代码用页面置换算法计算访问命中率。 (1)先进先出算法(FIFO) (2)最近最少使用算法(LRU) (3)最优置换算法(OPT)
时间: 2023-10-16 19:10:40 浏览: 104
页面置换算法三种(LRU OPT FIFO)
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以下是使用Python实现页面置换算法,并计算访问命中率的示例代码。代码中使用了随机数生成器模拟指令序列和页面序列,可根据实际需求进行修改。
```python
import random
# 随机生成指令序列
instructions = [random.randint(0, 319) for i in range(1000)]
# 页面大小为1KB
page_size = 1024
# 用户内存容量为4~32页
min_pages = 4
max_pages = 32
# 随机生成页面序列
page_seq = [random.randint(1, 100) for i in range(1000)]
page_seq = [page_seq[i:i+page_size] for i in range(0, len(page_seq), page_size)]
# 先进先出算法
def fifo(page_seq, num_pages):
# 初始化页表和缓存队列
page_table = [-1] * num_pages
cache_queue = []
hit_count = 0
miss_count = 0
# 遍历指令序列
for i in instructions:
page_num = i // page_size
# 判断页面是否在缓存中
if page_num in cache_queue:
hit_count += 1
else:
miss_count += 1
# 缓存队列未满
if len(cache_queue) < num_pages:
cache_queue.append(page_num)
# 缓存队列已满,替换队首页面
else:
replace_page = cache_queue.pop(0)
cache_queue.append(page_num)
# 更新页表
page_table[replace_page] = -1
# 更新页表
if page_table[page_num] == -1:
page_table[page_num] = 1
else:
page_table[page_num] += 1
hit_rate = hit_count / len(instructions)
return hit_rate
# 最近最少使用算法
def lru(page_seq, num_pages):
# 初始化页表和缓存队列
page_table = [-1] * num_pages
cache_queue = []
hit_count = 0
miss_count = 0
# 遍历指令序列
for i in instructions:
page_num = i // page_size
# 判断页面是否在缓存中
if page_num in cache_queue:
hit_count += 1
# 更新缓存队列中页面的访问时间戳
cache_queue.remove(page_num)
cache_queue.append(page_num)
else:
miss_count += 1
# 缓存队列未满
if len(cache_queue) < num_pages:
cache_queue.append(page_num)
# 缓存队列已满,替换最近最少使用的页面
else:
min_time = float('inf')
replace_page = -1
for j in cache_queue:
if page_table[j] < min_time:
min_time = page_table[j]
replace_page = j
cache_queue.remove(replace_page)
cache_queue.append(page_num)
# 更新页表
page_table[replace_page] = -1
# 更新页表
if page_table[page_num] == -1:
page_table[page_num] = 1
else:
page_table[page_num] += 1
hit_rate = hit_count / len(instructions)
return hit_rate
# 最优置换算法
def opt(page_seq, num_pages):
# 初始化页表和缓存队列
page_table = [-1] * num_pages
cache_queue = []
hit_count = 0
miss_count = 0
# 遍历指令序列
for i in instructions:
page_num = i // page_size
# 判断页面是否在缓存中
if page_num in cache_queue:
hit_count += 1
else:
miss_count += 1
# 缓存队列未满
if len(cache_queue) < num_pages:
cache_queue.append(page_num)
# 缓存队列已满,替换下一个最长时间不被访问的页面
else:
max_time = -1
replace_page = -1
for j in cache_queue:
if page_table[j] == -1:
replace_page = j
break
elif page_table[j] > max_time:
max_time = page_table[j]
replace_page = j
cache_queue.remove(replace_page)
cache_queue.append(page_num)
# 更新页表
page_table[replace_page] = -1
# 更新页表
if page_num in page_seq:
page_table[page_num] = page_seq.index(page_num)
else:
page_table[page_num] = len(page_seq)
# 删除已经访问的页面
if len(page_seq) > 1:
page_seq = page_seq[1:]
hit_rate = hit_count / len(instructions)
return hit_rate
# 计算不同页面置换算法的访问命中率
for num_pages in range(min_pages, max_pages+1):
print("===============")
print("Number of pages:", num_pages)
print("FIFO hit rate:", fifo(page_seq, num_pages))
print("LRU hit rate:", lru(page_seq, num_pages))
print("OPT hit rate:", opt(page_seq, num_pages))
```
在运行代码时,可以设置页面大小、用户内存容量和指令序列等参数,以测试不同页面置换算法的性能。
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