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plot_cache_stat.py
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plot_cache_stat.py
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'''
Plot cache statistics
'''
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import csv
import argparse
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Load csv file")
parser.add_argument(
"csvfile", help="The path to csv file containing cache analysis data")
args = parser.parse_args()
cache_size = [32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768]
block_size = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096]
associaivity = [1, 2, 4, 8, 16, 32]
data = {
'block_size': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096],
'miss_rate0': [0] * 13,
'miss_rate1': [0] * 13,
'miss_rate2': [0] * 13,
'miss_rate3': [0] * 13,
'miss_rate4': [0] * 13,
'miss_rate5': [0] * 13,
'miss_rate6': [0] * 13,
'miss_rate7': [0] * 13,
'miss_rate8': [0] * 13,
'miss_rate9': [0] * 13,
'miss_rate10': [0] * 13,
}
data2 = {
'associativity': [1, 2, 4, 8, 16, 32],
'miss_rate0': [0] * 6,
'miss_rate1': [0] * 6,
'miss_rate2': [0] * 6,
'miss_rate3': [0] * 6,
'miss_rate4': [0] * 6,
'miss_rate5': [0] * 6,
'miss_rate6': [0] * 6,
'miss_rate7': [0] * 6,
'miss_rate8': [0] * 6,
'miss_rate9': [0] * 6,
'miss_rate10': [0] * 6,
}
data3 = {
'cache_size': [32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768],
'wb_wa': [0] * 11,
'wb_nwa': [0] * 11,
'wt_wa': [0] * 11,
'wt_nwa': [0] * 11,
}
with open(args.csvfile, "r") as csvFile:
reader = csv.DictReader(csvFile)
for row in reader:
index = cache_size.index(int(row['cacheSize']) // 1024)
if int(row['blockSize']) == 64:
if int(row['writeBack']) == 1 and int(row['writeAllocate']) == 1:
data3['wb_wa'][index] = row['totalCycles']
elif int(row['writeBack']) == 1 and int(row['writeAllocate']) == 0:
data3['wb_nwa'][index] = row['totalCycles']
elif int(row['writeBack']) == 0 and int(row['writeAllocate']) == 1:
data3['wt_wa'][index] = row['totalCycles']
elif int(row['writeBack']) == 0 and int(row['writeAllocate']) == 0:
data3['wt_nwa'][index] = row['totalCycles']
if int(row['writeBack']) != 1 or int(row['writeAllocate']) != 1:
continue
key = 'miss_rate' + str(index)
data[key][block_size.index(
int(row['blockSize']))] = row['missRate']
if int(row['blockSize']) == 64:
data2[key][associaivity.index(
int(row['associativity']))] = row['missRate']
'''
print(data)
df = pd.DataFrame.from_dict(data)
for i in range(0, 11):
plt.plot('block_size', 'miss_rate'+str(i), data=df, marker='o')
# plt.ylim([0, 0.5])
plt.legend(['Cache Size: ' + str(cache_size[i]) +
'KB' for i in range(0, 11)])
plt.gca().invert_yaxis()
plt.xlabel('Block Size (Bytes)')
plt.ylabel('Miss Rate')
plt.title(args.csvfile)
plt.show()
'''
'''
print(data2)
df = pd.DataFrame.from_dict(data2)
for i in range(0, 11):
plt.plot('associativity', 'miss_rate'+str(i), data=df, marker='o')
plt.legend(['Cache Size: ' + str(cache_size[i]) +
'KB' for i in range(0, 11)])
plt.gca().invert_yaxis()
plt.xlabel('Associativity')
plt.ylabel('Miss Rate')
plt.title(args.csvfile)
plt.show()
'''
print(data3)
df = pd.DataFrame.from_dict(data3)
plt.plot('cache_size', 'wb_wa', data=df, marker='o')
plt.plot('cache_size', 'wb_nwa', data=df, marker='o')
plt.plot('cache_size', 'wt_wa', data=df, marker='o')
plt.plot('cache_size', 'wt_nwa', data=df, marker='o')
plt.legend(['WB, WA', 'WB, Non-WA', 'WT, WA', 'WT, Non-WA'])
plt.gca().invert_yaxis()
#plt.ylim(60000, 100000)
plt.xscale('log')
plt.xlabel('Cache Size (KB)')
plt.ylabel('Total Cycles')
plt.title(args.csvfile)
plt.show()