<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>性能优化 on Yiwen Cai</title><link>https://yiwen-cai.github.io/tags/%E6%80%A7%E8%83%BD%E4%BC%98%E5%8C%96/</link><description>Recent content in 性能优化 on Yiwen Cai</description><generator>Hugo -- gohugo.io</generator><language>zh-cn</language><managingEditor>caiyiwen.cs@foxmail.com (Yiwen Cai)</managingEditor><webMaster>caiyiwen.cs@foxmail.com (Yiwen Cai)</webMaster><copyright>© 2026 Yiwen Cai</copyright><lastBuildDate>Fri, 10 Jul 2026 21:12:36 +0800</lastBuildDate><atom:link href="https://yiwen-cai.github.io/tags/%E6%80%A7%E8%83%BD%E4%BC%98%E5%8C%96/index.xml" rel="self" type="application/rss+xml"/><item><title>Kernels、Triton 与 Profiling：CS336 Lecture 6 笔记</title><link>https://yiwen-cai.github.io/notes/triton/cs336-kernels-triton/</link><pubDate>Sun, 14 Jun 2026 00:00:00 +0000</pubDate><author>caiyiwen.cs@foxmail.com (Yiwen Cai)</author><guid>https://yiwen-cai.github.io/notes/triton/cs336-kernels-triton/</guid><description>从硬件抽象进入 kernel 编程实践：warp/occupancy/bank conflict/coalescing 如何映射到性能，benchmarking 与 profiling 方法论，以及用 Triton 实现 GeLU、softmax、row sum、matmul+ReLU 四个 kernel。</description></item><item><title>GPU 架构与 FlashAttention：CS336 Lecture 5 笔记</title><link>https://yiwen-cai.github.io/notes/cuda/cs336-gpu-attention/</link><pubDate>Sat, 13 Jun 2026 00:00:00 +0000</pubDate><author>caiyiwen.cs@foxmail.com (Yiwen Cai)</author><guid>https://yiwen-cai.github.io/notes/cuda/cs336-gpu-attention/</guid><description>从模型结构转入系统视角：为什么 LLM 必须依赖 GPU、GPU 的执行模型与内存层次、arithmetic intensity 与 roofline model，以及如何用这套性能模型解释 FlashAttention 这类 IO-aware 算法。</description></item></channel></rss>