stable_point_multi_ineff.py
10.4 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
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
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 plot_2d_stable_point_mulineff(args):
dir_path = args.input_dir
output_dir_path = args.output_dir
benchmarks, labels = get_benchmarks(args)
budgets = get_mul_ineffs()
colors=['g','b','r','y']
linestyles=['-','--',':','-']
#plot constants
cpuflist = get_cpu_freq_plot_list(args)
memflist = get_mem_freq_plot_list(args)
samplelabel = "Instructions (x 10M)"
cpuflabel = "CPU frequency(MHz)"
memflabel = "Memory frequency (MHz)"
offset = 0
rotation = 340
# finding points with 3% threshold
thresh = 3
noise_thresh = 0.5
for bmark in benchmarks:
fig = plt.figure(figsize=(6, 2.5))
canvas = FigureCanvas(fig)
fig.set_canvas(canvas)
ax = fig.add_subplot('111')
ax_cpuf = plt.subplot(2, 1, 1)
ax_memf = plt.subplot(2, 1, 2)
for budget in budgets:
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=[]
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
#filtering the simulation noise by considering all points within noise_thresh% of optimal 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"] <= noise_thresh:
local_rect_points.append(point)
# among filtered points, choosing points with highest frequencies (first CPU and then by memory)
final_point = optimal_point
min_dist = 100000000 # some random large number
for point in local_rect_points:
if point["cpu_freq"] > final_point["cpu_freq"]:
final_point = point
elif point["cpu_freq"] == final_point["cpu_freq"]:
if point["mem_freq"] > final_point["mem_freq"]:
final_point = point
prev_optimal = final_point
data_to_plot.append(final_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 for sample in range(len(data_to_plot))]
if budget < 2.0:
ax_cpuf.plot(samplepoints, cpufpoints, color=colors[budgets.index(budget)], label=budget)
ax_memf.plot(samplepoints, memfpoints, color=colors[budgets.index(budget)], label=budget)
else:
ax_cpuf.plot(samplepoints, cpufpoints, color=colors[budgets.index(budget)], label="unconstrained")
ax_memf.plot(samplepoints, memfpoints, color=colors[budgets.index(budget)], label="unconstrained")
ax_cpuf.legend(fontsize=legend_size)
ax_memf.legend(fontsize=legend_size)
plt.subplots_adjust(hspace=0.05)
ax_memf.set_xlabel(samplelabel)
ax_cpuf.set_ylabel(cpuflabel)
ax_memf.set_ylabel(memflabel)
ax_cpuf.set_xlim([0, max(samplepoints) + 5])
ax_cpuf.set_xticklabels([])
ax_cpuf.set_ylim([0, max(cpuflist)+100])
ax_memf.set_xlim([0, max(samplepoints) + 5])
ax_memf.set_ylim([0, max(memflist)+100])
# cpuf_ticks = [i + offset for i in cpuflist]
# ax_cpuf.set_yticks(cpuf_ticks)
# ax_cpuf.set_yticklabels(cpuflist)
cpuf_ticks = [i + offset for i in range(100,1001, 100)]
ax_cpuf.set_yticks(cpuf_ticks)
ax_cpuf.set_yticklabels([i for i in range(100, 1001, 100)])
instr_ticks = xrange(0, max(samplepoints), 10)
ax_cpuf.set_xticks(instr_ticks)
ax_cpuf.set_xticklabels([])
ax_memf.set_xticks(instr_ticks)
ax_memf.set_xticklabels(instr_ticks)
ax_cpuf.grid('on')
ax_memf.grid('on')
ax_cpuf.set_title(bmark)
outputf = os.path.join(os.path.join(output_dir_path, "2d_best_point_variation_mulineff"), "%s_2d_stable_point_mulineff" % (bmark))
save_plot(ax, outputf)
canvas.close()
def plot_2d_stable_point_mulineff_cpi_mpki(args):
dir_path = args.input_dir
output_dir_path = args.output_dir
benchmarks, labels = get_benchmarks(args)
benchmarks = ['gobmk']
budgets = get_mul_ineffs()
colors = [ 'r' , 'b' , 'g' , 'y' ]
linestyles = [ '-' , '--' , '-' , '-' ]
linewidths = [ 1 , 1 , 1 , 1 ]
linedashes = [ [] , [5,1] , [2,0.5,1,0.5] , [] ] # [ on_len, off_len....]
