Relevant bibliographies by topics / NVIDIA

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'NVIDIA'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 March 2023

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Journal articles on the topic "NVIDIA"

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Санжаров, В. В., В. А. Фролов, and В. А. Галактионов. "ИССЛЕДОВАНИЕ ТЕХНОЛОГИИ Nvidia RTX." Программирование, no. 4 (2020): 65–72. http://dx.doi.org/10.31857/s0132347420030061.

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Nangla, Siddhante. "GPU Programming using NVIDIA CUDA." International Journal for Research in Applied Science and Engineering Technology 6, no. 6 (June 30, 2018): 79–84. http://dx.doi.org/10.22214/ijraset.2018.6016.

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Sanzharov, V. V., V. A. Frolov, and V. A. Galaktionov. "Survey of Nvidia RTX Technology." Programming and Computer Software 46, no. 4 (July 2020): 297–304. http://dx.doi.org/10.1134/s0361768820030068.

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Lin, Chun-Yuan, Jin Ye, Che-Lun Hung, Chung-Hung Wang, Min Su, and Jianjun Tan. "Constructing a Bioinformatics Platform with Web and Mobile Services Based on NVIDIA Jetson TK1." International Journal of Grid and High Performance Computing 7, no. 4 (October 2015): 57–73. http://dx.doi.org/10.4018/ijghpc.2015100105.

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Current high-end graphics processing units (abbreviate to GPUs), such as NVIDIA Tesla, Fermi, Kepler series cards which contain up to thousand cores per-chip, are widely used in the high performance computing fields. These GPU cards (called desktop GPUs) should be installed in personal computers/servers with desktop CPUs; moreover, the cost and power consumption of constructing a high performance computing platform with these desktop CPUs and GPUs are high. NVIDIA releases Tegra K1, called Jetson TK1, which contains 4 ARM Cortex-A15 CPUs and 192 CUDA cores (Kepler GPU) and is an embedded board with low cost, low power consumption and high applicability advantages for embedded applications. NVIDIA Jetson TK1 becomes a new research direction. Hence, in this paper, a bioinformatics platform was constructed based on NVIDIA Jetson TK1. ClustalWtk and MCCtk tools for sequence alignment and compound comparison were designed on this platform, respectively. Moreover, the web and mobile services for these two tools with user friendly interfaces also were provided. The experimental results showed that the cost-performance ratio by NVIDIA Jetson TK1 is higher than that by Intel XEON E5-2650 CPU and NVIDIA Tesla K20m GPU card.
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Fasi, Massimiliano, Nicholas J. Higham, Mantas Mikaitis, and Srikara Pranesh. "Numerical behavior of NVIDIA tensor cores." PeerJ Computer Science 7 (February 10, 2021): e330. http://dx.doi.org/10.7717/peerj-cs.330.

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We explore the floating-point arithmetic implemented in the NVIDIA tensor cores, which are hardware accelerators for mixed-precision matrix multiplication available on the Volta, Turing, and Ampere microarchitectures. Using Volta V100, Turing T4, and Ampere A100 graphics cards, we determine what precision is used for the intermediate results, whether subnormal numbers are supported, what rounding mode is used, in which order the operations underlying the matrix multiplication are performed, and whether partial sums are normalized. These aspects are not documented by NVIDIA, and we gain insight by running carefully designed numerical experiments on these hardware units. Knowing the answers to these questions is important if one wishes to: (1) accurately simulate NVIDIA tensor cores on conventional hardware; (2) understand the differences between results produced by code that utilizes tensor cores and code that uses only IEEE 754-compliant arithmetic operations; and (3) build custom hardware whose behavior matches that of NVIDIA tensor cores. As part of this work we provide a test suite that can be easily adapted to test newer versions of the NVIDIA tensor cores as well as similar accelerators from other vendors, as they become available. Moreover, we identify a non-monotonicity issue affecting floating point multi-operand adders if the intermediate results are not normalized after each step.
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Peng, Tao, Dingnan Zhang, Don Lahiru Nirmal Hettiarachchi, and John Loomis. "An Evaluation of Embedded GPU Systems for Visual SLAM Algorithms." Electronic Imaging 2020, no. 6 (January 26, 2020): 325–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.6.iriacv-074.

