The Run class allows you to inspect the data for the run. Its main purpose is to parse the trace information into pandas DataFrames. Each tracepoint gets its own DataFrame.
First some hacks to allow importing trappy from here
In [1]:
import sys
sys.path.append("..")
In [2]:
%pylab inline
import trappy
Populating the interactive namespace from numpy and matplotlib
The Run class has one mandatory parameter: the path to trace.dat or trace.txt.
In [3]:
run = trappy.Run("./trace.txt")
You can access the DataFrame for each tracepoint using the .data_frame property. For example, the trace_thermal tracepoint is in the .thermal property of Run
In [4]:
run.thermal.data_frame
Out[4]:
__comm
__cpu
__pid
id
temp
temp_prev
thermal_zone
Time
0.000000
kworker/4:1
4
51
0
68786
68794
soc_thermal
0.099539
kworker/4:1
4
51
0
68781
68786
soc_thermal
0.199957
kworker/4:1
4
51
0
68782
68781
soc_thermal
0.299112
kworker/4:1
4
51
0
68835
68782
soc_thermal
0.399646
kworker/4:1
4
51
0
68882
68835
soc_thermal
0.499651
kworker/4:1
4
51
0
68907
68882
soc_thermal
0.609645
kworker/4:1
4
51
0
68943
68907
soc_thermal
0.708361
kworker/4:1
4
51
0
68959
68943
soc_thermal
0.810043
kworker/4:1
4
51
0
68947
68959
soc_thermal
0.909460
kworker/4:1
4
51
0
68950
68947
soc_thermal
1.008723
kworker/4:1
4
51
0
68952
68950
soc_thermal
1.108719
kworker/4:1
4
51
0
68965
68952
soc_thermal
1.209270
kworker/4:1
4
51
0
68978
68965
soc_thermal
1.309819
kworker/4:1
4
51
0
68989
68978
soc_thermal
1.408818
kworker/4:1
4
51
0
69007
68989
soc_thermal
1.509182
kworker/4:1
4
51
0
69008
69007
soc_thermal
1.619889
kworker/4:1
4
51
0
68996
69008
soc_thermal
1.718734
kworker/4:1
4
51
0
68990
68996
soc_thermal
1.819079
kworker/4:1
4
51
0
69007
68990
soc_thermal
1.918692
kworker/4:1
4
51
0
69026
69007
soc_thermal
2.018747
kworker/4:1
4
51
0
69074
69026
soc_thermal
2.118722
kworker/4:1
4
51
0
69113
69074
soc_thermal
2.219281
kworker/4:1
4
51
0
69131
69113
soc_thermal
2.319764
kworker/4:1
4
51
0
69161
69131
soc_thermal
2.419244
kworker/4:1
4
51
0
69155
69161
soc_thermal
2.519402
kworker/4:1
4
51
0
69203
69155
soc_thermal
2.618870
kworker/4:1
4
51
0
69229
69203
soc_thermal
2.718664
kworker/4:1
4
51
0
69269
69229
soc_thermal
2.818393
kworker/4:1
4
51
0
69294
69269
soc_thermal
2.919531
kworker/4:1
4
51
0
69313
69294
soc_thermal
...
...
...
...
...
...
...
...
