<?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>Occupancy on Sanketh's Blog</title><link>https://sankethbk.github.io/blog/tags/occupancy/</link><description>Recent content in Occupancy on Sanketh's Blog</description><generator>Hugo -- 0.166.0</generator><language>en-us</language><lastBuildDate>Tue, 01 Sep 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://sankethbk.github.io/blog/tags/occupancy/index.xml" rel="self" type="application/rss+xml"/><item><title>CUDA Occupancy: Filling the SM to Hide Latency</title><link>https://sankethbk.github.io/blog/posts/gpu/2026-09-01-2-cuda-occupancy/</link><pubDate>Tue, 01 Sep 2026 00:00:00 +0000</pubDate><guid>https://sankethbk.github.io/blog/posts/gpu/2026-09-01-2-cuda-occupancy/</guid><description>&lt;h1 id="cuda-occupancy-filling-the-sm-to-hide-latency"&gt;CUDA Occupancy: Filling the SM to Hide Latency&lt;/h1&gt;
&lt;p&gt;The &lt;a href="https://sankethbk.github.io/blog/gpu/2026-09-01-1-cuda-shared-memory-tiling"&gt;previous note&lt;/a&gt; showed how to use shared memory to fix uncoalesced access patterns. This note covers &lt;strong&gt;occupancy&lt;/strong&gt;: how many warps can live on an SM at the same time, and why that number determines whether the GPU can hide memory latency.&lt;/p&gt;
&lt;p&gt;Occupancy is the first CUDA performance topic that is not about memory access at all. It is about keeping the warp schedulers busy.&lt;/p&gt;</description></item></channel></rss>