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Simultaneous Localization and Mapping (SLAM) solves the computational problem of estimating the location of a robot and the map of the environment. SLAM is widely used in the area of navigation, odometry, and mobile robot mapping. However, the performance and efficiency of the small industrial mobile robots and unmanned aerial vehicles (UAVs) are highly constrained to the battery capacity. Therefore, a mobile robot, especially a UAV, requires low power consumption while maintaining high performance. This paper demonstrates holistic and quantitative performance evaluations of embedded computing devices that run on the Nvidia Jetson platform. Evaluations are based on the execution of two state-of-the-art Visual SLAM algorithms, ORB-SLAM2 and OpenVSLAM, on Nvidia Jetson Nano, Nvidia Jetson TX2, and Nvidia Jetson Xavier.
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Chilingaryan, Suren, Andrei Shkarin, Roman Shkarin, Matthias Vogelgesang, and Sergey Tsapko. "Benchmark for FFT Libraries." Applied Mechanics and Materials 756 (April 2015): 673–77. http://dx.doi.org/10.4028/www.scientific.net/amm.756.673.

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There are various vendors of FFT libraries, but there is no software available for it automatic benchmarking on all available devices. In this article an application that allows easy measure the performance and precision of various FFT libraries on the available GPUs and CPUs is presented. This application has been used to find out the fastest FFT library for NVIDIA GTX TESLA and NVIDIA GTX TITAN. The obtained results shown that the best implementation is provided by cuFFT library developed by NVIDIA.
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Zhu, Li, and Yi Min Yang. "Real-Time Multitasking Video Encoding Processing System of Multicore." Applied Mechanics and Materials 66-68 (July 2011): 2074–79. http://dx.doi.org/10.4028/www.scientific.net/amm.66-68.2074.

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This paper achieved the optimize which is based on the Series processors Produced by NVIDIA, such as Geforce, Tegra, Nexus and so on, and discussed the future development of the video image processor. Expounded the most popular DSP optimization techniques and objectives in the current, to optimized the design for the methods of the various papers available in existence. Based on the NVIDIA's series of products, specific discussed CUDA GPU architecture based on NVIDIA's products, raised the hardware and algorithms of the current most popular video encoding equipment, based on real practical technology to improve the transmission and encoding of multimedia data.
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Blyth, Simon. "Meeting the challenge of JUNO simulation with Opticks: GPU optical photon acceleration via NVIDIA® OptiXTM." EPJ Web of Conferences 245 (2020): 11003. http://dx.doi.org/10.1051/epjconf/202024511003.

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Opticks is an open source project that accelerates optical photon simulation by integrating NVIDIA GPU ray tracing, accessed via NVIDIA OptiX, with Geant4 toolkit based simulations. A single NVIDIA Turing architecture GPU has been measured to provide optical photon simulation speedup factors exceeding 1500 times single threaded Geant4 with a full JUNO analytic GPU geometry automatically translated from the Geant4 geometry. Optical physics processes of scattering, absorption, scintillator reemission and boundary processes are implemented within CUDA OptiX programs based on the Geant4 implementations. Wavelength-dependent material and surface properties as well as inverse cumulative distribution functions for reemission are interleaved into GPU textures providing fast interpolated property lookup or wavelength generation. Major recent developments enable Opticks to benefit from ray trace dedicated RT cores available in NVIDIA RTX series GPUs. Results of extensive validation tests are presented.
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McCarthy, Dylan, and J¨ Urgen P. Schulze. "Distributed VR Rendering Using NVIDIA OptiX." Electronic Imaging 2017, no. 3 (January 29, 2017): 36–41. http://dx.doi.org/10.2352/issn.2470-1173.2017.3.ervr-095.

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Dissertations / Theses on the topic "NVIDIA"

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Gameiro, Pedro Miguel Rodrigues. "Equity research - NVIDIA Corporation." Master's thesis, Instituto Superior de Economia e Gestão, 2018. http://hdl.handle.net/10400.5/16970.

