Gotowe bibliografie tematyczne / NVIDIA GPGUs

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Gotowa bibliografia na temat „NVIDIA GPGUs”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "NVIDIA GPGUs"

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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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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Bi, Yujiang, Shun Xu, and Yunheng Ma. "Running Qiskit on ROCm Platform." EPJ Web of Conferences 295 (2024): 11022. http://dx.doi.org/10.1051/epjconf/202429511022.

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Qiskit is one of the common quantum computing frameworks and and the qiskit-aer package can accelerating quantum circuit simulation using NVIDIA GPU with the help of THRUST. AMD ROCm framework similar to CUDA, a heterogeneous computing framework supporting both the NVIDIA and AMD GPUs provides the possibility to porting Qiskit/Qiskit-Aer from CUDA platform to its own. We present the porting progress of Qiskit/QiskitAer and preliminary performance test on both NVIDA and AMD GPUs. Our results show that Qiskit/Qiskit-Aer cand work well on AMD GPUs with the help of ROCm/HIP, and has comparable per

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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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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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Bähr, Pascal R., Bruno Lang, Peer Ueberholz, Marton Ady, and Roberto Kersevan. "Development of a hardware-accelerated simulation kernel for ultra-high vacuum with Nvidia RTX GPUs." International Journal of High Performance Computing Applications 36, no. 2 (2021): 141–52. http://dx.doi.org/10.1177/10943420211056654.

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Molflow+ is a Monte Carlo (MC) simulation software for ultra-high vacuum, mainly used to simulate pressure in particle accelerators. In this article, we present and discuss the design choices arising in a new implementation of its ray-tracing–based simulation unit for Nvidia RTX Graphics Processing Units (GPUs). The GPU simulation kernel was designed with Nvidia’s OptiX 7 API to make use of modern hardware-accelerated ray-tracing units, found in recent RTX series GPUs based on the Turing and Ampere architectures. Even with the challenges posed by switching to 32 bit computations, our kernel ru

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Chen, Dong, Hua You Su, Wen Mei, Li Xuan Wang, and Chun Yuan Zhang. "Scalable Parallel Motion Estimation on Muti-GPU System." Applied Mechanics and Materials 347-350 (August 2013): 3708–14. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3708.

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With NVIDIA’s parallel computing architecture CUDA, using GPU to speed up compute-intensive applications has become a research focus in recent years. In this paper, we proposed a scalable method for multi-GPU system to accelerate motion estimation algorithm, which is the most time consuming process in video encoding. Based on the analysis of data dependency and multi-GPU architecture, a parallel computing model and a communication model are designed. We tested our parallel algorithm and analyzed the performance with 10 standard video sequences in different resolutions using 4 NVIDIA GTX460 GPU

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Liu, Hui, Bo Yang, and Zhangxin Chen. "Accelerating algebraic multigrid solvers on NVIDIA GPUs." Computers & Mathematics with Applications 70, no. 5 (2015): 1162–81. http://dx.doi.org/10.1016/j.camwa.2015.07.005.

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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 (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

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Rozprawy doktorskie na temat "NVIDIA GPGUs"

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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Subramoniapillai, Ajeetha Saktheesh. "Architectural Analysis and Performance Characterization of NVIDIA GPUs using Microbenchmarking." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1344623484.

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Loundagin, Justin. "Optimizing Harris Corner Detection on GPGPUs Using CUDA." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1348.

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ABSTRACT Optimizing Harris Corner Detection on GPGPUs Using CUDA The objective of this thesis is to optimize the Harris corner detection algorithm implementation on NVIDIA GPGPUs using the CUDA software platform and measure the performance benefit. The Harris corner detection algorithm—developed by C. Harris and M. Stephens—discovers well defined corner points within an image. The corner detection implementation has been proven to be computationally intensive, thus realtime performance is difficult with a sequential software implementation. This thesis decomposes the Harris corner detection al

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Araújo, João Manuel da Silva. "Paralelização de algoritmos de Filtragem baseados em XPATH/XML com recurso a GPUs." Master's thesis, FCT - UNL, 2009. http://hdl.handle.net/10362/2530.

