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題名 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貢獻者 黃國峯<br>林谷合
Huang, Kuo-Feng<br>Lin, Ku-Ho
廖毓文
Liao, Yu-Wen關鍵詞 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 上傳時間 5-八月-2024 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 資料類型 thesis dc.contributor.advisor 黃國峯<br>林谷合 zh_TW dc.contributor.advisor Huang, Kuo-Feng<br>Lin, Ku-Ho en_US dc.contributor.author (作者) 廖毓文 zh_TW dc.contributor.author (作者) Liao, Yu-Wen en_US dc.creator (作者) 廖毓文 zh_TW dc.creator (作者) Liao, Yu-Wen en_US dc.date (日期) 2024 en_US dc.date.accessioned 5-八月-2024 12:12:30 (UTC+8) - dc.date.available 5-八月-2024 12:12:30 (UTC+8) - dc.date.issued (上傳時間) 5-八月-2024 12:12:30 (UTC+8) - dc.identifier (其他 識別碼) G0111363055 en_US dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/152439 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 企業管理研究所(MBA學位學程) zh_TW dc.description (描述) 111363055 zh_TW dc.description.abstract (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。 zh_TW dc.description.abstract (摘要) 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. en_US dc.description.tableofcontents 第一章 緒論 8 第一節 研究背景與動機 8 第二節 研究目的與研究問題 9 第三節 研究方法 10 第四節 研究架構 11 第二章 文獻探討 12 第一節 開放創新 12 第二節 創新生態系 16 第三節 五力分析 20 第三章 個案探討 22 第一節 個案產業與趨勢介紹 22 第二節 個案介紹 24 第四章 研究分析與討論 32 第一節 從開放創新觀點分析 NVIDIA CUDA 32 第二節 從創新生態系觀點分析 NVIDIA CUDA 34 第三節 NVIDIA CUDA 競爭環境分析 38 第五章 結論與未來建議 46 參考文獻 53 中文文獻 53 英文文獻 53 zh_TW dc.format.extent 1221270 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0111363055 en_US dc.subject (關鍵詞) NVIDIA zh_TW dc.subject (關鍵詞) 開放創新 zh_TW dc.subject (關鍵詞) 創新生態系 zh_TW dc.subject (關鍵詞) 五力分析 zh_TW dc.subject (關鍵詞) NVIDIA en_US dc.subject (關鍵詞) Open Innovation en_US dc.subject (關鍵詞) Innovation Ecosystem en_US dc.subject (關鍵詞) Five Forces Analysis en_US dc.title (題名) NVIDIA CUDA關鍵成功因素之探討 zh_TW dc.title (題名) An Exploration of the Key Success Factors of NVIDIA CUDA en_US dc.type (資料類型) thesis en_US dc.relation.reference (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 zh_TW