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Mestrado em Finanças
Este relatório reflete a avaliação da empresa de Semi-condutores, a NVIDIA Corporation e está de acordo com o trabalho final de mestrado de Finanças do ISEG. Este relatório foi escrito com base nas recomendações do CFA Institute. A NVIDIA é uma empresa que está a enfrentar um momento bastante singular comparado com os seus competidores, com um crescimento anual de vendas de 40% e um aumento na avaliação das suas ações de 334,46% nos últimos dos anos. Não só a NVIDIA está a ter uma performance financeira interessante como se está a entrar em mercados emergentes como a autonomização automóvel e a criptomoeda, o que faz com que seja um caso de estudo bastante interessante. Também a fascinação em relação a tecnologia e em especifico, ao gaming, foram uma das razões pela qual esta empresa foi escolhida. Este relatório foi desenvolvido com base em informação pública disponível até 30 de Junho de 2018 e nenhuma informação posterior a esta data não foi considerada. O preço de ação de $303,67, foi obtido através do modelo de Fluxos de Caixa Descontados. O método de avaliação relativa foi tentado, porém dado à situação única da NVIDIA, não existe competidores que consideremos como peer's comparáveis em termos de múltiplos. Esta avaliação sugere uma recomendação de COMPRA, apesar do seu risco médio, dado que a NVDIA está consolidada no seu mercado principal, o gaming, porém existe alguma incerteza relativamente aos mercados da criptomoeda e autonomização automóvel.
This project reflects an evaluation of NVIDIA Corporation, Semiconductor Company, according to ISEG´s Master in Finance final work project. This report was written in agreement with the recommendations of the CFA Institute. NVIDIA is a company that is facing a very singular moment comparing to its peers, with a 40% annual revenue growth and a valuation increase of 334,46% in the last two years. Not only NVIDIA is having an interesting financial performance but also is entering in emerging markets, such as, autonomous cars and cryptocurrencies, being a very interesting case study. Also the fascination about technology and gaming in specific was one of the reasons this company was chosen. This report was developed considering public information available until June 30th 2018 and any information or event subsequent to this date has not been considered. The price target of $303,67 was obtained from the Discounted Cash Flow method. The relative valuation method was attempted, but due to the unique situation of NVIDIA, there are not close peers following the criteria's used. This valuation suggests to a BUY recommendation, although with medium risk, since NVIDIA is consolidated in their main market, gaming, but there is some uncertainty relatively to markets like cryptocurrency and autonomous cars.
info:eu-repo/semantics/publishedVersion
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Zajíc, Jiří. "Překladač jazyka C# do jazyka Nvidia CUDA." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236439.

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This master's thesis is focused on GPU accelerated calculations on NVidia graphics card. CUDA technology is used and converted to implementation on a .NET platform. The problem is solved as a compiler from C# programing language to NVidia CUDA language with expression atrributes of C# language that preserves the same semantics of actions. Application is implemented in C# programing language and uses NRefactory, the open-source library.
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Santos, Paulo Carlos Ferreira dos. "Extração de informações de desempenho em GPUs NVIDIA." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-02042013-090806/.

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O recente crescimento da utilização de Unidades de Processamento Gráfico (GPUs) em aplicações científicas, que são voltadas ao desempenho, gerou a necessidade de otimizar os programas que nelas rodam. Uma ferramenta adequada para essa tarefa é o modelo de desempenho que, por sua vez, se beneficia da existência de uma ferramenta de extração de informações de desempenho para GPUs. Este trabalho cobre a criação de um gerador de microbenchmark para instruções PTX que também obtém informações sobre as características do hardware da GPU. Os resultados obtidos com o microbenchmark foram validados através de um modelo simplificado que obteve erros entre 6,11% e 16,32% em cinco kernels de teste. Também foram levantados os fatores de imprecisão nos resultados do microbenchmark. Utilizamos a ferramenta para analisar o perfil de desempenho das instruções e identificar grupos de comportamentos semelhantes. Também testamos a dependência do desempenho do pipeline da GPU em função da sequência de instruções executada e verificamos a otimização do compilador para esse caso. Ao fim deste trabalho concluímos que a utilização de microbenchmarks com instruções PTX é factível e se mostrou eficaz para a construção de modelos e análise detalhada do comportamento das instruções.
The recent growth in the use of tailored for performance Graphics Processing Units (GPUs) in scientific applications, generated the need to optimize GPU targeted programs. Performance models are the suitable tools for this task and they benefits from existing GPUs performance information extraction tools. This work covers the creation of a microbenchmark generator using PTX instructions and it also retrieves information about the GPU hardware characteristics. The microbenchmark results were validated using a simplified model with errors rates between 6.11% and 16.32% under five diferent GPU kernels. We also explain the imprecision factors present in the microbenchmark results. This tool was used to analyze the instructions performance profile, identifying groups with similar behavior. We also evaluated the corelation of the GPU pipeline performance and instructions execution sequence. Compiler optimization capabilities for this case were also verified. We concluded that the use of microbenchmarks with PTX instructions is a feasible approach and an efective way to build performance models and to generate detailed analysis of the instructions\' behavior.
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Krivoklatský, Filip. "Návrh vestavaného systému inteligentného vidění na platformě NVIDIA." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-400627.

