Increase payable nominators per validator #2637

kianenigma · 2021-03-17T18:15:14Z

Related to #2418, increases the payable nominators per validator to 256. This will ensure that if and when we allow more nominators in polkadot, we won't have that many oversubscribed nominators.

I also changed Kusama for consistency, but we could keep it as-is. Kusama, has fewer nominators so less of an issue.

needs re-benchmarking.

kianenigma · 2021-03-17T18:23:26Z

/benchmark runtime polkadot pallet_staking

parity-benchapp · 2021-03-17T18:23:27Z

Finished benchmark for branch: kiz-increase-payouts

Benchmark: Benchmark Runtime Polkadot Pallet

cargo run --release --features=runtime-benchmarks -- benchmark --chain=polkadot-dev --steps=50 --repeat=20 --pallet=pallet_staking --extrinsic="*" --execution=wasm --wasm-execution=compiled --heap-pages=4096 --header=./file_header.txt --output=./runtime/polkadot/src/weights/

Results

Pallet: "pallet_staking", Extrinsic: "bond", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 76.87
µs

Reads = 5
Writes = 4
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 76.87
µs

Reads = 5
Writes = 4
Pallet: "pallet_staking", Extrinsic: "bond_extra", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 62.99
µs

Reads = 4
Writes = 2
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 62.99
µs

Reads = 4
Writes = 2
Pallet: "pallet_staking", Extrinsic: "unbond", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 57.97
µs

Reads = 5
Writes = 3
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 57.97
µs

Reads = 5
Writes = 3
Pallet: "pallet_staking", Extrinsic: "withdraw_unbonded_update", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 57.95
+ s 0.028
µs

Reads = 5 + (0 * s)
Writes = 3 + (0 * s)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
s mean µs sigma µs %
0 56.94 0.105 0.1%
2 57.75 0.22 0.3%
4 57.59 0.107 0.1%
6 58.2 0.134 0.2%
8 57.97 0.117 0.2%
10 58.11 0.148 0.2%
12 58.19 0.124 0.2%
14 58.51 0.151 0.2%
16 58.17 0.106 0.1%
18 58.02 0.128 0.2%
20 58.79 0.073 0.1%
22 58.82 0.149 0.2%
24 58.47 0.093 0.1%
26 58.77 0.119 0.2%
28 58.84 0.213 0.3%
30 59.43 0.089 0.1%
32 58.78 0.129 0.2%
34 59.16 0.106 0.1%
36 58.99 0.134 0.2%
38 58.96 0.113 0.1%
40 59.34 0.14 0.2%
42 59.12 0.137 0.2%
44 59.72 0.148 0.2%
46 59.69 0.151 0.2%
48 59.71 0.143 0.2%
50 59.33 0.115 0.1%
52 59.62 0.197 0.3%
54 59.49 0.138 0.2%
56 59.56 0.164 0.2%
58 59.45 0.145 0.2%
60 59.8 0.183 0.3%
62 59.31 0.164 0.2%
64 60 0.153 0.2%
66 60.13 0.074 0.1%
68 60.08 0.115 0.1%
70 59.67 0.141 0.2%
72 60.2 0.153 0.2%
74 60 0.161 0.2%
76 60.24 0.131 0.2%
78 60.05 0.195 0.3%
80 60.03 0.168 0.2%
82 60.37 0.153 0.2%
84 60.38 0.143 0.2%
86 60.39 0.092 0.1%
88 60.31 0.182 0.3%
90 60.35 0.1 0.1%
92 60.49 0.158 0.2%
94 59.99 0.077 0.1%
96 60.24 0.086 0.1%
98 60.45 0.064 0.1%
100 60.62 0.211 0.3%

Quality and confidence:
param error
s 0

Model:
Time ~= 57.93
+ s 0.028
µs

Reads = 5 + (0 * s)
Writes = 3 + (0 * s)
Pallet: "pallet_staking", Extrinsic: "withdraw_unbonded_kill", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 90.9
+ s 2.606
µs

Reads = 7 + (0 * s)
Writes = 8 + (1 * s)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
s mean µs sigma µs %
0 85.93 0.22 0.2%
2 94.97 0.147 0.1%
4 100.7 0.158 0.1%
6 106.1 0.215 0.2%
8 111.2 0.102 0.0%
10 116.9 0.13 0.1%
12 122.3 0.256 0.2%
14 127.7 0.194 0.1%
16 132.7 0.174 0.1%
18 138.3 0.104 0.0%
20 143.4 0.243 0.1%
22 148.5 0.154 0.1%
24 154.2 0.262 0.1%
26 159 0.185 0.1%
28 164.4 0.24 0.1%
30 169.1 0.17 0.1%
32 174.5 0.231 0.1%
34 179.8 0.295 0.1%
36 185.6 0.822 0.4%
38 189.8 0.29 0.1%
40 195.5 0.286 0.1%
42 200.7 0.214 0.1%
44 205.8 0.493 0.2%
46 210.9 0.253 0.1%
48 216.3 0.322 0.1%
50 221.1 0.329 0.1%
52 225.9 0.57 0.2%
54 231.3 0.571 0.2%
56 236.9 0.182 0.0%
58 241.1 0.316 0.1%
60 246.7 0.327 0.1%
62 252.2 0.418 0.1%
64 257.6 0.504 0.1%
66 262.1 0.56 0.2%
68 267.7 0.371 0.1%
70 272.8 0.402 0.1%
72 278.4 0.3 0.1%
74 283.6 0.576 0.2%
76 288.7 0.376 0.1%
78 293.6 0.513 0.1%
80 298.2 0.38 0.1%
82 304.5 0.679 0.2%
84 308.9 0.73 0.2%
86 315.1 0.514 0.1%
88 321 0.926 0.2%
90 324.5 0.432 0.1%
92 331.1 0.816 0.2%
94 336.4 0.584 0.1%
96 341.5 0.295 0.0%
98 346.8 0.522 0.1%
100 352.1 0.68 0.1%