3.719973
kworker/4:1
4
51
0
69498
69480
soc_thermal
3.818846
kworker/4:1
4
51
0
69513
69498
soc_thermal
3.919578
kworker/4:1
4
51
0
69556
69513
soc_thermal
4.018333
kworker/4:1
4
51
0
69607
69556
soc_thermal
4.118291
kworker/4:1
4
51
0
69647
69607
soc_thermal
4.219773
kworker/4:1
4
51
0
69657
69647
soc_thermal
4.318951
kworker/4:1
4
51
0
69663
69657
soc_thermal
4.419577
kworker/4:1
4
51
0
69685
69663
soc_thermal
4.519842
kworker/4:1
4
51
0
69710
69685
soc_thermal
4.618309
kworker/4:1
4
51
0
69733
69710
soc_thermal
4.719834
kworker/4:1
4
51
0
69714
69733
soc_thermal
4.818769
kworker/4:1
4
51
0
69700
69714
soc_thermal
4.920188
kworker/4:1
4
51
0
69667
69700
soc_thermal
5.022114
kworker/4:1
4
51
0
69621
69667
soc_thermal
5.119599
kworker/4:1
4
51
0
69577
69621
soc_thermal
5.218946
kworker/4:1
4
51
0
69546
69577
soc_thermal
5.319060
kworker/4:1
4
51
0
69530
69546
soc_thermal
5.418773
kworker/4:1
4
51
0
69509
69530
soc_thermal
5.528891
kworker/4:1
4
51
0
69523
69509
soc_thermal
5.628737
kworker/4:1
4
51
0
69515
69523
soc_thermal
5.728319
kworker/4:1
4
51
0
69489
69515
soc_thermal
5.829462
kworker/4:1
4
51
0
69469
69489
soc_thermal
5.928346
kworker/4:1
4
51
0
69478
69469
soc_thermal
6.029079
kworker/4:1
4
51
0
69453
69478
soc_thermal
6.139673
kworker/4:1
4
51
0
69442
69453
soc_thermal
6.248760
kworker/4:1
4
51
0
69427
69442
soc_thermal
6.349737
kworker/4:1
4
51
0
69406
69427
soc_thermal
6.458919
kworker/4:1
4
51
0
69424
69406
soc_thermal
6.559456
kworker/4:1
4
51
0
69418
69424
soc_thermal
6.658977
kworker/4:1
4
51
0
69417
69418
soc_thermal
67 rows × 7 columns
You can use pandas' infrastructure to filter data. For example, the cpu_in_power trace has information for both clusters
In [5]:
run.cpu_in_power.data_frame.head()
Out[5]:
__comm
__cpu
__pid
cpus
dynamic_power
freq
load0
load1
load2
load3
static_power
Time
0.002264
kworker/4:1
4
51
00000000,00000006
250
1100000
24
19
0
0
178
0.002279
kworker/4:1
4
51
00000000,00000039
166
850000
36
49
48
7
131
0.100949
kworker/4:1
4
51
00000000,00000006
227
1100000
28
11
0
0
178
0.100970
kworker/4:1
4
51
00000000,00000039
178
850000
32
28
46
44
131
0.200914
kworker/4:1
4
51
00000000,00000006
256
1100000
20
24
0
0
178
We can filter it to return only the data for cpus "00000000,00000039":
In [6]:
run.cpu_in_power.data_frame[run.cpu_in_power.data_frame.cpus == "00000000,00000039"]
Out[6]:
__comm
__cpu
__pid
cpus
dynamic_power
freq
load0
load1
load2
load3
static_power
Time
0.002279
kworker/4:1
4
51
00000000,00000039
166
850000
36
49
48
7
131
0.100970
kworker/4:1
4
51
00000000,00000039
178
850000
32
28
46
44
131
0.200929
kworker/4:1
4
51
00000000,00000039
174
850000
28
44
42
33
131
0.300253
kworker/4:1
4
51
00000000,00000039
166
850000
52
31
49
8
131
0.401033
kworker/4:1
4
51
00000000,00000039
197
850000
38
39
34
55
131
0.500595
kworker/4:1
4
51
00000000,00000039
149
850000
46
48
4
28
131
0.610771
kworker/4:1
4
51
00000000,00000039
168
850000
42
64
20
16
131
0.709190
kworker/4:1
4
51
00000000,00000039
190
850000
80
41
23
16
131
0.811388
kworker/4:1
4
51
00000000,00000039
185
850000
56
60
10
30
131
0.910507
kworker/4:1
4
51
00000000,00000039
171
850000
29
25
43
47
131
1.009501
kworker/4:1
4
51
00000000,00000039
159
850000
33
29
52
20
131
1.109501
kworker/4:1
4
51
00000000,00000039
208
850000
38
36
93
8
131
1.210615
kworker/4:1
4
51
00000000,00000039
207
850000
38
35
27
74
131
1.311017
kworker/4:1
4
51
00000000,00000039
164
850000
22
24
68
24
131
1.409793
kworker/4:1
4
51
00000000,00000039
180
850000
48
30
7
67
137
1.510247
kworker/4:1
4
51
00000000,00000039
195
850000
92
44
11
17
137
1.621442
kworker/4:1
4
51
00000000,00000039
172
850000
25
25
63
32
131
1.719513
kworker/4:1
4
51
00000000,00000039
165
850000
69
45
18
7
131
1.820367
kworker/4:1
4
51
00000000,00000039
148
850000
41
37
40
7
137
1.919996
kworker/4:1
4
51
00000000,00000039
111
850000
42
39
6
7
137
2.020034
kworker/4:1
4
51
00000000,00000039
161
850000
44
39
8
45
137
2.119842
kworker/4:1
4
51
00000000,00000039
130
850000
31
26
6
47
137
2.220430
kworker/4:1
4
51
00000000,00000039
167
850000
61
57
17
6
137
2.321255
kworker/4:1
4
51
00000000,00000039
166
850000
40
24
57
19
137
2.420036
kworker/4:1
4
51
00000000,00000039
170
850000
57
38
38
10
137
2.520509
kworker/4:1
4
51
00000000,00000039
117
850000
40
36
17
6
137
2.620426
kworker/4:1
4
51
00000000,00000039
129
850000
56
25
22
6
137
2.719428
kworker/4:1
4
51
00000000,00000039
173
850000
44
29
66
7
137
2.819184
kworker/4:1
4
51
00000000,00000039
185
850000
42
40
62
12
137
2.921334
kworker/4:1
4
51
00000000,00000039
172
850000
26
23
30
66
137
...