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This diploma thesis deals with design of embedded computer vision system and transfer of existing computer vision application for 3D object detection from Windows OS to designed embedded system with Linux OS. Thesis focuses on design of communication interface for system control and camera video transfer through local network with video compression. Then, detection algorithm is enhanced by transferring computationally expensive functions to GPU using CUDA technology. Finally, a user application with graphical interface is designed for system control on Windows platform.
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Savioli, Nicolo'. "Parallelization of the algorithm WHAM with NVIDIA CUDA." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amslaurea.unibo.it/6377/.

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The aim of my thesis is to parallelize the Weighting Histogram Analysis Method (WHAM), which is a popular algorithm used to calculate the Free Energy of a molucular system in Molecular Dynamics simulations. WHAM works in post processing in cooperation with another algorithm called Umbrella Sampling. Umbrella Sampling has the purpose to add a biasing in the potential energy of the system in order to force the system to sample a specific region in the configurational space. Several N independent simulations are performed in order to sample all the region of interest. Subsequently, the WHAM algorithm is used to estimate the original system energy starting from the N atomic trajectories. The parallelization of WHAM has been performed through CUDA, a language that allows to work in GPUs of NVIDIA graphic cards, which have a parallel achitecture. The parallel implementation may sensibly speed up the WHAM execution compared to previous serial CPU imlementations. However, the WHAM CPU code presents some temporal criticalities to very high numbers of interactions. The algorithm has been written in C++ and executed in UNIX systems provided with NVIDIA graphic cards. The results were satisfying obtaining an increase of performances when the model was executed on graphics cards with compute capability greater. Nonetheless, the GPUs used to test the algorithm is quite old and not designated for scientific calculations. It is likely that a further performance increase will be obtained if the algorithm would be executed in clusters of GPU at high level of computational efficiency. The thesis is organized in the following way: I will first describe the mathematical formulation of Umbrella Sampling and WHAM algorithm with their apllications in the study of ionic channels and in Molecular Docking (Chapter 1); then, I will present the CUDA architectures used to implement the model (Chapter 2); and finally, the results obtained on model systems will be presented (Chapter 3).
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Ikeda, Patricia Akemi. "Um estudo do uso eficiente de programas em placas gráficas." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-25042012-212956/.

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Inicialmente projetadas para processamento de gráficos, as placas gráficas (GPUs) evoluíram para um coprocessador paralelo de propósito geral de alto desempenho. Devido ao enorme potencial que oferecem para as diversas áreas de pesquisa e comerciais, a fabricante NVIDIA destaca-se pelo pioneirismo ao lançar a arquitetura CUDA (compatível com várias de suas placas), um ambiente capaz de tirar proveito do poder computacional aliado à maior facilidade de programação. Na tentativa de aproveitar toda a capacidade da GPU, algumas práticas devem ser seguidas. Uma delas consiste em manter o hardware o mais ocupado possível. Este trabalho propõe uma ferramenta prática e extensível que auxilie o programador a escolher a melhor configuração para que este objetivo seja alcançado.
Initially designed for graphical processing, the graphic cards (GPUs) evolved to a high performance general purpose parallel coprocessor. Due to huge potencial that graphic cards offer to several research and commercial areas, NVIDIA was the pioneer lauching of CUDA architecture (compatible with their several cards), an environment that take advantage of computacional power combined with an easier programming. In an attempt to make use of all capacity of GPU, some practices must be followed. One of them is to maximizes hardware utilization. This work proposes a practical and extensible tool that helps the programmer to choose the best configuration and achieve this goal.
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Rivera-Polanco, Diego Alejandro. "COLLECTIVE COMMUNICATION AND BARRIER SYNCHRONIZATION ON NVIDIA CUDA GPU." Lexington, Ky. : [University of Kentucky Libraries], 2009. http://hdl.handle.net/10225/1158.