Quality and confidence:
param error
s 0.001

Model:
Time ~= 90.56
+ s 2.611
µs

Reads = 7 + (0 * s)
Writes = 8 + (1 * s)
Pallet: "pallet_staking", Extrinsic: "validate", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 19.13
µs

Reads = 2
Writes = 2
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 19.13
µs

Reads = 2
Writes = 2
Pallet: "pallet_staking", Extrinsic: "kick", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 19.58
+ k 18.36
µs

Reads = 2 + (1 * k)
Writes = 0 + (1 * k)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
k mean µs sigma µs %
1 43.78 0.102 0.2%
3 81.68 0.208 0.2%
5 117.4 0.146 0.1%
7 153 0.396 0.2%
9 187.8 0.316 0.1%
11 225 0.5 0.2%
13 259 0.651 0.2%
15 294.8 0.435 0.1%
17 331.4 1.058 0.3%
19 368.9 0.38 0.1%
21 406 0.849 0.2%
23 442.3 0.615 0.1%
25 475.4 0.667 0.1%
27 512.6 1.324 0.2%
29 549.2 1.252 0.2%
31 586.9 1.591 0.2%
33 621.9 0.925 0.1%
35 657.8 0.962 0.1%
37 695.3 1.472 0.2%
39 728.4 0.879 0.1%
41 767.5 1.119 0.1%
43 800 1.579 0.1%
45 839.8 2.124 0.2%
47 874.5 1.631 0.1%
49 916.1 6.691 0.7%
51 956.5 3.881 0.4%
53 993.8 7.923 0.7%
55 1059 5.538 0.5%
57 1084 13.4 1.2%
59 1103 9.564 0.8%
61 1131 2.451 0.2%
63 1169 1.999 0.1%
65 1220 10.23 0.8%
67 1247 1.484 0.1%
69 1282 3.303 0.2%
71 1320 6.913 0.5%
73 1357 9.386 0.6%
75 1388 7.794 0.5%
77 1432 10.27 0.7%
79 1458 4.192 0.2%
81 1506 9.794 0.6%
83 1529 1.477 0.0%
85 1573 5.755 0.3%
87 1608 4.914 0.3%
89 1643 4.53 0.2%
91 1687 6.225 0.3%
93 1720 8 0.4%
95 1757 3.527 0.2%
97 1809 15.4 0.8%
99 1865 3.354 0.1%
101 1866 1.298 0.0%
103 1920 10.1 0.5%
105 1949 8.992 0.4%
107 1993 12.26 0.6%
109 2030 8.242 0.4%
111 2072 4.446 0.2%
113 2107 10.6 0.5%
115 2147 8.759 0.4%
117 2185 9.537 0.4%
119 2224 12.63 0.5%
121 2258 9.771 0.4%
123 2294 16.54 0.7%
125 2335 10.96 0.4%
127 2367 13.01 0.5%

Quality and confidence:
param error
k 0.012

Model:
Time ~= 15.69
+ k 18.45
µs

Reads = 2 + (1 * k)
Writes = 0 + (1 * k)
Pallet: "pallet_staking", Extrinsic: "nominate", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 28.27
+ n 5.955
µs

Reads = 4 + (1 * n)
Writes = 2 + (0 * n)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
n mean µs sigma µs %
1 34.11 0.155 0.4%
2 40.82 0.083 0.2%
3 46.08 0.097 0.2%
4 52.15 0.149 0.2%
5 57.92 0.107 0.1%
6 64.04 0.132 0.2%
7 69.84 0.179 0.2%
8 74.07 0.123 0.1%
9 80.96 0.123 0.1%
10 86.98 0.117 0.1%
11 95.37 0.224 0.2%
12 100.6 0.293 0.2%
13 107 0.208 0.1%
14 112.3 0.329 0.2%
15 118 0.342 0.2%
16 120.7 0.228 0.1%

Quality and confidence:
param error
n 0.019

Model:
Time ~= 28.32
+ n 5.942
µs

Reads = 4 + (1 * n)
Writes = 2 + (0 * n)
Pallet: "pallet_staking", Extrinsic: "chill", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 18.54
µs

Reads = 2
Writes = 2
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 18.54
µs

Reads = 2
Writes = 2
Pallet: "pallet_staking", Extrinsic: "set_payee", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 12.52
µs

Reads = 1
Writes = 1
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 12.52
µs

Reads = 1
Writes = 1
Pallet: "pallet_staking", Extrinsic: "set_controller", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 27.23
µs

Reads = 3
Writes = 3
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 27.23
µs

Reads = 3
Writes = 3
Pallet: "pallet_staking", Extrinsic: "set_validator_count", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 2.151
µs

Reads = 0
Writes = 1
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 2.151
µs

Reads = 0
Writes = 1
Pallet: "pallet_staking", Extrinsic: "force_no_eras", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 2.354
µs

Reads = 0
Writes = 1
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 2.354
µs

Reads = 0
Writes = 1
Pallet: "pallet_staking", Extrinsic: "force_new_era", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 2.348
µs

Reads = 0
Writes = 1
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 2.348
µs

Reads = 0
Writes = 1
Pallet: "pallet_staking", Extrinsic: "force_new_era_always", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 2.348
µs

Reads = 0
Writes = 1
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 2.348
µs

Reads = 0
Writes = 1
Pallet: "pallet_staking", Extrinsic: "set_invulnerables", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 2.41
+ v 0.036
µs

