政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/152439

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/152439

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | 全文笔数/总笔数 : 112881/143847 (78%)
造访人次 : 50301335 在线人数 : 615

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

搜寻范围

查询小技巧：

您可在西文检索词汇前后加上"双引号"，以获取较精准的检索结果

若欲以作者姓名搜寻，建议至进阶搜寻限定作者字段，可获得较完整数据

进阶搜寻

主页 ‧ 登入 ‧ 上传 ‧ 说明 ‧ 关于政大典藏 ‧ 管理

到手机版

政大機構典藏 > 商學院 > 企業管理研究所（MBA學位學程） > 學位論文 > Item 140.119/152439

请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/152439

题名:	NVIDIA CUDA關鍵成功因素之探討 An Exploration of the Key Success Factors of NVIDIA CUDA
作者:	廖毓文 Liao, Yu-Wen
贡献者:	黃國峯林谷合 Huang, Kuo-Feng Lin, Ku-Ho 廖毓文 Liao, Yu-Wen
关键词:	NVIDIA 開放創新創新生態系五力分析 NVIDIA Open Innovation Innovation Ecosystem Five Forces Analysis
日期:	2024
上传时间:	2024-08-05 12:12:30 (UTC+8)
摘要:	自21世紀初學術機構開始使用GPU為科學計算和大數據處理加速，至2023年OpenAI發布GhatGPT的商業化應用，以GPU做通用運算的需求不斷增長，其中AI晶片市場需求持續強勁成長，2022 年市場規模 159 億美元，預計到 2030 年將達到 2,074 億美元，年均複合成長率為 37.9%。而NVIDIA作為GPU硬體效能的領導者，在數據中心AI領域市佔率高達九成，也早在2006年即透過建立CUDA為GPU用於通用運算進行佈局。NVIDIA透過CUDA專注為應用開發者提供完整的開發環境與技術效能支援，從而累積大量開發者與隨之而來的成功應用案例與合作機會；隨著圍繞CUDA的生態系茁壯，亦能協助NVIDIA從中維持創新的動力以及發掘潛在市場。本研究以次級資料收集法進行個案研究，以了解GPU用於加速運算的市場需求趨勢變化以及NVIDIA CUDA創立背景與功能介紹為開頭，再透過Chesbrough (2007); Chesbrough and Garman (2009) 開放式創新以及Jacobides (2019)創新生態系的架構進一步分析CUDA生態系的設計，最後依據Porter (1979)的五力分析了解CUDA的競爭環境，總結出NVIDIA CUDA的成功的關鍵以及未來的挑戰與建議。 Since the early 21st century, academic institutions have been utilizing GPUs for accelerating scientific computations and big data processing. By 2023, with the commercialization of OpenAI's GhatGPT, the demand for GPUs for general-purpose computing has continuously grown. The AI chip market has also seen a robust growth, with the market size reaching $15.9 billion in 2022 and projected to reach $207.4 billion by 2030, at a compound annual growth rate of 37.9%. NVIDIA, as a leader in GPU hardware performance, holds up to ninety percent market share in the AI data center domain. It has been strategically positioning itself in the general-purpose computing with GPUs since the establishment of CUDA in 2006. NVIDIA, through CUDA, focuses on providing a comprehensive development environment and technical performance support for application developers, thereby accumulating a vast number of developers, successful application cases, and collaborative opportunities. As the ecosystem around CUDA thrives, it also aids NVIDIA in maintaining its momentum for innovation and exploring potential markets. This study employs a case study approach using secondary data collection to understand the trends in market demand for GPU-accelerated computing and to introduce the background and functionality of NVIDIA's CUDA. Further analysis of the CUDA ecosystem is performed using Chesbrough's (2007) and Chesbrough and Garman's (2009) frameworks on open innovation, and Jacobides's (2019) framework on innovation ecosystems. Finally, Porter's (1979) Five Forces Analysis is used to understand the competitive environment of CUDA, concluding with the key factors for NVIDIA CUDA's success and future challenges and recommendations.
參考文獻:	中文文獻碩博士學位論文 1.張志偉（2022）。製造業的價值創造：台積電開放創新平台之個案研究。﹝碩士論文。國立臺灣大學﹞臺灣博碩士論文知識加值系統。 https://hdl.handle.net/11296/tanzj6。英文文獻期刊 1.Chesbrough, H. W. (2007). Why companies should have open business models. MIT Sloan management review. 2.Chesbrough, H. W., & Garman, A. R. (2009). How open innovation can help you cope in lean times. Harvard Business Review, 87(12), 68-76, 128. 3.Ghorpade, J., Parande, J., Kulkarni, M., & Bawaskar, A. (2012). GPGPU processing in CUDA architecture. arXiv preprint arXiv:1202.4347. 4.Jacobides, M. G. (2019). In the ecosystem economy, what’s your strategy? Harvard Business Review, 97(5), 128-137. 5.Porter, M. E. (1979). How competitive forces shape strategy [Article]. Harvard Business Review, 57(2), 137-145. 