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Rebuild rocky9_6 with kernel-5.14.0-570.17.1.el9_6
Rebuild_History BUILDABLE Rebuilding Kernel from rpm changelog with Fuzz Limit: 87.50% Number of commits in upstream range v5.14~1..kernel-mainline: 296506 Number of commits in rpm: 40 Number of commits matched with upstream: 37 (92.50%) Number of commits in upstream but not in rpm: 296469 Number of commits NOT found in upstream: 3 (7.50%) Rebuilding Kernel on Branch rocky9_6_rebuild_kernel-5.14.0-570.17.1.el9_6 for kernel-5.14.0-570.17.1.el9_6 Clean Cherry Picks: 35 (94.59%) Empty Cherry Picks: 2 (5.41%) _______________________________ Full Details Located here: ciq/ciq_backports/kernel-5.14.0-570.17.1.el9_6/rebuild.details.txt Includes: * git commit header above * Empty Commits with upstream SHA * RPM ChangeLog Entries that could not be matched Individual Empty Commit failures contained in the same containing directory. The git message for empty commits will have the path for the failed commit. File names are the first 8 characters of the upstream SHA
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‎Documentation/core-api/union_find.rst‎

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.. SPDX-License-Identifier: GPL-2.0
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====================
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Union-Find in Linux
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====================
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:Date: June 21, 2024
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:Author: Xavier <[email protected]>
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What is union-find, and what is it used for?
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------------------------------------------------
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Union-find is a data structure used to handle the merging and querying
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of disjoint sets. The primary operations supported by union-find are:
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Initialization: Resetting each element as an individual set, with
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each set's initial parent node pointing to itself.
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Find: Determine which set a particular element belongs to, usually by
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returning a “representative element” of that set. This operation
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is used to check if two elements are in the same set.
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Union: Merge two sets into one.
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As a data structure used to maintain sets (groups), union-find is commonly
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utilized to solve problems related to offline queries, dynamic connectivity,
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and graph theory. It is also a key component in Kruskal's algorithm for
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computing the minimum spanning tree, which is crucial in scenarios like
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network routing. Consequently, union-find is widely referenced. Additionally,
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union-find has applications in symbolic computation, register allocation,
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and more.
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Space Complexity: O(n), where n is the number of nodes.
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Time Complexity: Using path compression can reduce the time complexity of
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the find operation, and using union by rank can reduce the time complexity
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of the union operation. These optimizations reduce the average time
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complexity of each find and union operation to O(α(n)), where α(n) is the
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inverse Ackermann function. This can be roughly considered a constant time
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complexity for practical purposes.
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This document covers use of the Linux union-find implementation. For more
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information on the nature and implementation of union-find, see:
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Wikipedia entry on union-find
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https://en.wikipedia.org/wiki/Disjoint-set_data_structure
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Linux implementation of union-find
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-----------------------------------
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Linux's union-find implementation resides in the file "lib/union_find.c".
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To use it, "#include <linux/union_find.h>".
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The union-find data structure is defined as follows::
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struct uf_node {
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struct uf_node *parent;
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unsigned int rank;
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};
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In this structure, parent points to the parent node of the current node.
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The rank field represents the height of the current tree. During a union
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operation, the tree with the smaller rank is attached under the tree with the
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larger rank to maintain balance.
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Initializing union-find
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--------------------
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You can complete the initialization using either static or initialization
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interface. Initialize the parent pointer to point to itself and set the rank
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to 0.
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Example::
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struct uf_node my_node = UF_INIT_NODE(my_node);
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or
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uf_node_init(&my_node);
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Find the Root Node of union-find
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--------------------------------
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This operation is mainly used to determine whether two nodes belong to the same
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set in the union-find. If they have the same root, they are in the same set.
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During the find operation, path compression is performed to improve the
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efficiency of subsequent find operations.
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Example::
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int connected;
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struct uf_node *root1 = uf_find(&node_1);
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struct uf_node *root2 = uf_find(&node_2);
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if (root1 == root2)
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connected = 1;
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else
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connected = 0;
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Union Two Sets in union-find
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----------------------------
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To union two sets in the union-find, you first find their respective root nodes
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and then link the smaller node to the larger node based on the rank of the root
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nodes.
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Example::
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uf_union(&node_1, &node_2);

