notuning_wtuning.py
8.14 KB
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
217
import os, sys, argparse
import numpy
import matplotlib.pyplot as plt
import json
from pylab import *
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.patches import Rectangle
from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas
from sets import Set
from common import *
def get_energy(frontiers_data, cpu_freq, mem_freq, sample):
for point in frontiers_data["data"][sample]:
if point["cpu_freq"] == cpu_freq:
if point["mem_freq"] == mem_freq:
return point["energy"]
def get_performance(frontiers_data, cpu_freq, mem_freq, sample):
for point in frontiers_data["data"][sample]:
if point["cpu_freq"] == cpu_freq:
if point["mem_freq"] == mem_freq:
return point["performance"]
def get_aggr_energy(frontiers_data, cpu_freq, mem_freq):
for point in frontiers_data["data"]:
if point["cpu_freq"] == cpu_freq:
if point["mem_freq"] == mem_freq:
return point["energy"]
def get_aggr_performance(frontiers_data, cpu_freq, mem_freq):
for point in frontiers_data["data"]:
if point["cpu_freq"] == cpu_freq:
if point["mem_freq"] == mem_freq:
return point["performance"]
def get_no_tuning_energy_performance(args, budget, bmark):
dir_path = args.input_dir
# finding points with 3% threshold of target budget/inefficiency
thresh = 3
bmarkDirPath = os.path.join(os.path.join(dir_path, "aggr_data"), bmark)
frontiers_file = os.path.join(bmarkDirPath, "frontiers.json")
frontiers_data = json.loads(open(frontiers_file).read())
filtered_data=[]
for point in frontiers_data["data"]:
#filtering all those points which have inefficiency with error% of the budget
if point["inefficiency"] <= budget:
filtered_data.append(point)
elif (point["inefficiency"] - budget) * 100 / budget <= thresh:
filtered_data.append(point)
#finding the point with highest performance among filtered data
optimal_point = filtered_data[0]
for point in filtered_data:
if point["speedup"] > optimal_point["speedup"]:
optimal_point = point
energy = get_aggr_energy(frontiers_data, optimal_point["cpu_freq"], optimal_point["mem_freq"])
performance = get_aggr_performance(frontiers_data, optimal_point["cpu_freq"], optimal_point["mem_freq"])
energy = energy/1e6
performance = performance/1e6
return (optimal_point, energy, performance)
def energy_time(args, budget_list, cluster_thresh, perf_cost, energy_cost):
dir_path = args.input_dir
output_dir_path = args.output_dir
benchmarks, labels = get_benchmarks(args)
# benchmarks =['gobmk']
#plot constants
cpuflist = get_cpu_freq_plot_list(args)
memflist = get_mem_freq_plot_list(args)
# finding points with 3% threshold of target budget/inefficiency
thresh = 3
energy_data = [ [] for thresh in budget_list]
performance_data = [ [] for thresh in budget_list]
for bmark in benchmarks:
data = []
for budget_index, budget in enumerate(budget_list):
print "Inefficiency Budget: "+str(budget)+" Benchmark: "+bmark
aggr_opt_point, energy_notuning, performance_notuning = get_no_tuning_energy_performance(args, budget, bmark)
print "no.tuning: performance (ms): "+str(performance_notuning)+" energy(mJ): "+str(energy_notuning)
bmarkDirPath = os.path.join(os.path.join(dir_path, "per_sample_data"), bmark)
frontiers_file = os.path.join(bmarkDirPath, "per_sample_frontiers.json")
frontiers_data = json.loads(open(frontiers_file).read())
data_to_plot=[]
sample_points=[]
cpu_rectangle_points=[]
for data in frontiers_data["data"]:
filtered_data=[]
#filtering all those points which have inefficiency with error% of the budget
for point in data:
if point["inefficiency"] <= budget:
filtered_data.append(point)
elif (point["inefficiency"] - budget) * 100 / budget <= thresh:
filtered_data.append(point)
#finding the point with highest performance among filtered data
optimal_point = filtered_data[0]
for point in filtered_data:
if point["speedup"] > optimal_point["speedup"]:
optimal_point = point
local_rect_points=[]
for point in filtered_data:
if (optimal_point["speedup"] - point["speedup"]) < 0:
print "something is wrong!"