legend_size = 8
#plot constants
cpuflist = get_cpu_freq_plot_list(args)
memflist = get_mem_freq_plot_list(args)
samplelabel = "Instructions (x 10M)"
cpuflabel = "CPU"
memflabel = "Memory"
offset = 0
rotation = 340
# finding points with 3% threshold
thresh = 3
noise_thresh = 0.5
for bmark in benchmarks:
fig = plt.figure(figsize=(4, 3))
canvas = FigureCanvas(fig)
fig.set_canvas(canvas)
ax = fig.add_subplot('111')
fig.text(-0.009, 0.3, "Freq. (MHz)", va = 'center', rotation='vertical')
ax_cpuf = plt.subplot(4, 1, 3)
ax_memf = plt.subplot(4, 1, 4)
ax_cpi = plt.subplot(4, 1, 1)
ax_mpki = plt.subplot(4, 1, 2)
for budget in budgets:
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=[]
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
#filtering the simulation noise by considering all points within noise_thresh% of optimal 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"] <= noise_thresh:
local_rect_points.append(point)
# among filtered points, choosing points with highest frequencies (first CPU and then by memory)
final_point = optimal_point
min_dist = 100000000 # some random large number
for point in local_rect_points:
if point["cpu_freq"] > final_point["cpu_freq"]:
final_point = point
elif point["cpu_freq"] == final_point["cpu_freq"]:
if point["mem_freq"] > final_point["mem_freq"]:
final_point = point
prev_optimal = final_point
data_to_plot.append(final_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 for sample in range(len(data_to_plot))]
label = budget if budget < 2 else "\infty"
ax_cpuf.plot(samplepoints, cpufpoints, color=colors[budgets.index(budget)],
linestyle=linestyles[budgets.index(budget)], label=label,
linewidth=linewidths[budgets.index(budget)], dashes= linedashes[budgets.index(budget)])
ax_memf.plot(samplepoints, memfpoints, color=colors[budgets.index(budget)],
linestyle=linestyles[budgets.index(budget)], label=label,
linewidth=linewidths[budgets.index(budget)], dashes= linedashes[budgets.index(budget)])
samplepoints = [sample for sample in range(len(frontiers_data["data"]))]
cpi = get_cpi(bmark, dir_path)
ax_cpi.plot(samplepoints, cpi, color = 'k')
mpki = get_mpki(bmark, dir_path)
ax_mpki.plot(samplepoints, mpki, color = 'k')
handles, labels = ax_cpuf.get_legend_handles_labels()
ax_cpi.legend(handles, labels, fontsize=legend_size, ncol = 4, bbox_to_anchor=(0.15, 1.02, 1., .102), handlelength=2.0, handletextpad=0.1, loc=3, borderaxespad=0.,)
# ax_memf.legend(fontsize=legend_size)
plt.subplots_adjust(hspace=0.05)
ax_memf.set_xlabel(samplelabel)
ax_cpuf.set_ylabel(cpuflabel)
ax_memf.set_ylabel(memflabel)
ax_cpi.set_ylabel("CPI")
ax_mpki.set_ylabel("MPKI")
ax_cpuf.set_xlim([0, max(samplepoints) + 5])
ax_cpuf.set_xticklabels([])
ax_cpuf.set_ylim([0, max(cpuflist)+100])
ax_memf.set_xlim([0, max(samplepoints) + 5])
ax_memf.set_ylim([0, max(memflist)+100])
ax_cpi.set_xlim([0, max(samplepoints) + 5])
ax_cpi.set_xticklabels([])
ax_cpi.set_ylim([0, max(cpi) +1 ])
ax_mpki.set_xlim([0, max(samplepoints) + 5])
ax_mpki.set_xticklabels([])
ax_mpki.set_ylim([0, max(mpki) +1])
# cpuf_ticks = [i + offset for i in cpuflist]
# ax_cpuf.set_yticks(cpuf_ticks)
# ax_cpuf.set_yticklabels(cpuflist)
cpuf_ticks = [i + offset for i in range(100,1001, 200)]
ax_cpuf.set_yticks(cpuf_ticks)
ax_cpuf.set_yticklabels([i for i in range(100, 1001, 200)])
memf_ticks = [i + offset for i in range(100,801, 200)]
ax_memf.set_yticks(memf_ticks)
ax_memf.set_yticklabels([i for i in range(100, 801, 200)])
instr_ticks = xrange(0, max(samplepoints)+5, 10)
ax_cpuf.set_xticks(instr_ticks)
ax_cpuf.set_xticklabels([])
ax_memf.set_xticks(instr_ticks)
ax_memf.set_xticklabels(instr_ticks)
ax_cpi.set_xticks(instr_ticks)
ax_cpi.set_xticklabels([])
ax_mpki.set_xticks(instr_ticks)
ax_mpki.set_xticklabels([])
ax_cpuf.grid('on')
ax_memf.grid('on')
ax_cpi.grid('on')
ax_mpki.grid('on')
ax_cpuf.set_title(bmark)
ax_cpuf.set_axisbelow(True)
ax_memf.set_axisbelow(True)
fig.subplots_adjust(hspace=0)
outputf = os.path.join(os.path.join(output_dir_path, "2d_best_point_variation_mulineff"), "%s_2d_stable_point_mulineff_cpi_mpki" % (bmark))
save_plot(ax, outputf)
canvas.close()
def main(argv):
args = parse(argv)
#plot_2d_stable_point_mulineff(args)
plot_2d_stable_point_mulineff_cpi_mpki(args)
if __name__ == "__main__":
main(sys.argv)