<?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>Programming on Sanketh's Blog</title><link>https://sankethbk.github.io/blog/tags/programming/</link><description>Recent content in Programming on Sanketh's Blog</description><generator>Hugo -- 0.166.0</generator><language>en-us</language><lastBuildDate>Sun, 30 Aug 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://sankethbk.github.io/blog/tags/programming/index.xml" rel="self" type="application/rss+xml"/><item><title>CUDA Programming Model: Writing and Launching Kernels</title><link>https://sankethbk.github.io/blog/posts/gpu/2026-08-30-2-cuda-programming-model/</link><pubDate>Sun, 30 Aug 2026 00:00:00 +0000</pubDate><guid>https://sankethbk.github.io/blog/posts/gpu/2026-08-30-2-cuda-programming-model/</guid><description>&lt;h1 id="cuda-programming-model-writing-and-launching-kernels"&gt;CUDA Programming Model: Writing and Launching Kernels&lt;/h1&gt;
&lt;p&gt;The &lt;a href="https://sankethbk.github.io/blog/gpu/2026-08-30-cuda-thread-hierarchy"&gt;previous note&lt;/a&gt; covered the hierarchy: thread, warp, block, grid, and how those map to SMs. This note turns that hierarchy into actual code. By the end, you will have seen a complete CUDA program, understood every line, and know how a host program hands work to the GPU.&lt;/p&gt;
&lt;p&gt;CUDA is an extension of C/C++. Most of the code you write is ordinary C++. A small number of CUDA-specific pieces — kernel functions, the launch syntax, a few memory APIs, and built-in thread indices — turn a sequential program into a massively parallel one.&lt;/p&gt;</description></item></channel></rss>