...
...
...
...
...
...
...
...
...
...
...
3.721862
kworker/4:1
4
51
00000000,00000039
91
850000
33
29
9
6
137
3.820099
kworker/4:1
4
51
00000000,00000039
142
850000
54
51
9
6
137
3.920987
kworker/4:1
4
51
00000000,00000039
122
850000
34
27
8
34
137
4.019102
kworker/4:1
4
51
00000000,00000039
154
850000
38
34
19
39
137
4.119037
kworker/4:1
4
51
00000000,00000039
185
850000
36
29
79
12
137
4.221545
kworker/4:1
4
51
00000000,00000039
119
850000
25
23
32
20
137
4.320729
kworker/4:1
4
51
00000000,00000039
186
850000
30
28
64
35
137
4.421109
kworker/4:1
4
51
00000000,00000039
168
850000
52
48
11
31
137
4.521129
kworker/4:1
4
51
00000000,00000039
171
850000
28
23
10
83
137
4.619289
kworker/4:1
4
51
00000000,00000039
179
850000
29
25
54
43
137
4.721126
kworker/4:1
4
51
00000000,00000039
224
850000
45
44
84
16
137
4.820703
kworker/4:1
4
51
00000000,00000039
166
850000
26
24
7
83
137
4.921670
kworker/4:1
4
51
00000000,00000039
163
850000
44
22
7
64
137
5.022998
kworker/4:1
4
51
00000000,00000039
216
850000
46
41
84
11
137
5.120968
kworker/4:1
4
51
00000000,00000039
174
850000
49
26
37
35
137
5.220168
kworker/4:1
4
51
00000000,00000039
152
850000
25
46
8
49
137
5.320038
kworker/4:1
4
51
00000000,00000039
167
850000
78
31
28
4
137
5.419996
kworker/4:1
4
51
00000000,00000039
190
850000
47
52
17
44
137
5.530364
kworker/4:1
4
51
00000000,00000039
141
850000
34
74
7
4
137
5.629523
kworker/4:1
4
51
00000000,00000039
177
850000
34
51
5
59
137
5.729082
kworker/4:1
4
51
00000000,00000039
199
850000
40
52
26
50
137
5.830771
kworker/4:1
4
51
00000000,00000039
155
850000
26
41
31
33
137
5.929531
kworker/4:1
4
51
00000000,00000039
184
850000
34
27
31
63
137
6.030359
kworker/4:1
4
51
00000000,00000039
199
850000
61
40
40
27
137
6.141069
kworker/4:1
4
51
00000000,00000039
151
850000
43
22
38
24
137
6.251660
kworker/4:1
4
51
00000000,00000039
201
850000
40
36
55
38
137
6.351220
kworker/4:1
4
51
00000000,00000039
160
850000
34
89
6
6
137
6.460227
kworker/4:1
4
51
00000000,00000039
170
850000
48
49
20
26
137
6.560918
kworker/4:1
4
51
00000000,00000039
180
850000
64
37
42
9
137
6.659959
kworker/4:1
4
51
00000000,00000039
164
850000
26
57
20
35
137
67 rows × 11 columns
You can use the .write_csv() method of each tracepoint class to output the trace to CSV for analyzing with other programs. For example, let's write the trace_thermal_power_allocator tracepoint information (available in the .thermal_governor property of the Run instance) to file thermal_power_allocator.csv
In [7]:
run.thermal_governor.write_csv("thermal_power_allocator.csv")
Content source: derkling/trappy
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