-
Notifications
You must be signed in to change notification settings - Fork 0
/
generate_ipc_summary.py
216 lines (205 loc) · 6.08 KB
/
generate_ipc_summary.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
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
import enum
import math
import pathlib
from functools import wraps
from pathlib import Path
import click
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from matplotlib import colors
from matplotlib.ticker import PercentFormatter
from tqdm import tqdm
pd.set_option("display.max_columns", 5)
pd.set_option("display.width", 1000)
name_map = {
"ChampSim_fdip_lru": "lru",
"ChampSim_fdip_opt": "opt",
"ChampSim_fdip_srrip": "srrip",
"ChampSim_fdip_hot_warm_cold_80_50_f_keep_curr_hotter": "hwc 80 50 curr hotter",
"ChampSim_fdip_hot_warm_cold_80_50_f_keep_curr_hotter_lru": "hwc 80 50 curr hotter lru",
}
hit_access_9995_cdf = [
0.875,
0,
0,
0,
0,
0.66666667,
0.5,
0.5,
0.06451613,
0.5,
0,
0,
0,
0,
0,
0,
0,
0.5,
0.5,
0,
0.94444444,
0.93333333,
0.9,
0.9375,
0.95,
0.89473684,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.66666667,
0.75,
0.51795977,
0.5,
0.95833333,
0.96428571,
0.98,
0,
0.81818182,
0.80533333,
0.6,
0.5,
0.75,
0.9375,
]
def cal_ipc_speedup(line: pd.Series) -> pd.Series:
keys = ["opt", "srrip", "hwc 80 50 curr hotter", "hwc 80 50 curr hotter lru"]
result = []
for key in keys:
result.append(100 * (line[key] - line["lru"]) / line["lru"])
return pd.Series([line["Trace"]] + result, index=(["Trace"] + keys))
@click.command()
@click.option(
"--input-dir",
default=Path("/mnt/storage/isaachyw/champsim_pt/simulation_result"),
type=pathlib.Path,
)
@click.option(
"--output-dir",
default=Path("/mnt/storage/isaachyw/champsim_pt/simulation_result_ipc_summary"),
type=pathlib.Path,
)
@click.option("--pt", default=False, type=bool)
@click.option("--pt-ignore-outlier", default=False, type=bool)
def main(input_dir: Path, output_dir: Path, pt: bool, pt_ignore_outlier: bool):
base_dir = (
input_dir / "pt_result_analysis"
if pt
else input_dir / "ipc1_correct_new_btb_result_analysis"
)
# base_dir = Path("/Users/isaachywong/Desktop/UM/SURE/Code/ChampSim-pt/ipc1_correct_new_btb_result_analysis/")
mean_columns = [
"num_ways",
"opt",
"srrip",
"hwc 80 50 curr hotter",
"hwc 80 50 curr hotter lru",
]
mean_df = pd.DataFrame(columns=mean_columns)
for total_ways in [4, 8, 16, 32, 64, 128]:
# The commented part is mean to generate ipc speedup csv.
# If the experiment is not rerun, there is no need to re run this part.
# index_col = None
# cols = []
# for name in name_map.keys():
# # for file in sorted(base_dir.glob("*.csv")):
# file = base_dir / ("%s%s.csv" % (name, total_ways))
# print(file)
# df = pd.read_csv(file, header=0)
# if index_col is None:
# index_col = df[["Trace"]].copy()
# # new_df = df[["IPC"]].copy().rename(columns={"IPC": file.stem})
# new_df = df[["IPC"]].copy().rename(columns={"IPC": name_map[name]})
# cols.append(new_df)
# result_df = pd.concat([index_col, *cols], axis=1)
# new_df = result_df.apply(cal_ipc_speedup, axis=1)
# output_dir.mkdir(exist_ok=True)
# result_path = output_dir / ("pt_way%s.csv" % total_ways) if pt else output_dir / ("ipc1_way%s.csv" % total_ways)
# result_df.to_csv(result_path, index=False)
ipc_speedup_result_path = (
output_dir / ("ipc_speedup_pt_way%s.csv" % total_ways)
if pt
else output_dir / ("ipc_speedup_ipc1_way%s.csv" % total_ways)
)
# new_df.to_csv(ipc_speedup_result_path, index=False)
new_df = pd.read_csv(ipc_speedup_result_path)
print(new_df)
if pt and pt_ignore_outlier:
new_df = new_df.drop([7])
print(new_df)
hit_access_df = pd.DataFrame(hit_access_9995_cdf, columns=["99.95"])
if not pt:
new_df = pd.concat([new_df, hit_access_df], axis=1)
new_df = new_df.sort_values(by=["99.95", "Trace"])
# print(new_df)
plt.figure(figsize=(20, 12))
ax = plt.gca()
new_df.iloc[0:24].plot(
x="Trace",
y=["opt", "srrip", "hwc 80 50 curr hotter", "hwc 80 50 curr hotter lru"],
xlabel="",
ylabel="IPC speedup (%)",
kind="bar",
ax=ax,
width=0.4,
)
ax.set_axisbelow(True)
plt.grid(axis="y")
ax.spines["bottom"].set_position("zero")
for pos in ["top", "right", "left"]:
ax.spines[pos].set_visible(False)
plt.xticks(rotation=45)
if pt:
if pt_ignore_outlier:
filename = "plot_pt_ignore_outlier_way%s.pdf" % total_ways
else:
filename = "plot_pt_way%s.pdf" % total_ways
else:
filename = "plot_ipc1_way%s.pdf" % total_ways
plot_result_path = output_dir / filename
plt.savefig(str(plot_result_path))
plt.close()
mean_line = list(new_df.iloc[0:24].mean(axis=0))
if not pt:
mean_line = mean_line[:-1]
mean_line = [total_ways] + mean_line
print(list(mean_line))
mean_df = mean_df.append(pd.DataFrame([mean_line], columns=mean_columns))
plt.figure(figsize=(10, 6))
ax = plt.gca()
mean_df.plot(
x="num_ways",
y=mean_columns[1:],
xlabel="Num of ways",
ylabel="IPC speedup (%)",
kind="bar",
ax=ax,
width=0.4,
)
ax.set_axisbelow(True)
plt.grid(axis="y")
ax.spines["bottom"].set_position("zero")
for pos in ["top", "right", "left"]:
ax.spines[pos].set_visible(False)
if pt:
if pt_ignore_outlier:
filename = "plot_pt_mean_ignore_outlier.pdf"
else:
filename = "plot_pt_mean.pdf"
else:
filename = "plot_ipc1_mean.pdf"
plot_result_path = output_dir / filename
plt.savefig(str(plot_result_path))
plt.close()
print(mean_df)
if __name__ == "__main__":
main()