ on kiinnostava myös tulevaisuudessa kun OpenCL-kiihdytys tulee mahdolliseksi matkapuhelimissa
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Graves, Alex. "GPU-Accelerated Feature Tracking." Wright State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1462372516.

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Baravdish, Gabriel. "GPU Accelerated Light Field Compression." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-150558.

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This thesis presents a GPU accelerated method to compress light field or light field videos. The implementation is based on an earlier work of a full light field compression framework. The large amount of data storage by capturing light fields is a challenge to compress and we seek to accelerate the encoding part. We compress by projecting each data point onto a set of dictionaries and seek a sparse representation with the least error. An optimized greedy algorithm to suit computations on the GPU is presented. We benefit of the algorithm outline by encoding the data segmentally in parallel for faster computation speed while maintaining the quality. The results shows a significantly faster encoding time compared to the results in the same research field. We conclude that there are further improvements to increase the speed, and thus it is not too far from an interactive compression speed.
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Kottravel, Sathish. "GPU accelerated Nonlinear Soft Tissue Deformation." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-76895.

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There are two types of structures in human body, solid organs and hollow membrane like organs. Brain, liver and other soft tissues such as tendons, muscles, cartilage etc., are examples of solid organs. Colon and blood vessels are examples of hollow organs. They greatly differ in structure and mechanical behavior. Deformation of these types of structures is an important phenomena during the process of medical simulation. The primary focus of this project is on deformation of soft tissues. These kind of soft tissues usually undergo large deformation. Deformation of an organ can be considered as mechanical response of that organ during medical simulation. This can be modeled using continuum mechanics and FEM. The primary goal of any system, irrespective of methods and models chosen, it must provide real-time response to obtain sufficient realism and accurate information. One such example is medical training system using haptic feedback. In the past two decades many models were developed and very few considered the non-linear nature in material and geometry of the solid organs. TLED is one among them. A finite element formulation proposed by Miller in 2007, known as total Lagrangian explicit dynamics (TLED) algorithm, will be discussed with respect to implementation point of view and deploying GPU acceleration (because of its parallel nature to some extent) for both pre-processing and actual computation.
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Edespong, Erik. "GPU Accelerated Surface Reconstruction from Particles." Thesis, Linköpings universitet, Institutionen för teknik och naturvetenskap, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-93543.

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Realistic uid eects, such as smoke and water has been pursued by the visual eects industry for a long time. In recent years, particle simulations have gained a lot of popularity for achieving such eects. One problem noted by researchers has been the diculty of generating surfaces from the particles. This thesis investigates current techniques for particle surface reconstruction. In addition to this, a GPU-based implementation using constrained mesh smoothing is described. The result is globally smooth surfaces which closely follows the distribution of the particles, though some problems are still apparent. The performance of the algortihm is approximately an order of magnitude faster than its CPU counterpart, but is clogged by bottlenecks in sections still runnning on the CPU.
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BASTOS, THIAGO DE ALMEIDA. "GPU-ACCELERATED ADAPTIVELY SAMPLED DISTANCE FIELDS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2008. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=12160@1.