網際網路 6.CORPORATION, N. (2007). Form 10-K 2007. U.S. Securities and Exchange Commission. Retrieved April 28, 2024, from: https://www.sec.gov/Archives/edgar/data/1045810/000104581007000008/fy2007annualreportonform10-k.htm 7.CORPORATION, N. (2024). Form 10-k 2024. NVIDIA CORPORATION. Retrieved July 13, 2024, from: https://s201.q4cdn.com/141608511/files/doc_financials/2024/q4/1cbe8fe7-e08a-46e3-8dcc-b429fc06c1a4.pdf 8.Economist, T. (2024). Why do Nvidia’s chips dominate the AI market? Retrieved April 28, 2024, from: https://www.economist.com/the-economist-explains/2024/02/27/why-do-nvidias-chips-dominate-the-ai-market 9.Gray, A. (2015). NVIDIA and IBM Cloud Support ImageNet Large Scale Visual Recognition Challenge. Retrieved April 28, 2024, from: https://developer.nvidia.com/blog/nvidia-ibm-cloud-support-imagenet-large-scale-visual-recognition-challenge/ 10.Hu, K. (2023). ChatGPT sets record for fastest-growing user base - analyst note. Retrieved April 28, 2024, from: https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/ 11.Huang, J. (2019). NVIDIA CEO Jensen Huang - AI Keynote Session at MSOE. Retrieved April 28, 2024, from: https://www.youtube.com/watch?v=mkGy1by5vxw 12.Huang, J. (2024). Keynote by NVIDIA CEO Jensen Huang at 2024 SIEPR Economic Summit. Retrieved April 28, 2024, from: https://www.youtube.com/watch?v=cEg8cOx7UZk 13.Intel. (2018). New Intel Architectures and Technologies Target Expanded Market Opportunities Retrieved April 28, 2024, from: https://www.intc.com/news-events/press-releases/detail/106/new-intel-architectures-and-technologies-target-expanded 14.Josh Baer, S. N. (2019). The Winding Road to Better Machine Learning Infrastructure Through Tensorflow Extended and Kubeflow. Retrieved July 8, 2024, from: https://engineering.atspotify.com/2019/12/the-winding-road-to-better-machine-learning-infrastructure-through-tensorflow-extended-and-kubeflow/ 15.King, I. (2024). Nvidia rises most in about nine months as AI drives sales. Retrieved April 28, 2024, from: https://www.bnnbloomberg.ca/nvidia-to-top-meta-record-with-nearly-250-billion-value-jump-1.2037901#:~:text=Companies%20such%20as%20Amazon.com,in%20hardware%20for%20AI%20computing. 16.MarketDigits. (2024). The Artificial Intelligence Chip Market. Retrieved April 28, 2024, from: https://www.globenewswire.com/news-release/2024/01/25/2817130/0/en/Artificial-Intelligence-Chip-Market-projected-to-reach-USD-207-4-Billion-by-2030-growing-at-a-CAGR-of-37-9-during-the-forecast-period-of-2023-2030-pronounced-by-MarketDigits-in-its.html 17.Merritt, R. (2021). What Is Accelerated Computing? Retrieved April 28, 2024, from: https://blogs.nvidia.com/blog/what-is-accelerated-computing/ 18.Nemire, B. (2015). Inside the Programming Evolution of GPU Computing. Retrieved April 28, 2024, from: https://developer.nvidia.com/blog/inside-the-programming-evolution-of-gpu-computing/ 19.NVIDIA. (2011a). CUDA Education & Training. Retrieved April 29, 2024, from: https://developer.nvidia.com/cuda-education-training 20.NVIDIA. (2011b). Tools & Ecosystem. Retrieved April 29, 2024, from: https://developer.nvidia.com/tools-ecosystem 21.NVIDIA. (2014). CUDA Zone. Retrieved April 28, 2024, from: https://developer.nvidia.com/cuda-zone 22.NVIDIA. (2015). CUDA Profiling Tools Interface. NVIDIA Developer. Retrieved April 28, 2024, from: https://developer.nvidia.com/cuda-profiling-tools-interface 23.NVIDIA. (2017). NVIDIA DRIVE Partner Ecosystem. Retrieved April 28, 2024, from: https://www.nvidia.com/en-us/self-driving-cars/partners/ 24.NVIDIA. (2019). A Timeline of Innovation. Retrieved April 28, 2024 from: https://www.nvidia.com/en-us/about-nvidia/corporate-timeline/ 25.NVIDIA. (2020). Molecular Dynamics. Retrieved April 29, 2024, from: https://developer.nvidia.com/blog/tag/molecular-dynamics/ 26.NVIDIA. (2021). NVIDIA Clara. Retrieved April 29, 2024, from: https://www.nvidia.com/en-us/clara/ 27.NVIDIA. (2022). NVIDIA ISAAC. Retrieved April 29, 2024, from: https://developer.nvidia.com/isaac 28.NVIDIA. (2023). Earth-2. Retrieved April 29, 2024, from: https://www.nvidia.com/en-us/high-performance-computing/earth-2/ 29.Peddie, D. J. (2020). Famous Graphics Chips: Intel’s GPU History. Retrieved April 29, 2024, from: https://www.computer.org/publications/tech-news/chasing-pixels/intels-gpu-history 30.Pradeep, G. (2015). CUDA Refresher: The GPU Computing Ecosystem. NVIDIA Developer. Retrieved April 29, 2024, from: https://developer.nvidia.com/blog/cuda-refresher-the-gpu-computing-ecosystem/ 31.Thompson, D. (2023). AI in the datacenter industry — hype or growth catalyst. Retrieved April 29, 2024, from: https://www.spglobal.com/marketintelligence/en/news-insights/research/ai-in-the-datacenter-industry-hype-or-growth-catalyst
描述:	碩士國立政治大學企業管理研究所(MBA學位學程) 111363055
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0111363055
数据类型:	thesis
显示于类别:	[企業管理研究所（MBA學位學程）] 學位論文

文件中的档案:

档案	描述	大小	格式	浏览次数
305501.pdf		1192Kb	Adobe PDF	0	检视/开启

在政大典藏中所有的数据项都受到原著作权保护.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - 回馈