if (optimal_point["speedup"] - point["speedup"]) * 100 / optimal_point["speedup"] <= cluster_thresh:
data_to_plot.append(point)
sample_points.append(frontiers_data["data"].index(data))
local_rect_points.append(point)
cpufpoints = [cpuf for cpuf in [data_to_plot[sample]["cpu_freq"] for sample in range(len(data_to_plot))]]
memfpoints = [memf for memf in [data_to_plot[sample]["mem_freq"] for sample in range(len(data_to_plot))]]
samplepoints = sample_points
#print cpufpoints
#print memfpoints
#print samplepoints
#Total number of transitions
num_transitions = 0.0
#Available settings for (CPU, MEM)
settings_available = Set()
index = 0
current_sample = -1
length = 1
lengths = []
prev_tr_sample = 0
energy = 0
performance = 0
aggr_energy = 0
aggr_performance = 0
idx = 0
while index < len( samplepoints ):
current_sample = samplepoints[index]
#Construct the current settings
current_settings = Set()
while index < len( samplepoints ) and samplepoints[index] == current_sample:
current_settings.add((cpufpoints[index],memfpoints[index]))
index = index + 1
#Compute the common points between current and what is available
common_points = current_settings.intersection(settings_available)
if (len(common_points) == 0 or index == len( samplepoints )):
# find the point with highest cpu and mem freq
if (current_sample !=0 ):
optimal_point = settings_available.pop()
settings_available.add(optimal_point)
for point in settings_available:
if point[0] > optimal_point[0]:
optimal_point = point
elif point[0] == optimal_point[0]:
if point[1] > optimal_point[1]:
optimal_point = point
done = 0
while done == 0 or idx == len(samplepoints) -1:
energy += get_energy(frontiers_data,optimal_point[0], optimal_point[1], idx)
performance += get_performance(frontiers_data,optimal_point[0], optimal_point[1], idx)
aggr_energy += get_energy(frontiers_data, aggr_opt_point["cpu_freq"], aggr_opt_point["mem_freq"], idx)
aggr_performance += get_performance(frontiers_data, aggr_opt_point["cpu_freq"], aggr_opt_point["mem_freq"], idx)
if aggr_energy < energy:
print "unexpected!"
# print "("+str(optimal_point[0])+", "+str(optimal_point[1])+", "+str(energy)+", "+str(performance)+") "+"("+str(aggr_opt_point["cpu_freq"])+", "+str(aggr_opt_point["mem_freq"])+", "+str(aggr_energy)+", "+str(aggr_performance)+") "
if idx == samplepoints[index-2]:
done = 1
idx += 1
prev_tr_sample = samplepoints[index-2]
# When there are no common points, transition
if (len(common_points) == 0):
settings_available = current_settings #all current settings are now available
#Ignore the transition if it's the first sample
if (current_sample!=0):
num_transitions += 1
else: # Continue with the common points
settings_available = common_points
# Record the length if we need to transition or we are at the end
if (len(common_points) == 0 or index == len( samplepoints )):
#Ignore the length if the transition is for the first sample
if (current_sample!=0):
lengths.append(length)
#reset the length
length = 1
else: #otherwise, just increment the length
length += 1
energy = (energy + (num_transitions * energy_cost))/1e6
performance = (performance +(num_transitions * perf_cost))/1e6
energy_data[budget_index].append(energy)
performance_data[budget_index].append(performance)
aggr_energy = (aggr_energy + (num_transitions * energy_cost))/1e6
aggr_performance = (aggr_performance +(num_transitions * perf_cost))/1e6
print "no.tuning(cum): performance (ms): "+str(aggr_performance)+"energy(mJ): "+str(aggr_energy)
print "w.tuning: performance (ms): "+str(performance)+" energy(mJ): "+str(energy) +"\n"
def main(argv):
args = parse(argv)
for thresh in [0.0]:
energy_time(args, [1.0], thresh, 0, 0)
# energy_time(args, [1.0,1.1, 1.2, 1.3,1.6,3.0], thresh, 0, 0)
if __name__ == "__main__":
main(sys.argv)