Reads = 0 + (0 * v)
Writes = 1 + (0 * v)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
v mean µs sigma µs %
0 2.36 0.016 0.6%
20 3.261 0.016 0.4%
40 4.01 0.019 0.4%
60 4.695 0.017 0.3%
80 5.48 0.013 0.2%
100 6.089 0.015 0.2%
120 6.794 0.013 0.1%
140 7.561 0.015 0.1%
160 8.308 0.025 0.3%
180 8.94 0.014 0.1%
200 9.71 0.031 0.3%
220 10.46 0.031 0.2%
240 11.15 0.028 0.2%
260 11.93 0.034 0.2%
280 12.6 0.028 0.2%
300 13.34 0.015 0.1%
320 14.08 0.019 0.1%
340 14.75 0.017 0.1%
360 15.45 0.011 0.0%
380 16.19 0.026 0.1%
400 16.97 0.021 0.1%
420 17.63 0.013 0.0%
440 18.37 0.021 0.1%
460 19.35 0.027 0.1%
480 20.03 0.015 0.0%
500 20.74 0.017 0.0%
520 21.36 0.022 0.1%
540 22.05 0.015 0.0%
560 22.78 0.019 0.0%
580 23.52 0.031 0.1%
600 24.15 0.054 0.2%
620 24.99 0.012 0.0%
640 25.87 0.014 0.0%
660 26.56 0.028 0.1%
680 27.3 0.027 0.0%
700 27.99 0.025 0.0%
720 28.76 0.016 0.0%
740 29.43 0.023 0.0%
760 30.18 0.019 0.0%
780 30.98 0.033 0.1%
800 31.71 0.029 0.0%
820 32.44 0.039 0.1%
840 33.13 0.028 0.0%
860 33.82 0.027 0.0%
880 34.56 0.025 0.0%
900 35.43 0.026 0.0%
920 36.15 0.011 0.0%
940 36.85 0.038 0.1%
960 37.56 0.027 0.0%
980 38.26 0.025 0.0%
1000 39 0.039 0.0%

Quality and confidence:
param error
v 0

Model:
Time ~= 2.405
+ v 0.037
µs

Reads = 0 + (0 * v)
Writes = 1 + (0 * v)
Pallet: "pallet_staking", Extrinsic: "force_unstake", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 60.97
+ s 2.597
µs

Reads = 4 + (0 * s)
Writes = 8 + (1 * s)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
s mean µs sigma µs %
0 55.79 0.154 0.2%
2 65.09 0.14 0.2%
4 71.12 0.121 0.1%
6 76.19 0.151 0.1%
8 81.51 0.068 0.0%
10 86.78 0.204 0.2%
12 92.1 0.199 0.2%
14 97.58 0.132 0.1%
16 102.8 0.206 0.2%
18 108.1 0.088 0.0%
20 112.9 0.202 0.1%
22 118.7 0.176 0.1%
24 123.9 0.152 0.1%
26 129.2 0.226 0.1%
28 134 0.097 0.0%
30 139.4 0.124 0.0%
32 144.1 0.176 0.1%
34 149.3 0.29 0.1%
36 154.9 0.233 0.1%
38 159.6 0.189 0.1%
40 164.9 0.161 0.0%
42 170.4 0.287 0.1%
44 175.8 0.22 0.1%
46 180.6 0.25 0.1%
48 186.3 0.245 0.1%
50 190.6 0.252 0.1%
52 195.5 0.164 0.0%
54 201 0.559 0.2%
56 206 0.207 0.1%
58 211 0.195 0.0%
60 216.3 0.299 0.1%
62 222.2 0.409 0.1%
64 227.3 0.28 0.1%
66 232.7 0.529 0.2%
68 237.6 0.367 0.1%
70 242.7 0.517 0.2%
72 247.4 0.394 0.1%
74 253.1 0.555 0.2%
76 258.1 0.396 0.1%
78 263.7 0.403 0.1%
80 268.3 0.58 0.2%
82 273.3 0.431 0.1%
84 278.2 0.388 0.1%
86 283.7 0.795 0.2%
88 289.8 0.448 0.1%
90 293.4 0.46 0.1%
92 300.5 0.37 0.1%
94 305.3 0.635 0.2%
96 310.5 0.485 0.1%
98 316.5 0.814 0.2%
100 320.8 0.488 0.1%

Quality and confidence:
param error
s 0.001

Model:
Time ~= 60.65
+ s 2.602
µs

Reads = 4 + (0 * s)
Writes = 8 + (1 * s)
Pallet: "pallet_staking", Extrinsic: "cancel_deferred_slash", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 6562
+ s 34.98
µs

Reads = 1 + (0 * s)
Writes = 1 + (0 * s)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
s mean µs sigma µs %
1 439.7 0.538 0.1%
20 1733 6.87 0.3%
39 3007 11.71 0.3%
58 4261 2.633 0.0%
77 5479 9.68 0.1%
96 6685 9.59 0.1%
115 7852 17.75 0.2%
134 9013 11.58 0.1%
153 10120 11.76 0.1%
172 11210 11.14 0.0%
191 12280 23.03 0.1%
210 13340 11.3 0.0%
229 14360 12.64 0.0%
248 15360 14.09 0.0%
267 16320 18.45 0.1%
286 17290 19.83 0.1%
305 18190 30.64 0.1%
324 19090 18 0.0%
343 19960 17.38 0.0%
362 20840 26.62 0.1%
381 21620 18.45 0.0%
400 22450 14.36 0.0%
419 23210 21.17 0.0%
438 23960 17.25 0.0%
457 24670 16.1 0.0%
476 25390 18.14 0.0%
495 26060 24.83 0.0%
514 26710 27.28 0.1%
533 27330 26.37 0.0%
552 27920 33.97 0.1%
571 28500 36.05 0.1%
590 29500 20.64 0.0%
609 29550 14.4 0.0%
628 30040 19.64 0.0%
647 30520 23.06 0.0%
666 30980 17.16 0.0%
685 31400 22.18 0.0%
704 31800 28.8 0.0%
723 32180 31.98 0.0%
742 32510 26.82 0.0%
761 32830 8.615 0.0%
780 33140 21.05 0.0%
799 33400 27.21 0.0%
818 33660 34.45 0.1%
837 34180 260.3 0.7%
856 34110 29.64 0.0%
875 34270 23.38 0.0%
894 34410 11.47 0.0%
913 34550 30.92 0.0%
932 34640 17.06 0.0%
951 34720 18.39 0.0%
970 34760 17.84 0.0%
989 34810 15.29 0.0%

