<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:sy="http://purl.org/rss/1.0/modules/syndication/" xmlns:media="http://search.yahoo.com/mrss/"><channel><title>Distributed Systems on Shaowen Chen's Website</title><link>https://www.chenshaowen.com/en/tags/distributed-systems/</link><description>Recent content in Distributed Systems on Shaowen Chen's Website</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>&amp;copy;2016 - {year}, All Rights Reserved.</copyright><lastBuildDate>Sun, 08 Feb 2026 00:00:00 +0000</lastBuildDate><sy:updatePeriod>weekly</sy:updatePeriod><atom:link href="https://www.chenshaowen.com/en/tags/distributed-systems/atom.xml" rel="self" type="application/rss+xml"/><item><title>Multi-Node, Multi-Disk MinIO Cluster Performance Testing Under Different Erasure Code Configurations on IPoIB</title><link>https://www.chenshaowen.com/en/blog/multi-node-multi-disk-minio-cluster-performance-test-in-different-erasure-code-configurations-on-ipoib.html</link><pubDate>Sun, 08 Feb 2026 00:00:00 +0000</pubDate><atom:modified>Sun, 08 Feb 2026 00:00:00 +0000</atom:modified><guid>https://www.chenshaowen.com/en/blog/multi-node-multi-disk-minio-cluster-performance-test-in-different-erasure-code-configurations-on-ipoib.html</guid><description>The earlier tests found the bottleneck in the NIC; this post adds supplementary testing under IPoIB.
1. MinIO Cluster Environment 1.1 Creating the MinIO Cluster 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 export CONTAINER_CLI=nerdctl export IMAGE=minio/minio:RELEASE.</description><dc:creator>微信公众号</dc:creator><category>MinIO</category><category>Storage</category><category>Distributed Systems</category><category>Erasure Coding</category><category>IPoIB</category><category>Performance Testing</category><category>Benchmarking</category></item><item><title>MinIO Multi-Node, Multi-Disk Deployment and Operations</title><link>https://www.chenshaowen.com/en/blog/minio-multi-node-multi-disk-deployment-and-maintenance.html</link><pubDate>Sat, 07 Feb 2026 00:00:00 +0000</pubDate><atom:modified>Sat, 07 Feb 2026 00:00:00 +0000</atom:modified><guid>https://www.chenshaowen.com/en/blog/minio-multi-node-multi-disk-deployment-and-maintenance.html</guid><description>1. Environment Preparation 1.1 Preparing Data Disks Inspect the data disks 1 lsblk -d -o NAME,SIZE,TYPE | grep nvme 1 2 3 4 nvme0n1 745.2G disk nvme1n1 745.2G disk nvme2n1 745.2G disk nvme3n1 745.2G disk Prepare the storage directories 1 2 3 for i in {0..3}; do mkdir -p /mnt/data${i} done Format the data disks 1 2 3 for i in {0.</description><dc:creator>微信公众号</dc:creator><category>MinIO</category><category>Storage</category><category>Distributed Systems</category><category>Operations</category><category>Learning</category><category>Object Storage</category><category>S3</category><category>Deployment</category><category>High Availability</category></item><item><title>Distributed Computing Framework Ray</title><link>https://www.chenshaowen.com/en/blog/what-is-ray.html</link><pubDate>Sun, 09 Feb 2025 10:00:00 +0000</pubDate><atom:modified>Sun, 09 Feb 2025 10:00:00 +0000</atom:modified><guid>https://www.chenshaowen.com/en/blog/what-is-ray.html</guid><description>1. What Ray Is In 2016, UC Berkeley&amp;rsquo;s RISELab released a new distributed computing framework called Ray. In 2017, after the Ray paper was published, it drew broad attention across the industry; in China it was mainly Ant Group that adopted and contributed to Ray. In 2020, Ray released version</description><dc:creator>微信公众号</dc:creator><category>Distributed Systems</category><category>Computing Framework</category><category>Ray</category><category>Training</category><category>Inference</category></item><item><title>MPI Communication Primitives and Their Use in Python Programming</title><link>https://www.chenshaowen.com/en/blog/mpi-communication-primitives-and-python-programming.html</link><pubDate>Sun, 10 Nov 2024 00:00:00 +0000</pubDate><atom:modified>Sun, 10 Nov 2024 00:00:00 +0000</atom:modified><guid>https://www.chenshaowen.com/en/blog/mpi-communication-primitives-and-python-programming.html</guid><description>1. What MPI Is MPI, the Message Passing Interface, is a communication protocol used for parallel computing. MPI provides a set of standardized interfaces for transferring data between different compute nodes, and is widely used in scientific computing, machine learning, deep learning, and other fields. MPI has multiple implementations; the</description><dc:creator>微信公众号</dc:creator><category>MPI</category><category>AI</category><category>Distributed Systems</category><category>Communication</category></item><item><title>Data Parallel Architecture in Distributed Training</title><link>https://www.chenshaowen.com/en/blog/data-parallel-architecture-in-distributed-train.html</link><pubDate>Wed, 21 Aug 2024 00:00:00 +0000</pubDate><atom:modified>Wed, 21 Aug 2024 00:00:00 +0000</atom:modified><guid>https://www.chenshaowen.com/en/blog/data-parallel-architecture-in-distributed-train.html</guid><description>1. Parameter Server Architecture In the Parameter Server architecture, the nodes in the cluster are divided into two categories: parameter server nodes (Parameter Server) and worker server nodes (Worker).
1.1 Parameter Server The Parameter Server is used to store the model&amp;rsquo;s parameters.
Each parameter server node is responsible for managing and updating one part of the model&amp;rsquo;s parameters, while each worker node only processes the subset of data corresponding to it.</description><dc:creator>微信公众号</dc:creator><category>Distributed Systems</category><category>Training</category><category>Data Parallelism</category><category>AI</category><category>Operations</category></item><item><title>Tekton Pipelines Across Multiple Clusters</title><link>https://www.chenshaowen.com/en/blog/using-kubefed-to-distribute-tekton-resource-cross-cluster.html</link><pubDate>Sat, 26 Jun 2021 00:00:00 +0000</pubDate><atom:modified>Sat, 26 Jun 2021 00:00:00 +0000</atom:modified><guid>https://www.chenshaowen.com/en/blog/using-kubefed-to-distribute-tekton-resource-cross-cluster.html</guid><description>1. The Advantages of Multi-Cluster Tekton Builds Thanks to Kubernetes, Tekton already has good elasticity and can support large-scale builds. At the same time, developing Tasks mainly uses Yaml and Shell, which widens the range of scenarios Tekton can adapt to.
The figure above is a sketch of Tekton across multiple clusters.</description><dc:creator>微信公众号</dc:creator><category>Tekton</category><category>KubeFed</category><category>Kubernetes</category><category>Multi-Cluster</category><category>CICD</category><category>Operations</category><category>Learning</category><category>Pipeline</category><category>Build</category><category>Distributed Systems</category></item></channel></rss>