撰写一个cache模拟器，模拟cache不同配置下的命中率。 语言不限，C/C++/Java/Python等各种语言均可。 使用提供的trace文件进行评测。 要求能输入以下Cache配置参数对不同配置下的Cache命中率进行统计。 （1）Sizes: 1024, 2048, 4096, 8192, 16384 bytes （2）Block size: 8 bytes，16 bytes，32bytes （3）Associativities: Direct Mapped, 2-way, 4-way, 8-way （4）Replacement Policy: LRU, FIFO，Random 不用做图形化输入界面，只要能输入参数即可。

好的，我明白了你的要求。以下是一个简单的Python程序，用于模拟Cache不同配置下的命中率，使用的是提供的trace文件进行评测。

import sys

# Cache配置参数
cache_sizes = [1024, 2048, 4096, 8192, 16384]
block_sizes = [8, 16, 32]
associativities = [1, 2, 4, 8]
replacement_policies = ['LRU', 'FIFO', 'Random']

# 读取trace文件
def read_trace(trace_file):
    with open(trace_file, 'r') as f:
        trace = f.readlines()
    return trace

# 模拟Cache
def simulate_cache(trace, cache_size, block_size, associativity, replacement_policy):
    num_blocks = cache_size // block_size
    num_sets = num_blocks // associativity
    cache = [[0, 0] for i in range(num_blocks)] # 标记位和计数器
    hits, misses = 0, 0
    for line in trace:
        address = int(line.split()[1], 16)
        block_address = address // block_size
        tag = block_address // num_sets
        set_index = block_address % num_sets
        found = False
        for i in range(associativity):
            index = set_index * associativity + i
            if cache[index][0] == tag:
                cache[index][1] += 1
                hits += 1
                found = True
                break
        if not found:
            misses += 1
            if replacement_policy == 'LRU':
                min_count = sys.maxsize
                min_index = -1
                for i in range(associativity):
                    index = set_index * associativity + i
                    if cache[index][1] < min_count:
                        min_count = cache[index][1]
                        min_index = index
                cache[min_index][0] = tag
                cache[min_index][1] = 0
            elif replacement_policy == 'FIFO':
                min_count = sys.maxsize
                min_index = -1
                for i in range(associativity):
                    index = set_index * associativity + i
                    if cache[index][1] < min_count:
                        min_count = cache[index][1]
                        min_index = index
                cache[min_index][0] = tag
                cache[min_index][1] = misses
            elif replacement_policy == 'Random':
                index = set_index * associativity + random.randint(0, associativity-1)
                cache[index][0] = tag
                cache[index][1] = 0
    hit_rate = hits / (hits + misses)
    return hit_rate

# 主程序
if __name__ == '__main__':
    trace_file = 'trace.txt' # trace文件路径
    trace = read_trace(trace_file)
    for cache_size in cache_sizes:
        for block_size in block_sizes:
            for associativity in associativities:
                for replacement_policy in replacement_policies:
                    hit_rate = simulate_cache(trace, cache_size, block_size, associativity, replacement_policy)
                    print('cache_size:', cache_size, 'block_size:', block_size, 'associativity:', associativity,
                          'replacement_policy:', replacement_policy, 'hit_rate:', hit_rate)

该程序首先定义了Cache的配置参数，读取了trace文件，然后通过`simulate_cache`函数模拟了Cache不同配置下的命中率。在这个函数中，我们首先计算出Cache的大小、块大小、组相连度和块数等参数，然后用一个二维数组`cache`来模拟Cache的存储，每个元素包含一个标记位和一个计数器。对于每个访问请求，我们首先计算出标记位、组索引和块索引，然后在Cache中查找该块是否存在，如果存在，就将命中计数器加1，否则将缺失计数器加1，并根据替换策略选择要替换的块，更新标记位和计数器。

在程序的主程序中，我们循环遍历所有的配置参数，调用`simulate_cache`函数模拟命中率，并输出结果。