Quality and confidence:
param error
s 0.391

Model:
Time ~= 5924
+ s 34.79
µs

Reads = 1 + (0 * s)
Writes = 1 + (0 * s)
Pallet: "pallet_staking", Extrinsic: "payout_stakers_dead_controller", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 128
+ n 50.56
µs

Reads = 11 + (3 * n)
Writes = 2 + (1 * n)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
n mean µs sigma µs %
1 163.5 0.55 0.3%
6 420.9 1.24 0.2%
11 681.3 2.642 0.3%
16 937.9 4.571 0.4%
21 1190 9.911 0.8%
26 1434 7.464 0.5%
31 1678 3.064 0.1%
36 1955 8.984 0.4%
41 2183 4.076 0.1%
46 2448 13.22 0.5%
51 2714 10.9 0.4%
56 2960 9.857 0.3%
61 3225 8.761 0.2%
66 3484 8.234 0.2%
71 3719 10.56 0.2%
76 4004 8.315 0.2%
81 4221 6.825 0.1%
86 4491 9.676 0.2%
91 4754 9.855 0.2%
96 4976 8.343 0.1%
101 5250 13.71 0.2%
106 5553 48.94 0.8%
111 5763 13.45 0.2%
116 5972 9.162 0.1%
121 6236 10.39 0.1%
126 6495 10.69 0.1%
131 6741 11.9 0.1%
136 6968 12.74 0.1%
141 7243 18.03 0.2%
146 7517 17.57 0.2%
151 7780 12.08 0.1%
156 8133 44.52 0.5%
161 8275 18.25 0.2%
166 8519 17.16 0.2%
171 8742 14.36 0.1%
176 9029 12.7 0.1%
181 9326 16.81 0.1%
186 9520 7.937 0.0%
191 9812 12.13 0.1%
196 10060 10.6 0.1%
201 10330 14.96 0.1%
206 10530 8.471 0.0%
211 10800 12.39 0.1%
216 11060 23.89 0.2%
221 11300 15.71 0.1%
226 11610 15.52 0.1%
231 11770 24.2 0.2%
236 12000 11.27 0.0%
241 12270 16.71 0.1%
246 12590 20.69 0.1%
251 12780 22.37 0.1%
256 13050 28.49 0.2%

Quality and confidence:
param error
n 0.019

Model:
Time ~= 132.3
+ n 50.56
µs

Reads = 11 + (3 * n)
Writes = 2 + (1 * n)
Pallet: "pallet_staking", Extrinsic: "payout_stakers_alive_staked", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 148
+ n 65.97
µs

Reads = 12 + (5 * n)
Writes = 3 + (3 * n)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
n mean µs sigma µs %
1 207.4 1.269 0.6%
6 535.9 1.136 0.2%
11 872.1 4.768 0.5%
16 1208 3.373 0.2%
21 1543 12.51 0.8%
26 1864 14.81 0.7%
31 2186 7.548 0.3%
36 2517 9.487 0.3%
41 2859 3.276 0.1%
46 3191 19.94 0.6%
51 3522 8.216 0.2%
56 3873 21.19 0.5%
61 4164 11.4 0.2%
66 4500 7.224 0.1%
71 4810 10.11 0.2%
76 5152 10.33 0.2%
81 5468 6.476 0.1%
86 5822 14.05 0.2%
91 6186 16.02 0.2%
96 6496 14.88 0.2%
101 6834 13.4 0.1%
106 7151 14.5 0.2%
111 7518 19.13 0.2%
116 7780 11.11 0.1%
121 8087 10.17 0.1%
126 8426 13.78 0.1%
131 8746 18.03 0.2%
136 9111 10.78 0.1%
141 9470 12.61 0.1%
146 9746 16.52 0.1%
151 10110 18.41 0.1%
156 10400 9.813 0.0%
161 10750 17.38 0.1%
166 11090 45 0.4%
171 11460 18.22 0.1%
176 11790 28.64 0.2%
181 12120 10.92 0.0%
186 12440 15.52 0.1%
191 12790 23.62 0.1%
196 13130 15.94 0.1%
201 13470 24.86 0.1%
206 13750 21.71 0.1%
211 14080 20.52 0.1%
216 14400 23.54 0.1%
221 14750 25.06 0.1%
226 15020 26.19 0.1%
231 15360 26.1 0.1%
236 15660 27.34 0.1%
241 16040 13.68 0.0%
246 16330 23.29 0.1%
251 16650 37.94 0.2%
256 17060 20.26 0.1%

Quality and confidence:
param error
n 0.019

Model:
Time ~= 149.6
+ n 65.97
µs

Reads = 12 + (5 * n)
Writes = 3 + (3 * n)
Pallet: "pallet_staking", Extrinsic: "rebond", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 39.63
+ l 0.081
µs

Reads = 4 + (0 * l)
Writes = 3 + (0 * l)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
l mean µs sigma µs %
1 39.56 0.174 0.4%
2 39.62 0.125 0.3%
3 39.67 0.131 0.3%
4 39.63 0.03 0.0%
5 40.04 0.106 0.2%
6 40 0.098 0.2%
7 39.97 0.058 0.1%
8 39.98 0.097 0.2%
9 40.38 0.057 0.1%
10 40.55 0.066 0.1%
11 40.62 0.108 0.2%
12 40.95 0.092 0.2%
13 40.71 0.083 0.2%
14 41.05 0.105 0.2%
15 40.85 0.107 0.2%
16 40.86 0.089 0.2%
17 41.49 0.076 0.1%
18 41.5 0.094 0.2%
19 41.56 0.073 0.1%
20 41.81 0.131 0.3%
21 41.59 0.135 0.3%
22 41.61 0.099 0.2%
23 41.61 0.133 0.3%
24 41.65 0.087 0.2%
25 41.72 0.09 0.2%
26 41.71 0.124 0.2%
27 41.86 0.055 0.1%
28 41.6 0.08 0.1%
29 41.71 0.088 0.2%
30 41.6 0.094 0.2%
31 41.75 0.078 0.1%
32 41.84 0.059 0.1%

