<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Dask on PlumePHP</title><link>https://plumephp.com/tags/dask/</link><description>Recent content in Dask on PlumePHP</description><generator>Hugo</generator><language>zh-CN</language><lastBuildDate>Mon, 28 Sep 2026 10:00:00 +0800</lastBuildDate><atom:link href="https://plumephp.com/tags/dask/index.xml" rel="self" type="application/rss+xml"/><item><title>Python 高性能计算：Dask 与 Ray 分布式计算实战</title><link>https://plumephp.com/hpc-dask-ray-python/</link><pubDate>Mon, 28 Sep 2026 10:00:00 +0800</pubDate><guid>https://plumephp.com/hpc-dask-ray-python/</guid><description>&lt;h2 id="引言"&gt;引言&lt;/h2&gt;
&lt;p&gt;Python 是数据科学主流，但单线程 GIL 与内存瓶颈让它在「大数据」前吃力。&lt;strong&gt;Dask&lt;/strong&gt; 与 &lt;strong&gt;Ray&lt;/strong&gt; 是 Python 生态里最主流的两套分布式框架：Dask 把 Pandas/NumPy 的接口扩展到「集群内存」，Ray 则提供通用的任务/actor 并行运行时。理解它们，就能在笔记本上写代码、在集群上跑千核。&lt;/p&gt;</description></item></channel></rss>