Wednesday, January 27, 2016

看数据结构写代码(50)伙伴系统 - fuming0210sc的专栏 - 博客频道 - CSDN.NET


看数据结构写代码(50)伙伴系统 - fuming0210sc的专栏 - 博客频道 - CSDN.NET
伙伴系统 是一种 只 可以 分配 2的 幂次方 个 空间的 ,回收 内存 时 只 合并 "伙伴空间" 的一种 动态内存管理方式。
例如 一个 空间 大小 为 64 的 内存,伙伴 系统 为 这 64 的内存  建立 一组 双向循环 链表,分别 管理着  2的 0 次方,2的1 次方幂,2的 2 次方幂。。。2的6次方幂的 可用空间。
即使 我们 只想分配 一个 大小 为3的 空间,系统 却 只能 返回 一个 内存 大小 为 4(2的2次方)的 一个空间。
系统 在 初始化的 时候 ,并不是 为 每一个 链表 都 放入 一些 可用空间 。而是 在 2的 6次方幂 插入 一个 空间节点,大小 为64
当我们 想 要 分配 内存的时候,会 查找 最近 内存大小的 链表,如果 链表 有 可用空间,取 第一个 空间。否则 顺次 查找 可用空间。
假设 我们 要在 这个 64 的 空间 里,分配 一个 大小 为 3 的内存,系统 怎么 分配呢?
系统 会 从 第一个链表 一直 找,直到 找到 2的 6次方的 链表。系统 会 将 前 4个 空间 分配 给用户。可是 其余的 60个空间 ,不能 再 插入到 2的 6次方 的链表中了。只能 插入到
比 它小的链表中去。
具体的 分配 方式 如下:4    4    8   16   32 。
 前4个 分给用户 使用,后 面的 4 8 16 32  依次 插入 到 2的 2次方幂,2的 3次方,4次方,5次方幂的链表中去。
这 有一个 规则:
1. 用户 空间 的 邻接空间 总是 被 拆分成  一个 跟 用户 空间 大小 一样的 空间。这个 空间 就是 上面 所有的 "伙伴空间"。
2. 再 下一个空间 依次 是 用户空间的 2倍,4倍,8倍,。。。直到 被拆分空间 一半。 例子的 拆分空间 为 64,它的一半 为 32。
所以 可以 算出:64 是 如何 被 拆分的: 4(用户空间) 4(伙伴空间) 8   16  32 


当我们 回收空间时,首先 会 查看 这个空间的 伙伴空间 是否 为 空闲。如果空闲 会 合并,并从 链表中 删除 伙伴空间。。然后 会 再次 查看 合并后的 空间。直至 无法 合并。如果 不空闲,插入到 合适的 链表中去。
伙伴空间 就是 上面 所说的 "4" ,必须 跟 回收空间 空间大小一样 ,而且 是 从 同一个 大块 内存 分配 出去的,
有一个公式可以求出 伙伴空间:




例如 释放 上面 的 空间, 会查找 伙伴空间 ,伙伴空间 空闲,合并空间 成为 一个 大小 为8 的空间,并从 2 的 2 次方 链表中 删除 伙伴空间,然后 继续 查找。 继续删除。。最终 会 合并 成 一个 在 2的 6次幂链表中的 一个 空间。 和 初始的情况一样。

#define MAX_SIZE 64//最大空间
#define LIST_LEN 7//表头数组长度
#define MAX_INDEX 6//表头数组最大下标
struct BuddyNode{
 BuddyNode * preLink;
 BuddyNode * nextLink;
 int k;//只能分配2 的k次幂
 int tag;//0 空闲, 1 占用
};

typedef struct BuddyHead{
 BuddyNode * head;
 int nodeSize;
}FreeList[LIST_LEN];

static BuddyNode * freeSpace = NULL;//为了释放内存
static BuddyNode * userSpace[MAX_SIZE] = {NULL};
static int userCount = 0;

void initFreeList(FreeList list){
 for (int i = 0; i < LIST_LEN; i++){
  list[i].head = NULL;
  list[i].nodeSize = (int)pow(2.0,i);
 }
 //分配最大空间
 BuddyNode * p = (BuddyNode*)malloc(sizeof(BuddyNode) * MAX_SIZE);
 if (p != NULL){
  p->k = MAX_INDEX;
  p->tag = 0;
  p->nextLink = p->preLink = p;//双向循环链表
 }
 freeSpace = list[MAX_INDEX].head = p;
}

void destoryFreeList(FreeList list){
 for (int i = 0; i < LIST_LEN; i++){
  list[i].head = NULL;
 }
 free(freeSpace);
}