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Thesis (M.S.)--University of Kentucky, 2009.
Title from document title page (viewed on May 18, 2010). Document formatted into pages; contains: ix, 88 p. : ill. Includes abstract and vita. Includes bibliographical references (p. 86-87).
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Harvey, Jesse Patrick. "GPU acceleration of object classification algorithms using NVIDIA CUDA /." Online version of thesis, 2009. http://hdl.handle.net/1850/10894.

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Lerchundi, Osa Gorka. "Fast Implementation of Two Hash Algorithms on nVidia CUDA GPU." Thesis, Norwegian University of Science and Technology, Department of Telematics, 2009. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9817.

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User needs increases as time passes. We started with computers like the size of a room where the perforated plaques did the same function as the current machine code object does and at present we are at a point where the number of processors within our graphic device unit it’s not enough for our requirements. A change in the evolution of computing is looming. We are in a transition where the sequential computation is losing ground on the benefit of the distributed. And not because of the birth of the new GPUs easily accessible this trend is novel but long before it was used for projects like SETI@Home, fightAIDS@Home, ClimatePrediction and there were shouting from the rooftops about what was to come. Grid computing was its formal name. Until now it was linked only to distributed systems over the network, but as this technology evolves it will take different meaning. nVidia with CUDA has been one of the first companies to make this kind of software package noteworthy. Instead of being a proof of concept it’s a real tool. Where the transition is expressed in greater magnitude in which the true artist is the programmer who uses it and achieves performance increases. As with many innovations, a community distributed worldwide has grown behind this software package and each one doing its bit. It is noteworthy that after CUDA release a lot of software developments grown like the cracking of the hitherto insurmountable WPA. With Sony-Toshiba-IBM (STI) alliance it could be said the same thing, it has a great community and great software (IBM is the company in charge of maintenance). Unlike nVidia is not as accessible as it is but IBM is powerful enough to enter home made supercomputing market. In this case, after IBM released the PS3 SDK, a notorious application was created using the benefits of parallel computing named Folding@Home. Its purpose is to, inter alia, find the cure for cancer. To sum up, this is only the beginning, and in this thesis is sized up the possibility of using this technology for accelerating cryptographic hash algorithms. BLUE MIDNIGHT WISH (The hash algorithm that is applied to the surgery) is undergone to an environment change adapting it to a parallel capable code for creating empirical measures that compare to the current sequential implementations. It will answer questions that nowadays haven’t been answered yet. BLUE MIDNIGHT WISH is a candidate hash function for the next NIST standard SHA-3, designed by professor Danilo Gligoroski from NTNU and Vlastimil Klima – an independent cryptographer from Czech Republic. So far, from speed point of view BLUE MIDNIGHT WISH is on the top of the charts (generally on the second place – right behind EDON-R - another hash function from professor Danilo Gligoroski). One part of the work on this thesis was to investigate is it possible to achieve faster speeds in processing of Blue Midnight Wish when the computations are distributed among the cores in a CUDA device card. My numerous experiments give a clear answer: NO. Although the answer is negative, it still has a significant scientific value. The point is that my work acknowledges viewpoints and standings of a part of the cryptographic community that is doubtful that the cryptographic primitives will benefit when executed in parallel in many cores in one CPU. Indeed, my experiments show that the communication costs between cores in CUDA outweigh by big margin the computational costs done inside one core (processor) unit.

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Virk, Bikram. "Implementing method of moments on a GPGPU using Nvidia CUDA." Thesis, Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/33980.

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This thesis concentrates on the algorithmic aspects of Method of Moments (MoM) and Locally Corrected Nyström (LCN) numerical methods in electromagnetics. The data dependency in each step of the algorithm is analyzed to implement a parallel version that can harness the powerful processing power of a General Purpose Graphics Processing Unit (GPGPU). The GPGPU programming model provided by NVIDIA's Compute Unified Device Architecture (CUDA) is described to learn the software tools at hand enabling us to implement C code on the GPGPU. Various optimizations such as the partial update at every iteration, inter-block synchronization and using shared memory enable us to achieve an overall speedup of approximately 10. The study also brings out the strengths and weaknesses in implementing different methods such as Crout's LU decomposition and triangular matrix inversion on a GPGPU architecture. The results suggest future directions of study in different algorithms and their effectiveness on a parallel processor environment. The performance data collected show how different features of the GPGPU architecture can be enhanced to yield higher speedup.
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Books on the topic "NVIDIA"

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Kurniawan, Agus. IoT Projects with NVIDIA Jetson Nano. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6452-2.