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A representação de formas é um problema fundamental em Computação Gráfica. Dentre as representações conhecidas para objetos tridimensionais, os campos de distância amostrados adaptativamente (ADFs) destacam-se por sua versatilidade. ADFs combinam os conceitos de geometria com dados volumétricos, permitem representar objetos com precisão arbitrária, e consolidam diversas operações como visualização, modelagem de níveis de detalhe, detecção de colisão, testes de proximidade, metamorfose e operações booleanas em uma única representação. Este trabalho propõe métodos para acelerar a reconstrução de ADFs estáticas, melhorar a qualidade dos campos reconstruídos, e visualizar iso-superfícies das ADFs, valendo-se do enorme poder computacional encontrado nas placas gráficas modernas (GPUs). Para que as ADFs sejam representadas de forma eficiente em placas gráficas, propõe-se o uso de uma estrutura hierárquica baseada em dispersão espacial perfeita. A renderização de ADFs é feita integralmente pela GPU, utilizando uma técnica de lançamento de raios baseada em traçado por esferas. Uma maneira de tratar as descontinuidades C0 e C1 inerentes às ADFs é sugerida para que o sombreamento das superfícies seja suave. Finalmente, o trabalho propõe um novo método de reconstrução para ADFs, capaz de representar melhor superfícies curvas. Os resultados são apresentados através de aplicações simples de visualização interativa, com ADFs geradas a partir de malhas de triângulos e sólidos primitivos.
Shape representation is a fundamental problem in Computer Graphics. Among known representations for three-dimensional objects, adaptively sampled distance fields (ADFs) are noted for their versatility. ADFs combine the concepts of geometry with volume data, allow objects to be represented with arbitrary precision, and consolidate several operations - such as visualization, level-of-detail modeling, collision detection, proximity tests, morphing and boolean operations | into a single representation. This work proposes methods to accelerate the reconstruction of static ADFs, to improve the quality of reconstructed fields, and to visualize ADF isosurfaces, making use of the massive computational power found in modern graphics hardware (GPUs). In order to effciently represent ADFs on graphics cards, a hierarchical structure based on perfect spatial hashing is proposed. Rendering of ADFs is done completely on GPUs, using a ray casting technique based on sphere tracing. Means to overcome the C0 and C1 discontinuities inherent to ADFs are suggested in order to attain smoothly shaded iso-surfaces. Finally, a new reconstruction method for ADFs, which can better represent curved surfaces, is proposed. Results are presented through simple interactive visualization applications, with ADFs generated from both triangle meshes and primitive solids.
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Zhao, Kaiyong. "GPU accelerated sequence alignment /Zhao Kaiyong." HKBU Institutional Repository, 2016. https://repository.hkbu.edu.hk/etd_oa/378.

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DNA sequence alignment is a fundamental task in gene information processing, which is about searching the location of a string (usually based on newly collected DNA data) in the existing huge DNA sequence databases. Due to the huge amount of newly generated DNA data and the complexity of approximate string match, sequence alignment becomes a time-consuming process. Hence how to reduce the alignment time becomes a significant research problem. Some algorithms of string alignment based on HASH comparison, suffix array and BWT, which have been proposed for DNA sequence alignment. Although these algorithms have reached the speed of O(N), they still cannot meet the increasing demand if they are running on traditional CPUs. Recently, GPUs have been widely accepted as an efficient accelerator for many scientific and commercial applications. A typical GPU has thousands of processing cores which can speed up repetitive computations significantly as compared to multi-core CPUs. However, sequence alignment is one kind of computation procedure with intensive data access, i.e., it is memory-bounded. The access to GPU memory and IO has more significant influence in performance when compared to the computing capabilities of GPU cores. By analyzing GPU memory and IO characteristics, this thesis produces novel parallel algorithms for DNA sequence alignment applications. This thesis consists of six parts. The first two parts explain some basic knowledge of DNA sequence alignment and GPU computing. The third part investigates the performance of data access on different types of GPU memory. The fourth part describes a parallel method to accelerate short-read sequence alignment based on BWT algorithm. The fifth part proposes the parallel algorithm for accelerating BLASTN, one of the most popular sequence alignment software. It shows how multi-threaded control and multiple GPU cards can accelerate the BLASTN algorithm significantly. The sixth part concludes the whole thesis. To summarize, through analyzing the layout of GPU memory and comparing data under the mode of multithread access, this thesis analyzes and concludes a perfect optimization method to achieve sequence alignment on GPU. The outcomes can help practitioners in bioinformatics to improve their working efficiency by significantly reducing the sequence alignment time.
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Schmitt, Ryan Daniel. "GPU-Accelerated Point-Based Color Bleeding." DigitalCommons@CalPoly, 2012. https://digitalcommons.calpoly.edu/theses/810.

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Traditional global illumination lighting techniques like Radiosity and Monte Carlo sampling are computationally expensive. This has prompted the development of the Point-Based Color Bleeding (PBCB) algorithm by Pixar in order to approximate complex indirect illumination while meeting the demands of movie production; namely, reduced memory usage, surface shading independent run time, and faster renders than the aforementioned lighting techniques. The PBCB algorithm works by discretizing a scene’s directly illuminated geometry into a point cloud (surfel) representation. When computing the indirect illumination at a point, the surfels are rasterized onto cube faces surrounding that point, and the constituent pixels are combined into the final, approximate, indirect lighting value. In this thesis we present a performance enhancement to the Point-Based Color Bleeding algorithm through hardware acceleration; our contribution incorporates GPU-accelerated rasterization into the cube-face raster phase. The goal is to leverage the powerful rasterization capabilities of modern graphics processors in order to speed up the PBCB algorithm over standard software rasterization. Additionally, we contribute a preprocess that generates triangular surfels that are suited for fast rasterization by the GPU, and show that new heterogeneous architecture chips (e.g. Sandy Bridge from Intel) simplify the code required to leverage the power of the GPU. Our algorithm reproduces the output of the traditional Monte Carlo technique with a speedup of 41.65x, and additionally achieves a 3.12x speedup over software-rasterized PBCB.
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Pettersson, Niklas. "GPU-Accelerated Real-Time Surveillance De-Weathering." Thesis, Linköpings universitet, Datorseende, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-97401.