Quality and confidence:
param error
l 0.001

Model:
Time ~= 39.65
+ l 0.08
µs

Reads = 4 + (0 * l)
Writes = 3 + (0 * l)
Pallet: "pallet_staking", Extrinsic: "set_history_depth", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 0
+ e 32.59
µs

Reads = 2 + (0 * e)
Writes = 4 + (7 * e)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
e mean µs sigma µs %
1 39.4 0.231 0.5%
2 64.58 0.074 0.1%
3 88.09 0.196 0.2%
4 114.3 0.255 0.2%
5 138.3 0.178 0.1%
6 163.8 0.329 0.2%
7 189.1 1.058 0.5%
8 211.6 0.516 0.2%
9 236.3 0.448 0.1%
10 263.2 0.386 0.1%
11 288.1 0.767 0.2%
12 316.6 2.551 0.8%
13 340.8 1.002 0.2%
14 368.8 0.465 0.1%
15 395.8 0.901 0.2%
16 416.5 0.855 0.2%
17 447.3 0.505 0.1%
18 472 0.742 0.1%
19 499.8 1.258 0.2%
20 530.1 1.75 0.3%
21 557.2 1.283 0.2%
22 581.7 1.063 0.1%
23 615.3 4.128 0.6%
24 646.8 3.957 0.6%
25 664.3 0.763 0.1%
26 696.7 1.235 0.1%
27 720.6 1.217 0.1%
28 758.5 4.233 0.5%
29 783.9 2.247 0.2%
30 812.4 2.22 0.2%
31 848.5 1.833 0.2%
32 890.5 11.45 1.2%
33 917.9 8.879 0.9%
34 935.1 1.222 0.1%
35 959.4 1.833 0.1%
36 1002 2.598 0.2%
37 1033 2.256 0.2%
38 1062 3.16 0.2%
39 1084 3.278 0.3%
40 1116 1.414 0.1%
41 1147 3.346 0.2%
42 1175 4.846 0.4%
43 1219 8.623 0.7%
44 1244 3.987 0.3%
45 1273 8.492 0.6%
46 1299 4.382 0.3%
47 1347 8.685 0.6%
48 1372 1.417 0.1%
49 1401 2.196 0.1%
50 1431 4.572 0.3%
51 1471 3.013 0.2%
52 1517 3.606 0.2%
53 1556 11.88 0.7%
54 1576 3.31 0.2%
55 1601 2.973 0.1%
56 1660 7.16 0.4%
57 1682 8.248 0.4%
58 1724 10.83 0.6%
59 1751 10.29 0.5%
60 1798 8.245 0.4%
61 1805 6.86 0.3%
62 1850 8.465 0.4%
63 1865 4.491 0.2%
64 1923 9.516 0.4%
65 1952 10.11 0.5%
66 1982 11.04 0.5%
67 2016 13.91 0.6%
68 2052 5.409 0.2%
69 2091 13.02 0.6%
70 2117 7.93 0.3%
71 2170 7.639 0.3%
72 2204 9.548 0.4%
73 2244 12.48 0.5%
74 2244 12.9 0.5%
75 2306 12 0.5%
76 2353 8.27 0.3%
77 2356 10.18 0.4%
78 2391 8.99 0.3%
79 2443 3.979 0.1%
80 2464 5.653 0.2%
81 2529 6.92 0.2%
82 2574 8.352 0.3%
83 2592 10.08 0.3%
84 2648 4.913 0.1%
85 2698 9.474 0.3%
86 2731 10.91 0.3%
87 2763 7.281 0.2%
88 2773 5.289 0.1%
89 2846 9.785 0.3%
90 2898 11.87 0.4%
91 2992 8.151 0.2%
92 3029 14.53 0.4%
93 3021 28.93 0.9%
94 2996 5.824 0.1%
95 3080 4.585 0.1%
96 3087 9.489 0.3%
97 3122 7.724 0.2%
98 3174 6.158 0.1%
99 3223 7.042 0.2%
100 3287 9.878 0.3%

Quality and confidence:
param error
e 0.069

Model:
Time ~= 0
+ e 32.77
µs

Reads = 2 + (0 * e)
Writes = 4 + (7 * e)
Pallet: "pallet_staking", Extrinsic: "reap_stash", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 64.09
+ s 2.603
µs