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Dissertação de Mestrado em Engenharia Informática Esta dissertação envolve o estudo da viabilidade da utilização dos GPUs para o processamento paralelo aplicado aos algoritmos de filtragem de notificações num sistema editor/assinante. Este objectivo passou por realizar uma comparação de resultados experimentais entre a versão sequencial (nos CPUs) e a versão paralela de um algoritmo de filtragem escolhido como referência. Essa análise procurou dar elementos para aferir se eventuais ganhos da exploração dos GPUs serão suficientes para compensar a maior complexidade do processo.

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Surineni, Sruthikesh. "Performance/Accuracy Trade-offs of Floating-point Arithmetic on Nvidia GPUs| From a Characterization to an Auto-tuner." Thesis, University of Missouri - Columbia, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=13850754.

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Floating-point computations produce approximate results, possibly leading to inaccuracy and reproducibility problems. Existing work addresses two issues: first, the design of high precision floating-point representations, and second, the study of methods to support a trade-off between accuracy and performance of central processing unit (CPU) applications. However, a comprehensive study of trade-offs between accuracy and performance on modern graphic processing units (GPUs) is missing. This thesis covers the use of different floating-point precisions (i.e., single and double floating-point

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Yu, Pen-Yung, and 游本永. "Increasing Thread-Level Parallelism with Register Resource Management for NVIDIA GPUs." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/78523353456172674080.

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碩士 國立交通大學 資訊科學與工程研究所 102 Graphics processing units (GPUs) are equipped with enormous amounts of arithmetic processors running in a single-instruction, multiple-data fashion, producing a throughput of Tera floating-point operations per second, which is ten or even hundred times higher than the throughput of central processing units. GPUs reply on massive hardware multithreading to hide off-chip memory latencies, which are approximately 400–800 cycles. However, the number of parallel threads running on GPUs is highly restricted by the resource requirement of such a thread, especially

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Chi, Ping-Lin, and 机炳霖. "Simulation of Optical Properties for Thin Film Using CUDA on NVIDIA GPUs." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/23775928600592540874.

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碩士 國立高雄第一科技大學 光電工程研究所 99 Firstly, the thesis will discuss the difference of parallel computing between the ways of data permutation in multi-threads and single thread, then measure whether the performance of GPU can increase the efficiency and the confidence in the accuracy. Compared with Intel i7 series CPU, the efficiency of NVIDIA G100 series GPU increases more than 40 times, and the effect for relative difference is less than that of 10E-15. That is to say, GPU can be a replacement of CPU to conduct huge calculation. Compared with the simulation programming of development platf

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Chen, Wei-Sheng, and 陳威勝. "Hybrid Simulation of Optical Properties for Thin Film Using CUDA on NVIDIA GPUs." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/99410635297475769146.

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碩士 國立高雄第一科技大學 光電工程研究所 101 This study is optical simulation and multifunction program design by CUDA, it contains: 1.Optimization in the thickness of solar selective absorbing film, 2.Reflectivity of optimal thickness fitting, 3.The reflectivity simulation of double-sided coating, 4.The optimal film thickness fitting on superlattice, 5.The reflectivity of multilayer films and the calculation of the absorption rate. Then measure whether the performance of GPU can increase the efficiency and the confidence in the accuracy. Compared with Intel i7 series CPU, the efficiency of NVIDIA G

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Wankhede, Rahul. "Accelerating Estimation of Perfusion Maps in Contrast X-ray Computed Tomography using Many-core CPUs and GPUs." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/5826.

