mactab-bench/plot.py at main · jychen0611/mactab-bench · GitHub

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
import matplotlib.pyplot as plt
import csv

# Prepare storage
names = []
insert_times = []
lookup_times = []
vmpeaks = []
vmrsses = []

# Read CSV manually
with open('benchmark.csv', 'r') as f:
    reader = csv.reader(f)
    next(reader)  # Skip header line
    for row in reader:
        names.append(row[0])
        insert_times.append(float(row[1]))
        lookup_times.append(float(row[2]))
        vmpeaks.append(int(row[3]))
        vmrsses.append(int(row[4]))

# Set x positions
x = range(len(names))
bar_width = 0.35

# Plot Insert Time and Lookup Time
plt.figure(figsize=(10, 6))
plt.bar(x, insert_times, width=bar_width, label='Insert Time (s)', align='center')
plt.bar([i + bar_width for i in x], lookup_times, width=bar_width, label='Lookup Time (s)', align='center')
plt.xticks([i + bar_width/2 for i in x], names, rotation=20, ha='right')
plt.ylabel('Time (seconds in log scale)')
plt.yscale('log')
plt.title('Benchmark Insert vs Lookup Time')
plt.legend()
plt.grid(axis='y', which='both')
plt.tight_layout()
plt.show()

# Plot Memory Usage (VmRSS and VmPeak)
plt.figure(figsize=(10, 6))
plt.bar(x, vmrsses, width=bar_width, label='VmRSS (kB)', align='center')
plt.bar([i + bar_width for i in x], vmpeaks, width=bar_width, label='VmPeak (kB)', align='center')
plt.xticks([i + bar_width/2 for i in x], names, rotation=20, ha='right')
plt.ylabel('Memory (kB)')
plt.title('Benchmark Memory Usage')
plt.legend()
plt.grid(axis='y')
plt.tight_layout()
plt.show()


# Plot Miss Rate vs LRU Cache Size (Hot/Cold Traffic)

# Read the file manually
filename = 'sweep_result.csv'  # your CSV file

# Prepare data storage
hot_ratio_to_data = {}

with open(filename, 'r') as f:
    header = f.readline()  # skip the first line
    for line in f:
        parts = line.strip().split(',')
        if len(parts) != 6:
            continue  # skip invalid lines

        name, lru_size, hot_ratio, insert_time, lookup_time, miss_rate = parts
        lru_size = int(lru_size)
        hot_ratio = int(hot_ratio)
        miss_rate = float(miss_rate)

        if hot_ratio not in hot_ratio_to_data:
            hot_ratio_to_data[hot_ratio] = {'lru_sizes': [], 'miss_rates': []}

        hot_ratio_to_data[hot_ratio]['lru_sizes'].append(lru_size)
        hot_ratio_to_data[hot_ratio]['miss_rates'].append(miss_rate)

# Now plot
plt.figure(figsize=(10, 6))

for hot_ratio in sorted(hot_ratio_to_data.keys()):
    lru_sizes = hot_ratio_to_data[hot_ratio]['lru_sizes']
    miss_rates = hot_ratio_to_data[hot_ratio]['miss_rates']
    plt.plot(lru_sizes, miss_rates, marker='o', label=f'Hot {hot_ratio}%')

# Labels and title
plt.xlabel('LRU Cache Size')
plt.ylabel('Miss Rate')
plt.title('Miss Rate vs LRU Cache Size (Hot/Cold Traffic)')
plt.grid(True)
plt.legend(title="Hot Ratio")
plt.tight_layout()

# Show the plot
plt.savefig('miss_rate_vs_lru_size.png')
plt.show()