第17章 空闲空间管理

Homework

The program, malloc.py, lets you explore the behavior of a simple free-space allocator as described in the chapter. See the README for details of its basic operation.

Questions

1. First run with the flags -n 10 -H 0 -p BEST -s 0 to generate a few random allocations and frees. Can you predict what alloc()/free() will return? Can you guess the state of the free list after each request? What do you notice about the free list over time?

题目要求：

5次内存申请操作，头结构大小为 0，利用最优匹配算法管理空闲列表 ，随机数种子为0

可以观察到，因为不支持合并操作，在多次申请并释放内存后，内存碎片化很严重

python malloc.py -n 10 -H 0 -p BEST -s 0 -c

2. How are the results different when using a WORST fit policy to search the free list (-p WORST)? What changes?

相比最优匹配，最差匹配使得内存碎片化更严重 

python malloc.py -n 10 -H 0 -p WORST -s 0 -c

3. What about when using FIRST fit (-p FIRST)? What speeds up when you use first fit? 

首次匹配算法的运行速度很快，因为不用像最优匹配或者最差匹配一样遍历整个空闲列表

python malloc.py -n 10 -H 0 -p FIRST -s 0 -c

4. For the above questions, how the list is kept ordered can affect the time it takes to find a free location for some of the policies. Use the different free list orderings (-l ADDRSORT, -l SIZESORT+, -l SIZESORT-) to see how the policies and the list orderings interact. 

对于最优匹配，应该采用空闲列表升序排列的策略（就像最小堆），这样可以避免遍历

python malloc.py -n 10 -H 0 -p BEST -l SIZESORT+ -s 0 -c

对于最差匹配，应该采用空闲列表降序排列的策略（就像最大堆），这样可以避免遍历

python malloc.py -n 10 -H 0 -p WORST -l SIZESORT- -s 0 -c

对于首次匹配算法，应避免使用空闲列表降序排列的策略，因为会产生大量的内存碎片

书中建议采用基于地址排序的策略，因为这样有利于内存合并操作 

python malloc.py -n 10 -H 0 -p FIRST -l SIZESORT- -s 0 -c

5. Coalescing of a free list can be quite important. Increase the number of random allocations (say to -n 1000). What happens to larger allocation requests over time? Run with and without coalescing (i.e., without and with the -C flag). What differences in outcome do you see? How big is the free list over time in each case? Does the ordering of the list matter in this case? 

随着增加随机分配的数量(-n 1000)，可以模拟一个大型程序运行时对堆的操作， 可以观察到最差匹配的内存碎片化是最严重的

python malloc.py -n 1000 -H 0 -p BEST -c

python malloc.py -n 1000 -H 0 -p WORST -c   

python malloc.py -n 1000 -H 0 -p FIRST -c

 合并可以有效消除内存碎片，只要所有的malloc操作都要对应的free，那内存就可以恢复如初  

python malloc.py -n 1000 -H 0 -p FIRST -C -c

而空闲列表的排序至关重要。

以首次匹配为例子，如果基于地址排序，空闲列表可以很好的合并临近内存，从而消除内存碎片

但是如果按降序或者升序，就会导致内存碎片的增加 

python malloc.py -n 1000 -H 0 -p FIRST -l ADDRSORT -C -c

python malloc.py -n 1000 -H 0 -p FIRST -l SIZESORT+ -C -c

python malloc.py -n 1000 -H 0 -p FIRST -l SIZESORT- -C -c

6. What happens when you change the percent allocated fraction -P to higher than 50? What happens to allocations as it nears 100? What about as the percent nears 0?

-P 接近100时，堆的空间会被快速耗尽，导致后续的malloc操作失败

python malloc.py -n 100 -H 0 -p BEST -P 100 -C -c

-P 接近0时，就是每次malloc后立即free

python malloc.py -n 100 -H 0 -p BEST -P 1 -C -c

7. What kind of specific requests can you make to generate a highly fragmented free space? Use the -A flag to create fragmented free lists, and see how different policies and options change the organization of the free list. 

不free即可