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X-ray Computed Tomography (CT) perfusion imaging is a non-invasive medical imaging modality that has been established as a fast and economical method for diagnosing cerebrovascular diseases such as acute ischemia, sub-arachnoid hemorrhage, and vasospasm. Current CT perfusion imaging being dynamic in nature, requires three-dimensional data acquisition at multiple time points, resulting in a long time for processing ranging from six to twelve minutes post acquisition. In emergency medical conditions such as stroke, every second is crucial for obtaining the perfusion maps, which are used for depl

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Książki na temat "NVIDIA GPGUs"

Rucci, Enzo. Evaluación de rendimiento y eficiencia energética de sistemas heterogéneos para bioinformática. Editorial de la Universidad Nacional de La Plata (EDULP), 2018. http://dx.doi.org/10.35537/10915/66533.

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El problema del consumo energético se presenta como uno de los mayores obstáculos para el diseño de sistemas que sean capaces de alcanzar la escala de los Exaflops. Por lo tanto, la comunidad científica está en la búsqueda de diferentes maneras de mejorar la eficiencia energética de los sistemas HPC. Una tendencia reciente para incrementar el poder computacional y al mismo tiempo limitar el consumo de potencia de estos sistemas consiste en incorporarles aceleradores y coprocesadores, como pueden ser las GPUs de NVIDIA y AMD o los coprocesadores Xeon Phi de Intel. Por otra parte, las FPGAs apar

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Części książek na temat "NVIDIA GPGUs"

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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Wang, Mingliang, Hector Klie, Manish Parashar, and Hari Sudan. "Solving Sparse Linear Systems on NVIDIA Tesla GPUs." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01970-8_87.

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Monakov, Alexander, and Arutyun Avetisyan. "Implementing Blocked Sparse Matrix-Vector Multiplication on NVIDIA GPUs." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03138-0_32.

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Kobayashi, Ryohei, Norihisa Fujita, Yoshiki Yamaguchi, et al. "Accelerating Radiative Transfer Simulation on NVIDIA GPUs with OpenACC." In Parallel and Distributed Computing, Applications and Technologies. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-29927-8_27.

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Davis, Joshua Hoke, Christopher Daley, Swaroop Pophale, Thomas Huber, Sunita Chandrasekaran, and Nicholas J. Wright. "Performance Assessment of OpenMP Compilers Targeting NVIDIA V100 GPUs." In Accelerator Programming Using Directives. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74224-9_2.

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Zhou, Honghui, Ruyi Qin, Zihan Liu, Ying Qian, and Xiaoming Ju. "Optimizing Performance of Image Processing Algorithms on GPUs." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_95.

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AbstractThe application of machine learning algorithms in the field of power grid improves the service level of power enterprises and promotes the development of power grid. NVIDIA Volta and Turing GPUs powered by Tensor Cores can accelerate training and learning performance for these algorithms. With Tensor Cores enabled, FP32 and FP16 mixed precision matrix multiplication dramatically accelerates the throughput and reduces AI training times. In order to explore the cause of this phenomenon, we choose a convolutional neural network (CNN), which is widely used in computer vision, as an example

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Afanasyev, Ilya V., and Dmitry I. Lichmanov. "Developing Efficient Implementation of Label Propagation Algorithm for Modern NVIDIA GPUs." In Communications in Computer and Information Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64616-5_16.

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Valero-Lara, Pedro, Ivan Martínez-Pérez, Raül Sirvent, Xavier Martorell, and Antonio J. Peña. "NVIDIA GPUs Scalability to Solve Multiple (Batch) Tridiagonal Systems Implementation of cuThomasBatch." In Parallel Processing and Applied Mathematics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78024-5_22.

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Tsai, Yuhsiang M., Terry Cojean, and Hartwig Anzt. "Sparse Linear Algebra on AMD and NVIDIA GPUs – The Race Is On." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50743-5_16.

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Reiz, Severin, Tobias Neckel, and Hans-Joachim Bungartz. "Neural Nets with a Newton Conjugate Gradient Method on Multiple GPUs." In Parallel Processing and Applied Mathematics. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-30442-2_11.

