-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathlru.py
136 lines (109 loc) · 4.44 KB
/
lru.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
from functools import lru_cache
import sys
import random
import numpy as np
import pandas as pd
import argparse
from tqdm import tqdm_notebook as tqdm
from collections import Counter, deque, defaultdict
from sklearn import preprocessing
from sklearn.preprocessing import normalize
from sklearn.metrics import accuracy_score, confusion_matrix
import pandas as pd
import torch
def LFU(blocktrace, frame):
cache = set()
cache_frequency = defaultdict(int)
frequency = defaultdict(int)
hit, miss = 0, 0
lfu = np.zeros(len(blocktrace))
lfu_miss = np.zeros(len(blocktrace))
i=0
for block in tqdm(blocktrace, leave=False):
frequency[block] += 1
if block in cache:
hit += 1
cache_frequency[block] += 1
lfu[i] = 1
elif len(cache) < frame:
cache.add(block)
cache_frequency[block] += 1
miss += 1
lfu_miss[i] = block
else:
e, f = min(cache_frequency.items(), key=lambda a: a[1])
cache_frequency.pop(e)
cache.remove(e)
cache.add(block)
cache_frequency[block] = frequency[block]
miss += 1
lfu_miss[i] = block
i = i+1
hitrate = hit / ( hit + miss )
print(hitrate)
return lfu,lfu_miss
def LRU(blocktrace, frame):
cache = set()
recency = deque()
hit, miss = 0, 0
lru = np.zeros(len(blocktrace))
lru_miss = np.zeros(len(blocktrace))
i=0
for block in tqdm(blocktrace, leave=False):
if block in cache:
recency.remove(block)
recency.append(block)
hit += 1
lru[i] = 1
elif len(cache) < frame:
cache.add(block)
recency.append(block)
miss += 1
lru_miss[i] = block
else:
cache.remove(recency[0])
recency.popleft()
cache.add(block)
recency.append(block)
miss += 1
lru_miss[i] = block
i=i+1
hitrate = hit / (hit + miss)
print(hitrate)
return lru,lru_miss
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='caching algorithm.\n')
parser.add_argument('cache_percent', type=float, help='relative cache size, e.g., 0.2 stands for 20\% of total trace length\n')
parser.add_argument('traceFile', type=str, help='trace file name\n')
args = parser.parse_args()
cache_size = args.cache_percent
traceFile = args.traceFile
block_trace, offsets, lengths = torch.load(traceFile)
block_trace = [x.item() for x in block_trace]
#block_tmp = []
#for i in range (10000):
# block_tmp.append(block_trace[i].item())
blockTraceLength = len(block_trace)
cache_size = int(cache_size * blockTraceLength)
print("processed!")
#blockTraceLength = len(block_tmp)
#cache_size = int(cache_size * blockTraceLength)
print (f"created block trace list, cache size is {cache_size}")
# build LRU
lru_cache,lru_miss = LRU(block_trace, cache_size)
#lru_cache, lru_miss = LRU(block_tmp, cache_size)
cached_trace = traceFile[0:traceFile.rfind(".pt")] + "_lru_cache.csv"
df = pd.DataFrame(lru_cache)
df.to_csv(cached_trace)
cached_trace = traceFile[0:traceFile.rfind(".pt")] + "_lru_miss.csv"
df = pd.DataFrame(lru_miss)
df.to_csv(cached_trace)
# build LFU
lfu_cache, lfu_miss = LFU(block_trace, cache_size)
#lfu_cache, lfu_miss = LFU(block_tmp, cache_size)
cached_trace = traceFile[0:traceFile.rfind(".pt")] + "_lfu_cache.csv"
df = pd.DataFrame(lfu_cache)
df.to_csv(cached_trace)
cached_trace = traceFile[0:traceFile.rfind(".pt")] + "_lfu_miss.csv"
df = pd.DataFrame(lfu_miss)
df.to_csv(cached_trace)