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Dagg, Michael. NVIDIA GPU Programming: Massively Parallel Programming with CUDA. Wiley & Sons, Incorporated, John, 2013.

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Dagg, Michael. NVIDIA GPU Programming: Massively Parallel Programming with CUDA. Wiley & Sons, Incorporated, John, 2012.

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Meier, Jan. GPU Powered VDI: Virtual Desktops with NVIDIA GRID. Independently Published, 2018.

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Dagg, Michael. NVIDIA GPU Programming: Massively Parallel Programming with CUDA. Wiley & Sons, Incorporated, John, 2012.

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Jons, Kingston. Nvidia Shield TV Pro User Guide: The Ultimate User Guide to Master the New Nvidia Shield TV Pro in 2 Hours. Independently Published, 2020.

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Ltd, ICON Group, and ICON Group International Inc. NVIDIA CORP.: International Competitive Benchmarks and Financial Gap Analysis (Financial Performance Series). 2nd ed. Icon Group International, 2000.

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Ltd, ICON Group, and ICON Group International Inc. NVIDIA CORP.: Labor Productivity Benchmarks and International Gap Analysis (Labor Productivity Series). 2nd ed. Icon Group International, 2000.

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IoT Projects with NVIDIA Jetson Nano: AI-Enabled Internet of Things Projects for Beginners. Apress L. P., 2020.

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ELIV-MarketPlace 2018. VDI Verlag, 2018. http://dx.doi.org/10.51202/9783181023389.

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Alle Entwicklungen rund ums Auto und das vieldiskutierte vollautomatisierte Fahren sind sehr sehr spannend. Hier können Sie die neuesten Entwicklungen nachlesen. Inhalt (Auszüge) HAF und VAF: Anforderungen, Realisierungen und Ausblicke Artifical Intelligence in the Driver’s Seat / AI – enabling autonomous driving and transportation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1 P. de Boer, NVIDIA, USA AF und VAF: Realisierungen und Ausblicke Artificial Intelligence: Today ́s and tomorrow ́s opportunities, how do we adjust to it?. . . . . . . 3 C. Mitrohin, Continental Automotive GmbH, Babenhausen Prozess, Validierung, Absicherung, Verfügbarkeiten Database of relevant scenarios as a tool for safety assurance of automated driving . . . . . . . . 5 A. Zlocki, L. Eckstein, IKA, RWTH Aachen, Aachen Training and Validation of Neural Networks in Virtual Environments . . . . . . . . . . . . . . . . . . 17 D. Dörr, P. U...
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Book chapters on the topic "NVIDIA"

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Kalé, Laxmikant V., Abhinav Bhatele, Eric J. Bohm, James C. Phillips, David H. Bailey, Ananth Y. Grama, Joseph Fogarty, et al. "NVIDIA GPU." In Encyclopedia of Parallel Computing, 1339–45. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-09766-4_276.

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Halawa, Hassan, Hazem A. Abdelhafez, Andrew Boktor, and Matei Ripeanu. "NVIDIA Jetson Platform Characterization." In Lecture Notes in Computer Science, 92–105. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64203-1_7.

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Kurniawan, Agus. "Administering NVIDIA Jetson Nano." In IoT Projects with NVIDIA Jetson Nano, 21–47. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6452-2_3.

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Kurniawan, Agus. "NVIDIA Jetson Nano Programming." In IoT Projects with NVIDIA Jetson Nano, 49–62. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6452-2_4.

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Kurniawan, Agus. "NVIDIA Jetson Nano Camera." In IoT Projects with NVIDIA Jetson Nano, 85–105. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6452-2_6.

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Maitre, Ogier. "Understanding NVIDIA GPGPU Hardware." In Natural Computing Series, 15–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37959-8_2.

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Kurniawan, Agus. "Introduction to NVIDIA Jetson Nano." In IoT Projects with NVIDIA Jetson Nano, 1–6. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6452-2_1.

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Bernstein, Daniel J., Hsieh-Chung Chen, Chen-Mou Cheng, Tanja Lange, Ruben Niederhagen, Peter Schwabe, and Bo-Yin Yang. "ECC2K-130 on NVIDIA GPUs." In Progress in Cryptology - INDOCRYPT 2010, 328–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17401-8_23.

