This article, written by Google tech experts Jeff Dean and Sanjay Ghemawat, shares their years of accumulated practical experience in performance optimization at Google. The article systematically introduces performance estimation methods, analysis tool usage, API design optimization, algorithm improvements, memory representation optimization, reducing memory allocation, and various other techniques. Through numerous examples, it demonstrates how to apply these principles to actual code. Whether you’re developing high-performance systems, optimizing AI models, or designing chip architectures, these best practices from inside Google can help you write more efficient code. The article particularly emphasizes the necessity of performance optimization in the ‘critical 3%’ scenarios and provides abundant code examples and performance data. It’s an essential technical guide for any developer pursuing code performance.
Original Link:Hacker News
最新评论
I don't think the title of your article matches the content lol. Just kidding, mainly because I had some doubts after reading the article.
这个AI状态研究很深入,数据量也很大,很有参考价值。
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文章内容很有深度,AI模型的发展趋势值得关注。
内容丰富,对未来趋势分析得挺到位的。
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光纤技术真厉害,文章解析得挺透彻的。
文章内容很实用,想了解更多相关技巧。