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A fully automatic de-weathering system to increase the visibility/stability in surveillance applications during bad weather has been developed. Rain, snow and haze during daylight are handled in real-time performance with acceleration from CUDA implemented algorithms. Video from fixed cameras is processed on a PC with no need of special hardware except an NVidia GPU. The system does not use any background model and does not require any precalibration. Increase in contrast is obtained in all haze/rain/snow-cases while the system lags the maximum of one frame during rain or snow removal. De-hazing can be obtained for any distance to simplify tracking or other operating algorithms on a surveillance system.
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Books on the topic "GPU Accelerated"

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Annick, Le Goyat, ed. Skeleton Key: L'île de tous les dangers. [Paris]: Hachette Jeunesse, 2005.

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Skeleton Key. New York: Philomel Books, 2002.

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Skeleton key: An Alex Rider adventure. United States]: Paw Prints, 2008.

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Horowitz, Anthony. Skeleton Key. London: Walker Books Ltd, 2009.

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Skeleton key. London: Walker Books, 2014.

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Skeleton Key: An Alex Rider adventure. New York: Speak, 2006.

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Horowitz, Anthony. Skeleton Key. Paris: Hachette Jeunesse, 2012.

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Aissa, Mohamed Hassanine. GPU-accelerated CFD Simulations for Turbomachinery Design Optimization. von Karman Institute for Fluid Dynamics, 2018. http://dx.doi.org/10.35294/phdt201801.

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Vaidya, Bhaumik. Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA: Effective techniques for processing complex image data in real time using GPUs. Packt Publishing - ebooks Account, 2018.

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Horowitz, Anthony. Skeleton Key (Alex Rider). Puffin, 2006.

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Book chapters on the topic "GPU Accelerated"

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Liu, Yang, Wayne Huang, John Johnson, and Sheila Vaidya. "GPU Accelerated Smith-Waterman." In Computational Science – ICCS 2006, 188–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11758549_29.

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Krömer, Pavel, Jan Platoš, and Václav Snášel. "GPU Accelerated Genetic Clustering." In Lecture Notes in Computer Science, 410–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34859-4_41.

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Fleissner, Sebastian. "GPU-Accelerated Montgomery Exponentiation." In Computational Science – ICCS 2007, 213–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72584-8_28.

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Baúto, João, Rui Neves, and Nuno Horta. "GPU-Accelerated SAX/GA." In SpringerBriefs in Applied Sciences and Technology, 45–66. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73329-6_5.

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Ahmad, Mohamad Rosyidi, Ahmad Fakhri Arif Mat Zaid, Muhamad Husaini Abu Bakar, Mohd Fauzi Alias, and Pranesh Krishnan. "GPU Accelerated Speech Recognition." In Advanced Structured Materials, 59–69. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67750-3_6.

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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, 133–51. Cham: 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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Rizk, Guillaume, and Dominique Lavenier. "GPU Accelerated RNA Folding Algorithm." In Lecture Notes in Computer Science, 1004–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01970-8_101.

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Yang, Ying, Yu Gu, Chuanwen Li, Changyi Wan, and Ge Yu. "GPU-Accelerated Dynamic Graph Coloring." In Database Systems for Advanced Applications, 296–99. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18590-9_32.

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Cao, Jian, Xiao-fang Xie, Jie Liang, and De-dong Li. "GPU Accelerated Target Tracking Method." In Advances in Intelligent and Soft Computing, 251–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25989-0_42.

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Andrzejewski, Witold, and Pawel Boinski. "GPU-Accelerated Collocation Pattern Discovery." In Advances in Databases and Information Systems, 302–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40683-6_23.

