<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Parallel Processing on code.kiwi.com</title><link>https://code.kiwi.com/tag/parallel-processing/</link><description>Recent content in Parallel Processing on code.kiwi.com</description><generator>Hugo</generator><language>en</language><lastBuildDate>Thu, 16 Mar 2023 11:02:44 +0000</lastBuildDate><atom:link href="https://code.kiwi.com/tag/parallel-processing/index.xml" rel="self" type="application/rss+xml"/><item><title>Memory efficiency of parallel IO operations in Python</title><link>https://code.kiwi.com/articles/memory-efficiency-of-parallel-io-operations-in-python/</link><pubDate>Tue, 13 Mar 2018 10:59:00 +0000</pubDate><guid>https://code.kiwi.com/articles/memory-efficiency-of-parallel-io-operations-in-python/</guid><description>&lt;p&gt;Python allows for several different approaches to parallel processing. The main issue with parallelism is knowing its limitations. We either want to parallelise IO operations or CPU-bound tasks like image processing. The first use case is something we focused on in the recent &lt;a href="https://www.facebook.com/pg/codekiwicom/photos/?tab=album&amp;amp;album_id=1994315244222234"&gt;Python Weekend&lt;/a&gt;* and this article provides a summary of what we came up with.&lt;/p&gt;</description></item></channel></rss>