Relevant bibliographies by topics / NVIDIA

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  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: 25 July 2025

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Journal articles on the topic "NVIDIA"

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Xu, Kaifeng. "NVIDIAs Research and Development Investment: Impact on Financial Performance and Market Valuation." Advances in Economics, Management and Political Sciences 148, no. 1 (2025): 109–17. https://doi.org/10.54254/2754-1169/2024.ld19178.

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This paper provides a detailed financial analysis of NVIDIA Corporation (NVIDIA), a leading technology firm renowned for its advancements in graphics processing units (GPUs), artificial intelligence (AI), data center solutions, autonomous driving, and professional visualization technologies. The analysis delves into NVIDIA's revenue recognition, research and development (R&D) investments, inventory management strategies, and overarching strategic objectives. Utilizing key financial data from fiscal 2024 and the second quarter of fiscal 2025, this study evaluates NVIDIAs recent performance
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Huang, Siyu. "NVIDIA's Financial Strategy and Operation Strategy Analysis." BCP Business & Management 45 (April 27, 2023): 73–81. http://dx.doi.org/10.54691/bcpbm.v45i.4865.

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NVIDIA is a global leader in visual computing that focuses on building products that enhance the human-computer interaction experience of personal and professional computing platforms. This paper mainly analyzes NVIDIA's financial strategy and operational strategy. What NVIDIA does will be especially important amid the coronavirus pandemic and the rapidly changing semiconductor industry. This paper applies SWOT, financial report analysis and other methods to specifically analyze NVIDIA's FCF, net profit and other data, and tries to explore NVIDIA's current financial strategy, operating strateg
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Liu, Yuzhe. "A Financial Analysis and Valuation of NVIDIA." Advances in Economics, Management and Political Sciences 175, no. 1 (2025): 28–34. https://doi.org/10.54254/2754-1169/2025.21946.

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This paper analyzes the financial performance and strategic positioning of NVIDIA, a leading player in the semiconductor industry. NVIDIA's market value has surged in recent years, largely driven by advancements in artificial intelligence (AI) and the increasing demand for high-performance computing. The companys data center division has experienced substantial revenue growth year-on-year, fueled by the widespread adoption of AI-driven applications, cloud computing, and machine learning technologies. However, NVIDIA faces several challenges, including intense competition from industry rivals s
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Liu, Junjing. "A Financial Analysis and Valuation of NVIDIA." Advances in Economics, Management and Political Sciences 148, no. 1 (2025): 137–42. https://doi.org/10.54254/2754-1169/2024.ld19183.

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This paper analyzes Nvidias financial data and its strategic shift towards the AI and data center markets, which the company has recently entered. Nvidia, initially renowned for its GPUs, has now expanded its expertise into AI, computing, and self-driving cars. The calculation of Nvidias financial ratios for the years 2021-2023 reveals strong liquidity, solvency, and profitability indicators, despite external threats such as export restrictions on American microcircuits in China and increased competition. The company has successfully minimized its reliance on debt and is well-positioned to pro
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Gong, Chuanzhi. "Analysis of NVIDIA's Strategic Business Philosophy." Highlights in Business, Economics and Management 39 (August 8, 2024): 928–33. http://dx.doi.org/10.54097/7aamtj45.

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While many companies have been hit on different scales in recent years by the COVID-19 epidemic and global geopolitical conflicts, an analysis of NVIDIA's financial statements over the past few years reveals that NVIDIA is demonstrating its profitability at a phenomenal rate of growth. To explore the source of this profitability of NVIDIA, this paper analyses and suggests the growth of the company's profitability in these four years by analyzing NVIDIA's competitors and by using SWOT analysis on NVIDIA itself. The conclusion finds that NVIDIA has fully grasped the dividends brought by the AI e
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Zhang, Rui, and Lei Hu. "Research on NVIDIA's Development Strategy." International Journal of Global Economics and Management 5, no. 2 (2024): 79–84. https://doi.org/10.62051/ijgem.v5n2.10.

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NVIDIA, as the world's leading supplier of graphics processing units (GPUs) and artificial intelligence (AI) computing hardware, has achieved rapid development and expansion in recent years. This paper studies the evolution of NVIDIA's development strategy and the factors for its success by analyzing NVIDIA's development history, industry environment and competitive landscape. At the same time, this paper also explores the challenges faced by NVIDIA and the direction of its future development.
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Chen, Shujie. "Research on Nvidia Investment Strategies and Analysis." Highlights in Business, Economics and Management 24 (January 22, 2024): 2234–40. http://dx.doi.org/10.54097/vzd0m812.