‎Documentation/translations/zh_CN/core-api/union_find.rst‎

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.. SPDX-License-Identifier: GPL-2.0
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.. include:: ../disclaimer-zh_CN.rst
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:Original: Documentation/core-api/union_find.rst
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===========================
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Linux中的并查集（Union-Find）
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===========================
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:日期: 2024年6月21日
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:作者: Xavier <[email protected]>
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何为并查集，它有什么用？
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---------------------
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并查集是一种数据结构，用于处理一些不交集的合并及查询问题。并查集支持的主要操作：
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初始化：将每个元素初始化为单独的集合，每个集合的初始父节点指向自身
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查询：查询某个元素属于哪个集合，通常是返回集合中的一个“代表元素”。这个操作是为
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了判断两个元素是否在同一个集合之中。
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合并：将两个集合合并为一个。
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并查集作为一种用于维护集合（组）的数据结构，它通常用于解决一些离线查询、动态连通性和
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图论等相关问题，同时也是用于计算最小生成树的克鲁斯克尔算法中的关键，由于最小生成树在
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网络路由等场景下十分重要，并查集也得到了广泛的引用。此外，并查集在符号计算，寄存器分
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配等方面也有应用。
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空间复杂度: O(n)，n为节点数。
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时间复杂度：使用路径压缩可以减少查找操作的时间复杂度，使用按秩合并可以减少合并操作的
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时间复杂度，使得并查集每个查询和合并操作的平均时间复杂度仅为O(α(n))，其中α(n)是反阿
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克曼函数，可以粗略地认为并查集的操作有常数的时间复杂度。
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本文档涵盖了对Linux并查集实现的使用方法。更多关于并查集的性质和实现的信息，参见：
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维基百科并查集词条
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https://en.wikipedia.org/wiki/Disjoint-set_data_structure
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并查集的Linux实现
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----------------
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Linux的并查集实现在文件“lib/union_find.c”中。要使用它，需要
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“#include <linux/union_find.h>”。
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并查集的数据结构定义如下::
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struct uf_node {
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struct uf_node *parent;
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unsigned int rank;
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};
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其中parent为当前节点的父节点，rank为当前树的高度，在合并时将rank小的节点接到rank大
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的节点下面以增加平衡性。
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初始化并查集
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---------
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可以采用静态或初始化接口完成初始化操作。初始化时，parent 指针指向自身，rank 设置
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为 0。
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示例::
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struct uf_node my_node = UF_INIT_NODE(my_node);
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或
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uf_node_init(&my_node);
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查找并查集的根节点
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----------------
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主要用于判断两个并查集是否属于一个集合，如果根相同，那么他们就是一个集合。在查找过程中
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会对路径进行压缩，提高后续查找效率。
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示例::
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int connected;
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struct uf_node *root1 = uf_find(&node_1);
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struct uf_node *root2 = uf_find(&node_2);
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if (root1 == root2)
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connected = 1;
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else
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connected = 0;
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合并两个并查集
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-------------
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对于两个相交的并查集进行合并，会首先查找它们各自的根节点，然后根据根节点秩大小，将小的
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节点连接到大的节点下面。
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示例::
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uf_union(&node_1, &node_2);

‎MAINTAINERS‎

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F: drivers/visorbus/
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F: include/linux/visorbus.h
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UNION-FIND
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M: Xavier <[email protected]>
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L: [email protected]
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S: Maintained
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F: Documentation/core-api/union_find.rst
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F: Documentation/translations/zh_CN/core-api/union_find.rst
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F: include/linux/union_find.h
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F: lib/union_find.c
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1985519864
UNIVERSAL FLASH STORAGE HOST CONTROLLER DRIVER
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R: Alim Akhtar <[email protected]>
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R: Avri Altman <[email protected]>

‎Makefile.rhelver‎

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#
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# Use this spot to avoid future merge conflicts.
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# Do not trim this comment.
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RHEL_RELEASE = 570.16.1
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RHEL_RELEASE = 570.17.1
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#
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# ZSTREAM

‎ciq/ciq_backports/kernel-5.14.0-570.17.1.el9_6/rebuild.details.txt‎

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Rebuild_History BUILDABLE
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Rebuilding Kernel from rpm changelog with Fuzz Limit: 87.50%
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Number of commits in upstream range v5.14~1..kernel-mainline: 296506
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Number of commits in rpm: 40
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Number of commits matched with upstream: 37 (92.50%)
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Number of commits in upstream but not in rpm: 296469
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Number of commits NOT found in upstream: 3 (7.50%)
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Rebuilding Kernel on Branch rocky9_6_rebuild_kernel-5.14.0-570.17.1.el9_6 for kernel-5.14.0-570.17.1.el9_6
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Clean Cherry Picks: 35 (94.59%)
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Empty Cherry Picks: 2 (5.41%)
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_______________________________
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__EMPTY COMMITS__________________________
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93c8332c8373fee415bd79f08d5ba4ba7ca5ad15 Union-Find: add a new module in kernel library
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a22b3d54de94f82ca057cc2ebf9496fa91ebf698 cgroup/cpuset: Fix race between newly created partition and dying one
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__CHANGES NOT IN UPSTREAM________________
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Porting to Rocky Linux 9, debranding and Rocky branding'
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Ensure aarch64 kernel is not compressed'
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cgroup/cpuset: Add warnings to catch inconsistency in exclusive CPUs

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