<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
	<channel>
		<title>GPU Computing on Recep&#39;s Technical Blog</title>
		<link>https://erolrecep.github.io/tags/gpu-computing/</link>
		<description>Recent content in GPU Computing on Recep&#39;s Technical Blog</description>
		<generator>Hugo</generator>
		<language>en-US</language>
		
		
		
		
			<lastBuildDate>Mon, 02 Nov 2026 12:00:00 -0600</lastBuildDate>
		
			<atom:link href="https://erolrecep.github.io/tags/gpu-computing/index.xml" rel="self" type="application/rss+xml" />
			<item>
				<title>Best Practices for Using HPC: Scaling Up to Cluster-Native Workflows</title>
				<link>https://erolrecep.github.io/posts/best-practices-for-hpc/</link>
				<pubDate>Mon, 02 Nov 2026 12:00:00 -0600</pubDate>
				<guid>https://erolrecep.github.io/posts/best-practices-for-hpc/</guid>
				<description>&lt;p&gt;&lt;em&gt;Moving Beyond the Laptop Mindset: A Practical Guide for Researchers and Computational Scientists&lt;/em&gt;&lt;/p&gt;&#xA;&lt;hr&gt;&#xA;&lt;h2 id=&#34;abstract&#34;&gt;Abstract&lt;/h2&gt;&#xA;&lt;p&gt;Unlocking the full potential of advanced computational infrastructure requires moving beyond the mindset of simply scaling up local code. While transitioning from a personal workstation or Google Colab notebook to a shared High-Performance Computing (HPC) cluster removes local compute and memory bottlenecks, true acceleration is achieved only by adopting &lt;strong&gt;cluster-native workflows&lt;/strong&gt;.&lt;/p&gt;&#xA;&lt;p&gt;Modern supercomputing systems are not simply bigger laptops; they are orchestrated, multi-tenant community ecosystems governed by batch schedulers, distributed parallel filesystems, and specialized hardware accelerators. Navigating these environments demands an operational paradigm shift.&lt;/p&gt;</description>
			</item>
	</channel>
</rss>