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Kurniawan, Agus. "NVIDIA Jetson Nano I/O Programming." In IoT Projects with NVIDIA Jetson Nano, 63–83. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6452-2_5.

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Kurniawan, Agus. "Setting Up and Running." In IoT Projects with NVIDIA Jetson Nano, 7–19. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6452-2_2.

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Conference papers on the topic "NVIDIA"

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Green, Simon. "NVIDIA FlameWorks." In ACM SIGGRAPH 2014 Computer Animation Festival. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2633956.2658828.

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Toksvig, Michael, Parthasarathy Sriram, John Matheson, Brian Cabral, and Brian Smith. "NVIDIA Tegra." In 2008 IEEE Hot Chips 20 Symposium (HCS). IEEE, 2008. http://dx.doi.org/10.1109/hotchips.2008.7476540.

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Evans, Jonathon. "Nvidia Grace." In 2022 IEEE Hot Chips 34 Symposium (HCS). IEEE, 2022. http://dx.doi.org/10.1109/hcs55958.2022.9895599.

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Pursai, Sridhar. "NVIDIA® Ion." In 2009 IEEE Hot Chips 21 Symposium (HCS). IEEE, 2009. http://dx.doi.org/10.1109/hotchips.2009.7478361.

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Lamb, Chris. "OpenCL for NVIDIA GPUs." In 2009 IEEE Hot Chips 21 Symposium (HCS). IEEE, 2009. http://dx.doi.org/10.1109/hotchips.2009.7478346.

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Bernauer, Julie. "NVIDIA Deep Learning Tutorial." In 2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 2017. http://dx.doi.org/10.1109/ipdps.2017.7.

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Naphade, Milind, David C. Anastasiu, Anuj Sharma, Vamsi Jagrlamudi, Hyeran Jeon, Kaikai Liu, Ming-Ching Chang, Siwei Lyu, and Zeyu Gao. "The NVIDIA AI City Challenge." In 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). IEEE, 2017. http://dx.doi.org/10.1109/uic-atc.2017.8397673.

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Svedin, Martin, Steven W. D. Chien, Gibson Chikafa, Niclas Jansson, and Artur Podobas. "Benchmarking the Nvidia GPU Lineage." In HEART '21: International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3468044.3468053.

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Arafa, Yehia, Ammar ElWazir, Abdelrahman Elkanishy, Youssef Aly, Ayatelrahman Elsayed, Abdel-Hameed Badawy, Gopinath Chennupati, Stephan Eidenbenz, and Nandakishore Santhi. "NVIDIA GPGPUs Instructions Energy Consumption." In 2020 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS). IEEE, 2020. http://dx.doi.org/10.1109/ispass48437.2020.00022.

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Tynefield, John. "NVIDIA GTX200: TeraFLOPS visual computing." In 2008 IEEE Hot Chips 20 Symposium (HCS). IEEE, 2008. http://dx.doi.org/10.1109/hotchips.2008.7476559.

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Reports on the topic "NVIDIA"

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Lippuner, Jonas. NVIDIA CUDA. Office of Scientific and Technical Information (OSTI), July 2019. http://dx.doi.org/10.2172/1532687.

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Elwazir, Ammar, Abdel-Hameed Badawy, Omar Aaziz, and Jeanine Cook. LDMS-GPU: Lightweight Distributed Metric Service (LDMS) for NVIDIA GPGPUs. Office of Scientific and Technical Information (OSTI), November 2020. http://dx.doi.org/10.2172/1813665.

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Kurzak, Jakub, Pitor Luszczek, Stanimire Tomov, and Jack Dongarra. Preliminary Results of Autotuning GEMM Kernels for the NVIDIA Kepler Architecture- GeForce GTX 680. Office of Scientific and Technical Information (OSTI), April 2012. http://dx.doi.org/10.2172/1173292.

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Leinhauser, Matthew, Jeffrey Young, Sergei Bastrakov, Rene Widera, Ronnie Chatterjee, and Sunita Chandrasekaran. Performance Analysis of PIConGPU: Particle-in-Cell on GPUs using NVIDIA’s NSight Systems and NSight Compute. Office of Scientific and Technical Information (OSTI), January 2021. http://dx.doi.org/10.2172/1761619.

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