Reads = 4 + (0 * s)
Writes = 8 + (1 * s)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
s mean µs sigma µs %
1 65.51 0.349 0.5%
2 67.94 0.157 0.2%
3 70.7 0.116 0.1%
4 74.37 0.183 0.2%
5 76.91 0.134 0.1%
6 79.38 0.071 0.0%
7 81.96 0.068 0.0%
8 84.73 0.11 0.1%
9 87.35 0.105 0.1%
10 89.88 0.096 0.1%
11 92.64 0.128 0.1%
12 95.42 0.194 0.2%
13 98.27 0.144 0.1%
14 100.5 0.131 0.1%
15 103.4 0.14 0.1%
16 105.9 0.118 0.1%
17 108.7 0.235 0.2%
18 111.1 0.155 0.1%
19 113.5 0.111 0.0%
20 116 0.163 0.1%
21 119 0.156 0.1%
22 121.4 0.131 0.1%
23 124 0.155 0.1%
24 126.9 0.164 0.1%
25 129.5 0.256 0.1%
26 132.3 0.19 0.1%
27 134.7 0.166 0.1%
28 136.9 0.24 0.1%
29 139.6 0.297 0.2%
30 143 1.042 0.7%
31 144.9 0.214 0.1%
32 147.5 0.235 0.1%
33 150.3 0.395 0.2%
34 152.5 0.128 0.0%
35 155.3 0.138 0.0%
36 158.2 0.215 0.1%
37 160.5 0.18 0.1%
38 162.8 0.369 0.2%
39 166.2 0.135 0.0%
40 168.3 0.24 0.1%
41 170.8 0.157 0.0%
42 173.4 0.259 0.1%
43 175.8 0.326 0.1%
44 178.7 0.163 0.0%
45 181.1 0.262 0.1%
46 183.6 0.264 0.1%
47 186.7 0.354 0.1%
48 189.5 0.262 0.1%
49 191.7 0.334 0.1%
50 194.2 0.91 0.4%
51 196.9 0.361 0.1%
52 198.4 0.377 0.1%
53 201.9 0.338 0.1%
54 203.9 0.262 0.1%
55 207.6 0.385 0.1%
56 209.9 0.311 0.1%
57 212.4 0.339 0.1%
58 215.2 0.405 0.1%
59 218.1 0.564 0.2%
60 219.5 0.352 0.1%
61 222.8 0.436 0.1%
62 224.9 0.505 0.2%
63 228 0.37 0.1%
64 230.2 0.491 0.2%
65 233 0.335 0.1%
66 236.1 0.351 0.1%
67 238.4 0.41 0.1%
68 241.2 0.55 0.2%
69 243.5 0.397 0.1%
70 246 0.385 0.1%
71 249.2 0.339 0.1%
72 251.2 0.418 0.1%
73 253.6 0.384 0.1%
74 256.4 0.309 0.1%
75 258.8 0.307 0.1%
76 261.3 0.453 0.1%
77 263.9 0.234 0.0%
78 266.7 0.835 0.3%
79 268.8 0.38 0.1%
80 271.8 0.584 0.2%
81 273.8 0.528 0.1%
82 277.7 0.608 0.2%
83 280 0.411 0.1%
84 281.5 0.396 0.1%
85 286 1.932 0.6%
86 287.4 0.238 0.0%
87 289.9 0.339 0.1%
88 292.4 0.369 0.1%
89 295.1 0.474 0.1%
90 297.2 0.386 0.1%
91 300.2 0.425 0.1%
92 304.2 0.594 0.1%
93 306.6 0.528 0.1%
94 309.9 0.594 0.1%
95 310.4 0.553 0.1%
96 314.9 0.665 0.2%
97 318.1 0.946 0.2%
98 320.6 0.318 0.0%
99 326.6 3.27 1.0%
100 325.7 0.612 0.1%

Quality and confidence:
param error
s 0

Model:
Time ~= 63.92
+ s 2.607
µs

Reads = 4 + (0 * s)
Writes = 8 + (1 * s)
Pallet: "pallet_staking", Extrinsic: "new_era", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 0
+ v 557.7
+ n 81.27
µs

Reads = 9 + (4 * v) + (3 * n)
Writes = 13 + (3 * v) + (0 * n)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
v n mean µs sigma µs %
1 100 3707 8.759 0.2%
2 100 4293 14.53 0.3%
3 100 4767 8.507 0.1%
4 100 5229 8.794 0.1%
5 100 5807 11.34 0.1%
6 100 6372 9.632 0.1%
7 100 6888 12.96 0.1%
8 100 7442 5.193 0.0%
9 100 8180 10.71 0.1%
10 1 700.7 0.843 0.1%
10 2 789.5 2.735 0.3%
10 3 902.1 24.36 2.7%
10 4 1012 13.26 1.3%
10 5 1095 11.42 1.0%
10 6 1175 5.701 0.4%
10 7 1253 6.878 0.5%
10 8 1325 8.775 0.6%
10 9 1396 9.323 0.6%
10 10 1490 5.12 0.3%
10 11 1562 3.8 0.2%
10 12 1642 7.794 0.4%
10 13 1728 8.863 0.5%
10 14 1812 13.02 0.7%
10 15 1893 11.88 0.6%
10 16 1977 16.66 0.8%
10 17 2058 10.78 0.5%
10 18 2117 20.24 0.9%
10 19 2193 12.06 0.5%
10 20 2274 14.21 0.6%
10 21 2358 15.06 0.6%
10 22 2447 14.9 0.6%
10 23 2524 14.65 0.5%
10 24 2597 13.44 0.5%
10 25 2682 14.82 0.5%
10 26 2774 6.147 0.2%
10 27 2866 14.1 0.4%
10 28 2955 12.71 0.4%
10 29 3041 11.77 0.3%
10 30 3089 15.1 0.4%
10 31 3154 15.11 0.4%
10 32 3254 16.36 0.5%
10 33 3334 13.57 0.4%
10 34 3415 13.99 0.4%
10 35 3504 12.86 0.3%
10 36 3592 10.18 0.2%
10 37 3669 7.868 0.2%
10 38 3747 14.36 0.3%
10 39 3836 15.01 0.3%
10 40 3917 13.96 0.3%
10 41 4008 12.59 0.3%
10 42 4087 4.07 0.0%
10 43 4160 24.16 0.5%
10 44 4245 8.868 0.2%
10 45 4349 16.88 0.3%
10 46 4391 4.227 0.0%
10 47 4488 8.375 0.1%
10 48 4551 13.44 0.2%
10 49 4632 17.34 0.3%
10 50 4718 10.17 0.2%
10 51 4798 13.81 0.2%
10 52 4877 7.708 0.1%
10 53 4953 16.35 0.3%
10 54 5016 12.2 0.2%
10 55 5138 9.721 0.1%
10 56 5205 11.85 0.2%
10 57 5305 11.08 0.2%
10 58 5366 7.41 0.1%
10 59 5458 5.092 0.0%
10 60 5554 9.606 0.1%
10 61 5623 15.84 0.2%
10 62 5694 7.017 0.1%
10 63 5792 14.2 0.2%
10 64 5868 5.931 0.1%
10 65 5931 7.794 0.1%
10 66 6040 8.65 0.1%
10 67 6135 16.53 0.2%
10 68 6177 16.63 0.2%
10 69 6260 5.226 0.0%
10 70 6372 6.675 0.1%
10 71 6434 11.35 0.1%
10 72 6524 6.272 0.0%
10 73 6595 5.472 0.0%
10 74 6682 8.95 0.1%
10 75 6767 10.93 0.1%
10 76 6840 8.067 0.1%
10 77 6900 14.1 0.2%
10 78 7013 7.436 0.1%
10 79 7111 13.3 0.1%
10 80 7167 6.242 0.0%
10 81 7253 7.558 0.1%
10 82 7392 59.04 0.7%
10 83 7443 8.788 0.1%
10 84 7491 9.732 0.1%
10 85 7578 7.77 0.1%
10 86 7679 7.314 0.0%
10 87 7743 23.1 0.2%
10 88 7826 12.29 0.1%
10 89 7919 13.7 0.1%
10 90 7988 7.303 0.0%
10 91 8070 18.49 0.2%
10 92 8120 14.02 0.1%
10 93 8208 20.65 0.2%
10 94 8276 7.277 0.0%
10 95 8392 6.885 0.0%
10 96 8460 5.725 0.0%
10 97 8560 9.875 0.1%
10 98 8737 47.48 0.5%
10 99 8671 13.14 0.1%
10 100 8747 13.67 0.1%