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Under the background of macroeconomic factors such as the global economic downturn and imperfect trade policies, sound investment decisions and risk management are all important. This research paper takes Nvidia as an investment sample for investors to conduct a comprehensive analysis and complete overview. Based on an analysis of Nvidia’s annual reports and market value from 2021 to 2023, the study found that Nvidia has shown amazing income growth and profitability, solidifying what is happening as a precursor in the advancement and semiconductor industry. This shows significant growth potent
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Li, Meihui. "Research on NVIDIA's Success and Socioeconomic Perspective." Advances in Economics, Management and Political Sciences 67, no. 1 (2024): 44–49. http://dx.doi.org/10.54254/2754-1169/67/20241260.

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As the world's leading semiconductor company, NVIDIA continues to expand its marketing power through technological innovation, while its products also provide directions and tools for global social and economic development. This article explores the reasons for NVIDIA's success as a leader in the semiconductor industry and its impact on the socio-economic system from a socio-economic perspective. This article analyzes NVIDIA's characteristics in technological innovation, market orientation, sustainable development, social responsibility, etc. from a socio-economic perspective, and demonstrates
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Le, Xi. "The Application of DCF in Company Valuation: Case of NVIDIA." Highlights in Business, Economics and Management 39 (August 8, 2024): 244–51. http://dx.doi.org/10.54097/a50yxz91.

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Company valuation has always been a crucial theme in financial analysis and corporate management. It plays an important role in both corporate strategic adjustment and investment selection. NVIDIA is an American leading technology company dedicated to expanding into various areas including graphics processing units (GPUs), artificial intelligence (AI), autonomous vehicles, data centers, and other products. The stock of NVIDIA has been rising for several years and attract much attention from investors. Therefore, this paper combines DCF model with Fundamental analysis to value NVIDIA. Results o
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Wang, Jinchuan. "Research on the Competitive Development and Prospects of Nvidia." Advances in Economics, Management and Political Sciences 161, no. 1 (2025): 51–56. https://doi.org/10.54254/2754-1169/2025.19882.

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With the rapid development of technologies such as artificial intelligence (AI), deep learning and high performance computing (HPC), Nvidia has gradually become the technology company with the upper hand in the world in the last decade with its technical advantages in the graphics processing unit (GPU) field. The purpose of this article is to analyze the future development prospects of Nvidia and explore its competition and challenges in the AI and GPU markets. Through the review and analysis of existing literature, combined with NVIDIA's financial data, SWOT analysis and industry environment
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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<br>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 au
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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 atr
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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 algo
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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
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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.<br>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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<p>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 li
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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 itera
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Books on the topic "NVIDIA"

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

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Zielgruppenanalyse - Nvidia GeForce-Grafikkarten: Nvidia GeForce Grafikkarten im Performance- und High-End-Segment. GRIN Verlag GmbH, 2011.

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Ruiz, José Marcos Arroyo. Fundamentos básicos de NVIDIA Jetso Nano. Marcombo, 2022.

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

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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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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.: Labor Productivity Benchmarks and International Gap Analysis (Labor Productivity Series). 2nd ed. Icon Group International, 2000.

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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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Book chapters on the topic "NVIDIA"

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Kalé, Laxmikant V., Abhinav Bhatele, Eric J. Bohm, et al. "NVIDIA GPU." In Encyclopedia of Parallel Computing. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-09766-4_276.

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Deguy, Sébastien. "NVIDIA and Pachinko." In Allegorithmic. CRC Press, 2025. https://doi.org/10.1201/9781003534624-16.

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Halawa, Hassan, Hazem A. Abdelhafez, Andrew Boktor, and Matei Ripeanu. "NVIDIA Jetson Platform Characterization." In Lecture Notes in Computer Science. 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. 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. 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. 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. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37959-8_2.

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Bernstein, Daniel J., Hsieh-Chung Chen, Chen-Mou Cheng, et al. "ECC2K-130 on NVIDIA GPUs." In Progress in Cryptology - INDOCRYPT 2010. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17401-8_23.