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Conference papers on the topic "GPU Accelerated"

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Chakroun, Imen, Nick Michiels, and Roel Wuyts. "GPU-accelerated CellProfiler." In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2018. http://dx.doi.org/10.1109/bibm.2018.8621271.

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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 representation and a data structure suitable for parallel processing on GPU. Then the methodology is used for designing the 3-axis CNC milling simulation algorithm accelerated with the GPGPU technology. The developed algorithm is validated by performing an experimental machining simulation and evaluation of the performance results. The experimental simulation shows an importance of an optimization process and usage of algorithms that provide enough work to GPU. The used test configuration also demonstrates almost an order of magnitude difference between CPU and GPU performance results.
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Metlicka, Magdalena, Donald Davendra, Frank Hermann, Markus Meier, and Matthias Amann. "GPU accelerated NEH algorithm." In 2014 IEEE Symposium on Computational Intelligence in Production and Logistics Systems (CIPLS). IEEE, 2014. http://dx.doi.org/10.1109/cipls.2014.7007169.

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Sanaullah, Ahmed, Saiful A. Mojumder, Kathleen M. Lewis, and Martin C. Herbordt. "GPU-accelerated charge mapping." In 2016 IEEE High Performance Extreme Computing Conference (HPEC). IEEE, 2016. http://dx.doi.org/10.1109/hpec.2016.7761599.

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Van Ranst, Wiebe, Floris De Smedt, and Toon Goedemé. "GPU Accelerated ACF Detector." In International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0006585102420248.

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Dan Liu. "GPU accelerated background subtraction." In 2015 IEEE 16th International Conference on Communication Technology (ICCT). IEEE, 2015. http://dx.doi.org/10.1109/icct.2015.7399860.

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Graves, Alexander. "GPU-accelerated feature tracking." In 2016 IEEE National Aerospace and Electronics Conference (NAECON) and Ohio Innovation Summit (OIS). IEEE, 2016. http://dx.doi.org/10.1109/naecon.2016.7856842.

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Kong, Jiangang, and Yangdong Deng. "GPU accelerated face detection." In 2010 International Conference on Intelligent Control and Information Processing (ICICIP). IEEE, 2010. http://dx.doi.org/10.1109/icicip.2010.5564978.

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Schofield, Ian, and Amirhossein Alimohammad. "Parallel GPU-Accelerated Spike Sorting." In 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE). IEEE, 2019. http://dx.doi.org/10.1109/ccece.2019.8861978.

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Neophytou, N., and K. Mueller. "GPU accelerated image aligned splatting." In Volume Graphics 2005. IEEE, 2005. http://dx.doi.org/10.1109/vg.2005.194115.

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Reports on the topic "GPU Accelerated"

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Antz, Hartwig, Piotr Luszczek, Jack Dongarra, and Vinent Heuveline. GPU-Accelerated Asynchronous Error Correction for Mixed Precision Iterative Refinement. Office of Scientific and Technical Information (OSTI), December 2011. http://dx.doi.org/10.2172/1173289.

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Tognini, S., P. Canal, T. Evans, G. Lima, A. Lund, S. Johnson, S. Jun, V. Pascuzzi, and P. Romano. $Celeritas$: GPU-accelerated particle transport for detector simulation in High Energy Physics experiments. Office of Scientific and Technical Information (OSTI), March 2022. http://dx.doi.org/10.2172/1863002.

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Kolev, T. CEED-MS36: High-order algorithmic developments and optimizations for large-scale GPU-accelerated simulations. Office of Scientific and Technical Information (OSTI), March 2021. http://dx.doi.org/10.2172/1845639.

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Ryan, Benjamin, and Manichandra Morampudi. bolt: A GPU-Accelerated Solver for Marginally Collisional Kinetic Physics Using a High- Resolution Constrained Transport Scheme. Office of Scientific and Technical Information (OSTI), June 2022. http://dx.doi.org/10.2172/1873300.

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Kommera, Pranay, Vinay Ramakrishnaiah, and Christine Sweeney. Accelerate M-TIP on GPUs and deploy to Summit and NERSC-9 (against simulated data) WBS 2.2.4.05 ExaFEL, Milestone ADSE13-199. Office of Scientific and Technical Information (OSTI), November 2021. http://dx.doi.org/10.2172/1830563.

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You might also be interested in the extended bibliographies on the topic 'GPU Accelerated' for particular source types:
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