<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>MFU on 安橙的博客</title><link>https://blog.ans20xx.com/tags/mfu/</link><description>Recent content in MFU on 安橙的博客</description><generator>Hugo -- 0.163.3</generator><language>zh</language><lastBuildDate>Sat, 20 Jun 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://blog.ans20xx.com/tags/mfu/index.xml" rel="self" type="application/rss+xml"/><item><title>Day 27 · 训练性能分析</title><link>https://blog.ans20xx.com/posts/ai/day27/</link><pubDate>Sat, 20 Jun 2026 00:00:00 +0000</pubDate><guid>https://blog.ans20xx.com/posts/ai/day27/</guid><description>学习训练性能分析的最小闭环:计算 MFU / HFU,用 Nsight Systems 抓一段训练 step,通过 NVTX、CUDA kernel、NCCL timeline 识别 compute、communication 与 pipeline bubble。</description></item><item><title>Day 28 · 周复盘 + 小项目</title><link>https://blog.ans20xx.com/posts/ai/day28/</link><pubDate>Sat, 20 Jun 2026 00:00:00 +0000</pubDate><guid>https://blog.ans20xx.com/posts/ai/day28/</guid><description>阶段 2 收官:复盘分布式训练 Infra 的 NCCL、DDP、ZeRO、TP、PP、SP/CP、DeepSpeed、checkpoint、data pipeline、算子加速与 profiling;在 2 卡或云上 8 卡训练一个约 125M GPT,记录 MFU,并完成 ZeRO-3 vs TP+PP 的硬件取舍笔记。</description></item></channel></rss>