<?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>Workflow Optimization on Vladimir Lapin</title><link>https://vlap.github.io/tags/workflow-optimization/</link><description>Recent content in Workflow Optimization on Vladimir Lapin</description><image><title>Vladimir Lapin</title><url>https://vlap.github.io/images/decor/vladimir_lapin.jpg</url><link>https://vlap.github.io/images/decor/vladimir_lapin.jpg</link></image><generator>Hugo</generator><language>en-us</language><lastBuildDate>Mon, 20 Jul 2026 12:00:00 +0200</lastBuildDate><atom:link href="https://vlap.github.io/tags/workflow-optimization/index.xml" rel="self" type="application/rss+xml"/><item><title>Eliminating the Conda Bottleneck on HPC: Fast Python &amp; Climate Workflows with uv on Lustre</title><link>https://vlap.github.io/posts/2026-07-20-scaling-python-hpc-uv-lustre/</link><pubDate>Mon, 20 Jul 2026 12:00:00 +0200</pubDate><guid>https://vlap.github.io/posts/2026-07-20-scaling-python-hpc-uv-lustre/</guid><description>We&amp;rsquo;ve all watched a 64-node Slurm job stall for three minutes just importing xarray. Here is why standard Conda environments choke parallel filesystems like Lustre, and how pairing Astral uv with system LMOD modules eliminates job startup lag on MareNostrum 5.</description></item></channel></rss>