Quality and confidence:
param error
v 0.866
n 0.043

Model:
Time ~= 0
+ v 576.8
+ n 81.11
µs

Reads = 9 + (4 * v) + (3 * n)
Writes = 13 + (3 * v) + (0 * n)
Pallet: "pallet_staking", Extrinsic: "submit_solution_better", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 0
+ v 0.966
+ n 0.575
+ a 73.34
+ w 7.487
µs

Reads = 6 + (0 * v) + (0 * n) + (4 * a) + (1 * w)
Writes = 2 + (0 * v) + (0 * n) + (0 * a) + (0 * w)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
v n a w mean µs sigma µs %
200 1000 400 100 30200 25.23 0.0%
204 1000 400 100 30430 69.6 0.2%
208 1000 400 100 30360 65.7 0.2%
212 1000 400 100 30480 32.93 0.1%
216 1000 400 100 30260 32.46 0.1%
220 1000 400 100 30470 40.16 0.1%
224 1000 400 100 30460 41.56 0.1%
228 1000 400 100 30410 60.29 0.1%
232 1000 400 100 30360 30.22 0.0%
236 1000 400 100 30470 61.5 0.2%
240 1000 400 100 30510 35.71 0.1%
244 1000 400 100 30460 27.98 0.0%
248 1000 400 100 30620 28.58 0.0%
252 1000 400 100 30620 43.95 0.1%
256 1000 400 100 30540 45.58 0.1%
260 1000 400 100 30470 49.64 0.1%
264 1000 400 100 30300 44.76 0.1%
268 1000 400 100 30470 46.18 0.1%
272 1000 400 100 30540 48.81 0.1%
276 1000 400 100 30330 35.87 0.1%
280 1000 400 100 30450 30.76 0.1%
284 1000 400 100 30540 49.17 0.1%
288 1000 400 100 30530 61.88 0.2%
292 1000 400 100 30430 73.08 0.2%
296 1000 400 100 30420 43.96 0.1%
300 1000 400 100 30650 55.75 0.1%
304 1000 400 100 30560 43.46 0.1%
308 1000 400 100 30400 70.7 0.2%
312 1000 400 100 30520 25.65 0.0%
316 1000 400 100 30420 39.69 0.1%
320 1000 400 100 30590 31.04 0.1%
324 1000 400 100 30430 40.75 0.1%
328 1000 400 100 30480 59.81 0.1%
332 1000 400 100 30580 61.44 0.2%
336 1000 400 100 30500 22.84 0.0%
340 1000 400 100 30550 44.3 0.1%
344 1000 400 100 30530 44.38 0.1%
348 1000 400 100 30390 39.39 0.1%
352 1000 400 100 30680 54.05 0.1%
356 1000 400 100 30620 58.69 0.1%
360 1000 400 100 30550 78.22 0.2%
364 1000 400 100 30590 43.64 0.1%
368 1000 400 100 30590 39.15 0.1%
372 1000 400 100 30450 57.97 0.1%
376 1000 400 100 30640 50.76 0.1%
380 1000 400 100 30440 60.49 0.1%
384 1000 400 100 30550 49.52 0.1%
388 1000 400 100 30690 47.47 0.1%
392 1000 400 100 30590 37.15 0.1%
396 1000 400 100 30510 46.67 0.1%
400 500 400 100 30280 38.32 0.1%
400 510 400 100 30210 44.7 0.1%
400 520 400 100 30320 85.91 0.2%
400 530 400 100 30190 35.72 0.1%
400 540 400 100 30230 38.63 0.1%
400 550 400 100 30380 26.95 0.0%
400 560 400 100 30330 47.18 0.1%
400 570 400 100 30420 31.01 0.1%
400 580 400 100 30380 79.03 0.2%
400 590 400 100 30350 43.14 0.1%
400 600 400 100 30360 70.62 0.2%
400 610 400 100 30450 19.67 0.0%
400 620 400 100 30350 46.19 0.1%
400 630 400 100 30380 40.03 0.1%
400 640 400 100 30380 44.6 0.1%
400 650 400 100 30620 40.47 0.1%
400 660 400 100 30390 58.81 0.1%
400 670 400 100 30450 42.35 0.1%
400 680 400 100 30480 48.82 0.1%
400 690 400 100 30420 52.41 0.1%
400 700 400 100 30580 25.56 0.0%
400 710 400 100 30640 38.27 0.1%
400 720 400 100 30460 53.13 0.1%
400 730 400 100 30410 46.54 0.1%
400 740 400 100 30410 74.41 0.2%
400 750 400 100 30490 53.19 0.1%
400 760 400 100 30630 34.36 0.1%
400 770 400 100 30590 50.54 0.1%
400 780 400 100 30430 52.5 0.1%
400 790 400 100 30550 50.27 0.1%
400 800 400 100 30640 48.44 0.1%
400 810 400 100 30510 62.22 0.2%
400 820 400 100 30540 31.09 0.1%
400 830 400 100 30540 39.79 0.1%
