<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Kernel 优化 on PlumePHP</title><link>https://plumephp.com/tags/kernel-%E4%BC%98%E5%8C%96/</link><description>Recent content in Kernel 优化 on PlumePHP</description><generator>Hugo</generator><language>zh-CN</language><lastBuildDate>Sun, 27 Sep 2026 10:00:00 +0800</lastBuildDate><atom:link href="https://plumephp.com/tags/kernel-%E4%BC%98%E5%8C%96/index.xml" rel="self" type="application/rss+xml"/><item><title>算子融合与 Kernel 优化：从 CUDA Graph 到 FlashAttention 的极致性能</title><link>https://plumephp.com/ai-kernel-fusion-optimization/</link><pubDate>Sun, 27 Sep 2026 10:00:00 +0800</pubDate><guid>https://plumephp.com/ai-kernel-fusion-optimization/</guid><description>&lt;blockquote&gt;
&lt;p&gt;在 GPU 推理中，最贵的往往不是计算，而是数据搬运：每一个算子都要把中间结果写回显存、再读出来，内存带宽成为瓶颈。算子融合就是把多个算子合并成一个 kernel，减少显存往返，是 TensorRT、vLLM、PyTorch 编译器都在做的事。本文从融合原理讲起，拆解 FlashAttention 类融合，再到 CUDA Graph 与自定义 kernel 开发流程。&lt;/p&gt;</description></item></channel></rss>