BuddyNode * buddyAlloc(FreeList list,int size){
 bool isFirst = true;
 int k = -1;//分配空间的k值
 int needK = -1;//需要分配空间的k值.
 //查找第一个满足条件的空间.
 for (int i = 0; i < LIST_LEN; i++){
  BuddyHead head = list[i];
  if (head.nodeSize >= size){
   if (isFirst){
    needK = i;
    isFirst = false;
   }
   if (head.head != NULL){//找到可分配的空间了
    k = i;
    break;
   }
  }
 }
 BuddyNode * freeNode = NULL;
 if (k == -1){
  return NULL;//找不到满足条件的空间了..
 }
 else{
  //设置这个空间 被占用,并且 更换 表头元素..
  freeNode = list[k].head;
  freeNode->k = needK;
  freeNode->tag = 1;//设置 成 已占用..
  list[k].head = freeNode->nextLink;//重新设置表头..
  if (freeNode->preLink == freeNode->nextLink){//删除这个空间后,成为空表.
   list[k].head = NULL;
  }
  else{//删除这个节点.
   freeNode->preLink->nextLink = freeNode->nextLink;
   freeNode->nextLink->preLink = freeNode->preLink;
  }
  //剩余空间 依次 插在 needK 和 k-1 表里.
  for (int i = needK; i < k ; i++){//从高位开始分配..
   int index = (int)pow(2.0,i);
   BuddyNode * p = freeNode + index;
   p->k = i;
   p->tag = 0;
   BuddyNode * head = list[i].head;
   if (head != NULL){
    p->preLink = head->preLink;
    p->nextLink = head;
    p->preLink->nextLink = head->preLink = p;
   }
   else{
    p->preLink = p->nextLink = p;
   }
   list[i].head = p;//重新设置表头..
  }
 }
 userSpace[userCount++] = freeNode;
 return freeNode;
}

void userSpaceFree(BuddyNode * node){
 for (int i = 0; i < userCount; i++){
  if (userSpace[i] == node){
   userSpace[i] = NULL;
  }
 }
}

//伙伴算法 回收...
void buddyReclaim(FreeList list,BuddyNode * node){
 userSpaceFree(node);
 while (node->k < MAX_INDEX){//循环查找伙伴,k值达到 MAX_INDEX 不需要 寻找...
  int sub = node - freeSpace;//计算出 具体坐标位置
  BuddyNode * buddy = NULL;
  int i = (int)pow(2.0,node->k + 1);
  bool isNext = true;//伙伴是不是在后
  if(sub % i == 0){//伙伴在后
   buddy = node + i/2;
  }
  else{//伙伴在前.
   buddy = node - i/2;
   isNext = false;
  }
  if (buddy->tag == 0 ){//伙伴空闲,合并
   //首先从列表里释放 buddy
   if (buddy->preLink == buddy->nextLink){//表中 只有一个节点,释放后,成为空表
    list[buddy->k].head = NULL;
   }
   else{//删除节点
    buddy->preLink->nextLink = buddy->nextLink;
    buddy->nextLink->preLink = buddy->preLink;
    list[buddy->k].head = buddy->nextLink;
   }
   if (isNext == false){
    node = buddy;
   }
   node->k ++;
  }
  else{//伙伴空间被占用..
   break;
  }
 }
 BuddyNode * head = list[node->k].head;
 node->tag = 0;
 if (head == NULL){//表头为空
   node->preLink = node->nextLink = node;
 }
 else{
  node->nextLink = head;
  node->preLink = head->preLink;
  node->preLink->nextLink = head->preLink = node;
 }
 list[node->k].head = node;
}

void printList(FreeList list){
 printf("------------------------打印伙伴列表-------------------\n");
 for (int i = 0; i < LIST_LEN; i++){
  BuddyNode * head = list[i].head;
  if (head){
   printf("首地址空间为:%0x,表的前驱:%0x,后继:%0x,表空间大小是2的%d次方\n",head,head->preLink,head->nextLink,head->k);
  }
 }
 printf("------------------------用户空间-------------------\n");
 for (int i = 0; i < userCount; i++){
  BuddyNode * us = userSpace[i];
  if (us != NULL){
   printf("首地址空间为:%0x,表空间大小是2的%d次方\n",us,us->k);
  }
 }
 printf("\n");
}



