1.0想方涉法 - 量瓶外的天空 M-Y-Oceanehttps://myoceane.fr檸檬爸https://myoceane.fr/index.php/author/johnny5584767gmail-com/[GPU] 加速主成分分析 (PCA) - 想方涉法 - 量瓶外的天空 M-Y-Oceanerich600338<blockquote class="wp-embedded-content" data-secret="uqOme1xsgX"><a href="https://myoceane.fr/index.php/gpu-accelerate-pca/">[GPU] 加速主成分分析 (PCA)</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://myoceane.fr/index.php/gpu-accelerate-pca/embed/#?secret=uqOme1xsgX" width="600" height="338" title="“[GPU] 加速主成分分析 (PCA)” — 想方涉法 - 量瓶外的天空 M-Y-Oceane" data-secret="uqOme1xsgX" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script type="text/javascript">
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https://myoceane.fr/wp-content/uploads/2025/07/Running-time-in-seconds.png1154746檸檬爸幾年前有分享過一篇主成分分析 (Principle Component Analysis) 原理的文章，由於 PCA 其實就是矩陣的運算，所以非常適合使用分散式運算來做加速，不論是 Spark 或是 GPU 的架構都很適合，Spark MLlib 本身就可以加速 PCA 等機器學習的運算，使用 cuML + GPU 根據 Nvidia Blog 的數據，加速的效果更加明顯，本篇想要紀錄如何導入 GPU 到 PCA 等等傳統的機器學習運算？