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AbstractTraining deep neural networks consumes increasing computational resource shares in many compute centers. Often, a brute force approach to obtain hyperparameter values is employed. Our goal is (1) to enhance this by enabling second-order optimization methods with fewer hyperparameters for large-scale neural networks and (2) to compare optimizers for specific tasks to suggest users the best one for their problem. We introduce a novel second-order optimization method that requires the effect of the Hessian on a vector only and avoids the huge cost of explicitly setting up the Hessian for

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Streszczenia konferencji na temat "NVIDIA GPGUs"

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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Li, Xinyi, Ang Li, Bo Fang, Katarzyna Swirydowicz, Ignacio Laguna, and Ganesh Gopalakrishnan. "Discovery of Floating-Point Differences Between NVIDIA and AMD GPUs." In 2024 IEEE 24th International Symposium on Cluster, Cloud and Internet Computing (CCGrid). IEEE, 2024. http://dx.doi.org/10.1109/ccgrid59990.2024.00083.

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Zahid, Anwar Hossain, Ignacio Laguna, and Wei Le. "Testing GPU Numerics: Finding Numerical Differences Between NVIDIA and AMD GPUs." In SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE, 2024. https://doi.org/10.1109/scw63240.2024.00077.

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Bois, Karl. "“The Copper Behind Blackwell”: Understanding Today's Copper Scale-up Networks." In Optical Fiber Communication Conference. Optica Publishing Group, 2025. https://doi.org/10.1364/ofc.2025.w3a.1.

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This presentation will delve into leading-edge copper scale-up architectures. The historical trend of bandwidth in NVIDIA GPUs and all-to-all GPU domain will be discussed. A scale-up copper architecture utilizing state-of-the-art signaling at 200 Gbit/s per differential pair will be presented, specifically the GB200 NVL 72 rack-scale design. The discussion will cover its constitutive components, including GB200 compute nodes, NVLink switch trays, and copper cable backplane cartridges. Full-text article not available; see video presentation

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Wilfong, Benjamin, Anand Radhakrishnan, Henry A. Le Berre, Steve Abbott, Reuben D. Budiardja, and Spencer H. Bryngelson. "OpenACC offloading of the MFC compressible multiphase flow solver on AMD and NVIDIA GPUs." In SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE, 2024. https://doi.org/10.1109/scw63240.2024.00242.

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Yang, Zeyu, Karel Adamek, and Wesley Armour. "Accurate and Convenient Energy Measurements for GPUs: A Detailed Study of NVIDIA GPU’s Built-In Power Sensor." In SC24: International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE, 2024. https://doi.org/10.1109/sc41406.2024.00028.

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Cornelius, Jason, Zachary Miles, Anthony Comer, et al. "Long-Range Mars Rotorcraft Design Optimization using Machine Learning." In Vertical Flight Society 81st Annual Forum and Technology Display. The Vertical Flight Society, 2025. https://doi.org/10.4050/f-0081-2025-364.

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Simulation data consisting of multiple fidelity levels were generated using Graphical Processing Unit (GPU) resources on the NASA supercomputers. First, two large aerodynamic simulation databases were generated for geometric perturbations over a range of flight conditions for a hex-rotor bi-plane tailsitter aircraft. Results were visualized using the NASA Advanced Supercomputing Division's Hyperwall to improve the geometric design constraints. More than 3,000 full aircraft aerodynamic simulations were run using GPU enabled OVERFLOW with an actuator disk model to generate the airframe aerodynam

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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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Arafa, Yehia, Ammar ElWazir, Abdelrahman Elkanishy, et al. "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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Bakita, Joshua, and James H. Anderson. "Hardware Compute Partitioning on NVIDIA GPUs*." In 2023 IEEE 29th Real-Time and Embedded Technology and Applications Symposium (RTAS). IEEE, 2023. http://dx.doi.org/10.1109/rtas58335.2023.00012.

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Raporty organizacyjne na temat "NVIDIA GPGUs"

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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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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