int _tmain(int argc, _TCHAR* argv[])
{
 FreeList list;
 initFreeList(list);
 printList(list);
 printf("---------------分配一个1空间之后--------------\n");
 BuddyNode * s1_1 = buddyAlloc(list,1);
 printList(list);
 printf("---------------分配一个1空间之后--------------\n");
 BuddyNode * s1_2 = buddyAlloc(list,1);
 printList(list);
 printf("---------------分配一个29空间之后--------------\n");
 BuddyNode * s29 = buddyAlloc(list,29);
 printList(list);
 printf("---------------释放一个1空间之后--------------\n");
 buddyReclaim(list,s1_2);
 printList(list);
 printf("---------------释放一个1空间之后--------------\n");
 buddyReclaim(list,s1_1);
 printList(list);
 destoryFreeList(list);
 return 0;
}
 1.伙伴系统概念
  伙伴系统是一种经典的内存管理方法。Linux伙伴系统的引入为内核提供了一种用于分配一组连续的页而建立的一种高效的分配策略,并有效的解决了外碎片问题。
 2.伙伴系统的组织结构
  Linux中的内存管理的“页”大小为4KB把所有的空闲页分组为11个块链表,每个块链表分别包含大小为1248163264128256512和1024个连续页框的页块。最大可以申请1024个连续页,对应4MB大小的连续内存。每个页块的第一个页的物理地址是该块大小的整数倍。
   结构如图所示:第i个块链表中,num表示大小为(2^i)页块的数目,address表示大小为(2^i)页块的首地址。

 3.伙伴系统的内存分配及释放
  当向内核请求分配(2^(i-1)2^i]数目的页块时,按照2^i页块请求处理。如果对应的块链表中没有空闲页块,则在更大的页块链表中找。当分配的页块中有多余的页时,伙伴系统根据多余的页框大小插入到对应的空闲页块链表中。
  当释放单页的内存时,内核将其置于CPU高速缓存中,对很可能出现在cache的页,则放到“快表”的列表中。在此过程中,内核先判断CPU高速缓存中的页数是否超过一定“阈值”,如果是,则将一批内存页还给伙伴系统,然后将该页添加到CPU高速缓存中。
  释放多页的块时,内核首先计算出该内存块的伙伴的地址。内核将满足以下条件的三个块称为伙伴(1)两个块具有相同的大小,记作b(2)它们的物理地址是连续的。(3)第一块的第一个页的物理地址2*(2^b)的倍数。如果找到了该内存块的伙伴,确保该伙伴的所有页都是空闲的,以便进行合并。内存继续检查合并后页块的“伙伴”并检查是否可以合并,依次类推。
 4.伙伴系统的反碎片机制
  内核将已分配页分为以下三种不同的类型:
  (1)不可移动页:这些页在内存中有固定的位置,不能够移动。
  (2)可回收页:这些页不能移动,但可以删除。内核在回收页占据了太多的内存时或者内存短缺时进行页面回收。
  (3)可移动页:这些页可以任意移动,用户空间应用程序使用的页都属于该类别。它们是通过页表映射的。当它们移动到新的位置,页表项也会相应的更新。
  在内存子系统初始化期间,所有的页都被标记为可移动的。在启动期间,核心内核分配的内存是不可移动的。此时内核的策略是分配一个尽可能大的连续内存块,将其从可移动列表转换到不可移动列表。分配一个尽可能大的连续内存块而不是选择更小的满足要求的内存块的原因如下:
  例如:现有一块可移动的具有16页的连续空闲内存块。当内核需要分配1页不可移动内存页时。分配器选择后8(而不是一页)转移到不可移动列表,然后从不可移动列表中选择1用于分配。如果内核又需要分配1页不可移动内存页则从不可移动列表中选择一页用于分配,下图显示了进行4次分配后内存的分布。
  但如果内核只选择1页用于分配将其加入到不可移动列表,当下次需要分配时又选择一页加入到不可移动列表,下图显示了按照这种方式进行4次分配后内存的分布。
  

