<?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>Journal on Jun Kwan</title><link>https://junwenkwan.github.io/posts/</link><description>Recent content in Journal on Jun Kwan</description><generator>Hugo -- 0.147.2</generator><language>en-us</language><lastBuildDate>Mon, 09 Mar 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://junwenkwan.github.io/posts/index.xml" rel="self" type="application/rss+xml"/><item><title>GPU Acceleration with the C++ Standard Libray</title><link>https://junwenkwan.github.io/posts/nvidia-gpu-acceleration/</link><pubDate>Mon, 09 Mar 2026 00:00:00 +0000</pubDate><guid>https://junwenkwan.github.io/posts/nvidia-gpu-acceleration/</guid><description>&lt;p>This post is based on &lt;a href="https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-AC-08+V1">this self-paced course&lt;/a> offered by NVIDIA&amp;rsquo;s Deep Learning Institute.&lt;/p>
&lt;p>NVIDIA&amp;rsquo;s &lt;strong>stdpar&lt;/strong> lets you offload standard C++20 algorithms onto the GPU without CUDA kernels or new syntax. To show how this works in practice, we&amp;rsquo;ll use &lt;strong>DAXPY&lt;/strong>, which is a simple but memory-intensive linear algebra operation, and see how a few small code changes take it from a single-threaded CPU loop to full GPU execution.&lt;/p>
&lt;h2 id="daxpy-as-a-bandwidth-benchmark">DAXPY as a Bandwidth Benchmark&lt;/h2>
&lt;p>DAXPY stands for &lt;strong>D&lt;/strong>ouble-precision &lt;strong>A&lt;/strong>X &lt;strong>P&lt;/strong>lus &lt;strong>Y&lt;/strong>, and it computes the following equation.&lt;/p></description></item><item><title>Model Context Protocol</title><link>https://junwenkwan.github.io/posts/model-context-protocol/</link><pubDate>Wed, 04 Jun 2025 00:00:00 +0000</pubDate><guid>https://junwenkwan.github.io/posts/model-context-protocol/</guid><description>&lt;p>Model Context Protocol (MCP) is an open source standard for LLMs to be interacting with applications. MCP is originally introduced by Anthropic &lt;a href="https://www.anthropic.com/news/model-context-protocol">in this post&lt;/a>. With MCP, LLMs now can have assess to custom tools or data source to provide a more intelligent response.&lt;/p>
&lt;p>In addition, MCP also allows developers to build agents capable of more tasks, such as searching the internet or exploring dataset. MCP essentially standardizes LLM applications to work with many toolings. A nice quote by an Anthropic developer, &amp;ldquo;the models are only as good as the context provided to them&amp;rdquo;.&lt;/p></description></item></channel></rss>