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Bayer, Robert, Jon Voigt Tøttrup, and Pınar Tözün. "TPCx-AI on NVIDIA Jetsons." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-29576-8_4.

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

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Conference papers on the topic "NVIDIA"

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Zhang, Lingqi, Ryan Barton, Peng Chen, et al. "Investigating Nvidia GPU Architecture Trends via Microbenchmarks." In 2024 IEEE International Conference on Cluster Computing Workshops (CLUSTER Workshops). IEEE, 2024. http://dx.doi.org/10.1109/clusterworkshops61563.2024.00045.

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Altooq, Kawthar Abdulla Ali, Layla Jawad Hasan, Sumathi Kumaraswamy, Shrikant Panigrahi, and Sasikanta Tripathy. "Forecasting Nvidia Stock Trends: An Analytical Approach." In 2024 International Conference on Sustainable Islamic Business and Finance (SIBF). IEEE, 2024. https://doi.org/10.1109/sibf63788.2024.10883878.

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Zhao, Zhengji, Brian Austin, Ermal Rrapaj, and Nicholas J. Wright. "Understanding VASP Power Profiles on NVIDIA A100 GPUs." In SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE, 2024. https://doi.org/10.1109/scw63240.2024.00189.

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Tirumala, Ajay, and Raymond Wong. "NVIDIA Blackwell Platform: Advancing Generative AI and Accelerated Computing." In 2024 IEEE Hot Chips 36 Symposium (HCS). IEEE, 2024. http://dx.doi.org/10.1109/hcs61935.2024.10665247.

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Ulbricht, Fabian, Gian Marti, Reinhard Wiesmayr, and Christoph Studer. "PyJama: Differentiable Jamming and Anti-Jamming with NVIDIA Sionna." In 2024 IEEE 25th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE, 2024. http://dx.doi.org/10.1109/spawc60668.2024.10694375.

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Singh, Nongmeikapam Thoiba, Harkamal Kaur, Jyoti Dhiman, Ayush Aryan, Jyoti Rani, and Manoj Wadhwa. "AI-Driven Document Analysis: Employing Streamlit, Faiss, Nvidia Nemo." In 2025 3rd International Conference on Inventive Computing and Informatics (ICICI). IEEE, 2025. https://doi.org/10.1109/icici65870.2025.11069902.

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Wang, Wan, David A. Miller, and Adam Wax. "High performance low-cost OCT system powered by NVIDIA Jetson Orin Nano." In Clinical and Translational Biophotonics. Optica Publishing Group, 2025. https://doi.org/10.1364/translational.2025.tw1f.4.

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Green, Simon. "NVIDIA FlameWorks." In ACM SIGGRAPH 2014 Computer Animation Festival. 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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Reports on the topic "NVIDIA"

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Lippuner, Jonas. NVIDIA CUDA. Office of Scientific and Technical Information (OSTI), 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), 2020. http://dx.doi.org/10.2172/1813665.

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Marrogi, Eryney, and Niko McCarty. Evo 2 Can Design Entire Genomes. Asimov Press, 2025. https://doi.org/10.62211/45yp-23jh.

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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), 2012. http://dx.doi.org/10.2172/1173292.

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Motorny, Sergey, S. Glandon, and Jing-Ru Cheng. The design of multimedia object detection pipelines within the HPC environment. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49599.

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Computer vision multimedia pipelines have become both more sophisticated and robust over the years. The pipelines can accept multiple inputs, perform frame analysis, and produce outputs on a variety of platforms with near-real-time performance. Vendors such as Nvidia have significantly grown their framework and library offerings while providing tutorials and documentation via online training and tutorials. Despite the prolific growth, many of the libraries, frameworks, and tutorials come with noticeable limitations. The limitations are especially apparent within the high-performance computing
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Hunter, R., S. Ross, and Jing-Ru Cheng. A general-purpose multiplatform GPU-accelerated ray tracing API. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47260.

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Real-time ray tracing is an important tool in computational research. Among other things, it is used to model sensors for autonomous vehicle simulation, efficiently simulate radiative energy propagation, and create effective data visualizations. However, raytracing libraries currently offered for GPU platforms have a high level of complexity to facilitate the detailed configuration needed by gaming engines and high-fidelity renderers. A researcher wishing to take advantage of the performance gains offered by the GPU for simple ray casting routines would need to learn how to use these ray traci
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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), 2021. http://dx.doi.org/10.2172/1761619.

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