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

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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.
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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

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