  因此,当没有满足可用于分配的不可移动空闲块时,分配器会在可移动列表中迁移一个尽可能大的连续内存块给不可移动列表。这样避免了启动期间内核分配的内存散列到物理内存各处,从而使其他类型的内存分配免受碎片的干扰。
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How To (1) Algorithm - New (1) Algorithm Series (1) Algorithms Part I (1) Analysis of Algorithm (1) Array-Element Index Negative (1) Array-Rearrange (1) Auxiliary Array (1) Auxiliary Array: Inc&Dec (1) BACK (1) BK-Tree (1) BZOJ (1) Basic (1) Bayes (1) Beauty of Math (1) Big Integer (1) Big Number (1) Binary (1) Binary Tree Variant (1) Bipartite (1) Bit-Missing Number (1) BitMap (1) BitMap index (1) BitSet (1) Bug Free Code (1) BuildIt (1) C/C++ (1) CC Interview (1) Cache (1) Calculate Height at Same Recusrion (1) Cartesian tree (1) Check Tree Property (1) Chinese (1) Circular Buffer (1) Code Quality (1) Codesolutiony (1) Company - Alibaba (1) Company - Palantir (1) Company - WalmartLabs (1) Company-Apple (1) Company-Epic (1) Company-Salesforce (1) Company-Snapchat (1) Company-Yelp (1) Compression Algorithm (1) Concurrency (1) Convert BST to DLL (1) Convert DLL to BST (1) Custom Sort (1) Cyclic Replacement (1) DFS-Matrix (1) DP - Probability (1) DP Fill Diagonal First (1) DP-Difficult (1) DP-End with 0 or 1 (1) DP-Fill Diagonal First (1) DP-Graph (1) DP-Left and Right Array (1) DP-MaxMin (1) DP-Memoization (1) DP-Node All Possibilities (1) DP-Optimization (1) DP-Preserve Previous Value (1) DP-Print All Solution (1) Database (1) Detect Negative Cycle (1) Directed Graph (1) Do Two Things at Same Recusrion (1) Domino (1) Dr Dobb's (1) Duplicate (1) Equal probability (1) External Sort (1) FST (1) Failure Function (1) Fraction (1) Front End Pointers (1) Funny (1) Fuzzy String Search (1) Game (1) Generating Function (1) Generation (1) Genetic algorithm (1) GeoHash (1) Geometry - Orientation (1) Google APAC (1) Graph But No Graph (1) Graph Transpose (1) Graph Traversal (1) Graph-Coloring (1) Graph-Longest Path (1) Gray Code (1) HOJ (1) Hanoi (1) Hard Algorithm (1) How Hash (1) How to Test (1) Improve It (1) In Place (1) Inorder-Reverse Inorder Traverse Simultaneously (1) Interpolation search (1) Interview (1) Interview - Easy (1) Interview - Facebook (1) Isomorphic (1) JDK8 (1) K Dimensional Tree (1) Knapsack - Fractional (1) Knapsack - ZeroOnePack (1) Knight (1) Kosaraju’s algorithm (1) Kruskal (1) Kruskal MST (1) Kth Element (1) Least Common Ancestor (1) LeetCode - Binary Tree (1) LeetCode - Coding (1) LeetCode - Detail (1) LeetCode - Related (1) LeetCode Diffcult (1) Linked List Reverse (1) Linkedin (1) Linkedin Interview (1) Local MinMax (1) Logic Pattern (1) Longest Common Subsequence (1) Longest Common Substring (1) Longest Prefix Suffix(LPS) (1) Manhattan Distance (1) Map && Reverse Map (1) Math - Induction (1) Math-Multiply (1) Math-Sum Of Digits (1) Matrix - O(N+M) (1) Matrix BFS (1) Matrix Graph (1) Matrix Search (1) Matrix+DP (1) Matrix-Rotate (1) Max Min So Far (1) Median (1) Memory-Efficient (1) MinHash (1) MinMax Heap (1) Monotone Queue (1) Monto Carlo (1) Multi-Reverse (1) Multiple DFS (1) Multiple Tasks (1) Next Successor (1) Offline Algorithm (1) PAT (1) Partition (1) Path Finding (1) Patience Sort (1) Persistent (1) Pigeon Hole Principle (1) Power Set (1) Pratical Algorithm (1) Probabilistic Data Structure (1) Proof (1) Python (1) Queue & Stack (1) RSA (1) Ranking (1) Rddles (1) ReHash (1) Realtime (1) Recurrence Relation (1) Recursive DFS (1) Recursive to Iterative (1) Red-Black Tree (1) Region (1) Regular Expression (1) Resources (1) Reverse Inorder Traversal (1) Robin (1) Selection (1) Self Balancing BST (1) Similarity (1) Sort && Binary Search (1) String Algorithm. Symbol Table (1) String DP (1) String Distance (1) SubMatrix (1) Subsequence (1) System of Difference Constraints(差分约束系统) (1) TSP (1) Ternary Search Tree (1) Test (1) Thread (1) TimSort (1) Top-Down (1) Tournament (1) Tournament Tree (1) Transform Tree in Place (1) Tree Diameter (1) Tree Rotate (1) Trie + DFS (1) Trie and Heap (1) Trie vs Hash (1) Trie vs HashMap (1) Triplet (1) Two Data Structures (1) Two Stacks (1) USACO - Classical (1) USACO - Problems (1) UyHiP (1) Valid Tree (1) Vector (1) Wiggle Sort (1) Wikipedia (1) Yahoo Interview (1) ZOJ (1) baozitraining (1) codevs (1) cos126 (1) javabeat (1) jum (1) namic Programming (1) sqrt(N) (1) 两次dijkstra (1) 九度 (1) 二进制枚举 (1) 夹逼法 (1) 归一化 (1) 折半枚举 (1) 枚举 (1) 状态压缩DP (1) 男人八题 (1) 英雄会 (1) 逆向思维 (1)

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