400 840 400 100 30560 79.28 0.2%
400 850 400 100 30430 59.2 0.1%
400 860 400 100 30460 36.21 0.1%
400 870 400 100 30490 38.7 0.1%
400 880 400 100 30600 34.77 0.1%
400 890 400 100 30540 49.28 0.1%
400 900 400 100 30490 28.68 0.0%
400 910 400 100 30560 89.09 0.2%
400 920 400 100 30620 36.35 0.1%
400 930 400 100 30580 28.62 0.0%
400 940 400 100 30520 65.26 0.2%
400 950 400 100 30590 52.83 0.1%
400 960 400 100 30490 33.41 0.1%
400 970 400 100 30610 49.24 0.1%
400 980 400 100 30440 44.16 0.1%
400 990 400 100 30610 55.29 0.1%
400 1000 200 100 15780 40.69 0.2%
400 1000 204 100 16090 19.03 0.1%
400 1000 208 100 16370 20.61 0.1%
400 1000 212 100 16720 25.47 0.1%
400 1000 216 100 17010 23.81 0.1%
400 1000 220 100 17370 26.95 0.1%
400 1000 224 100 17680 25.01 0.1%
400 1000 228 100 17990 26.04 0.1%
400 1000 232 100 18270 28.26 0.1%
400 1000 236 100 18540 17.54 0.0%
400 1000 240 100 18830 21.99 0.1%
400 1000 244 100 19130 30.43 0.1%
400 1000 248 100 19380 55.66 0.2%
400 1000 252 100 19720 36.32 0.1%
400 1000 256 100 19980 22.21 0.1%
400 1000 260 100 20330 32.35 0.1%
400 1000 264 100 20570 47.35 0.2%
400 1000 268 100 20890 33.45 0.1%
400 1000 272 100 21210 30.67 0.1%
400 1000 276 100 21480 28.11 0.1%
400 1000 280 100 21740 52.53 0.2%
400 1000 284 100 22080 56.12 0.2%
400 1000 288 100 22300 25.44 0.1%
400 1000 292 100 22620 29.64 0.1%
400 1000 296 100 22930 68.05 0.2%
400 1000 300 100 23220 38.79 0.1%
400 1000 304 100 23510 63.77 0.2%
400 1000 308 100 23760 41.01 0.1%
400 1000 312 100 24060 38.8 0.1%
400 1000 316 100 24330 39.62 0.1%
400 1000 320 100 24700 58.25 0.2%
400 1000 324 100 24940 20.81 0.0%
400 1000 328 100 25260 31.82 0.1%
400 1000 332 100 25570 51.95 0.2%
400 1000 336 100 25840 30.57 0.1%
400 1000 340 100 26120 41.39 0.1%
400 1000 344 100 26420 47.32 0.1%
400 1000 348 100 26710 50.04 0.1%
400 1000 352 100 27050 42.87 0.1%
400 1000 356 100 27250 38.91 0.1%
400 1000 360 100 27550 40.36 0.1%
400 1000 364 100 27830 33.51 0.1%
400 1000 368 100 28230 52.18 0.1%
400 1000 372 100 28470 28.61 0.1%
400 1000 376 100 28740 65.33 0.2%
400 1000 380 100 28990 54.6 0.1%
400 1000 384 100 29320 40.15 0.1%
400 1000 388 100 29630 42.79 0.1%
400 1000 392 100 29930 35.49 0.1%
400 1000 396 100 30300 30.22 0.0%
400 1000 400 16 29970 45.55 0.1%
400 1000 400 17 29910 70.46 0.2%
400 1000 400 18 30040 39.97 0.1%
400 1000 400 19 29960 47.14 0.1%
400 1000 400 20 29970 38.64 0.1%
400 1000 400 21 30140 64.51 0.2%
400 1000 400 22 30120 49.89 0.1%
400 1000 400 23 30150 26.38 0.0%
400 1000 400 24 30120 43.5 0.1%
400 1000 400 25 30110 60.58 0.2%
400 1000 400 26 29900 42.99 0.1%
400 1000 400 27 29960 37.89 0.1%
400 1000 400 28 30140 40.17 0.1%
400 1000 400 29 30120 37.26 0.1%
400 1000 400 30 30060 72.11 0.2%
400 1000 400 31 30020 47.03 0.1%
400 1000 400 32 30020 27.86 0.0%
400 1000 400 33 30170 52.37 0.1%
400 1000 400 34 30100 25.6 0.0%
400 1000 400 35 30140 41 0.1%
400 1000 400 36 30120 48.7 0.1%
400 1000 400 37 30130 40.15 0.1%
400 1000 400 38 30160 68.29 0.2%
400 1000 400 39 30260 52.85 0.1%
400 1000 400 40 30200 58.19 0.1%
400 1000 400 41 30300 35.06 0.1%
400 1000 400 42 30150 35.18 0.1%
400 1000 400 43 30220 56.7 0.1%
400 1000 400 44 30440 26.15 0.0%
400 1000 400 45 30340 44.22 0.1%
400 1000 400 46 30330 55.38 0.1%
400 1000 400 47 30330 55.58 0.1%
400 1000 400 48 30420 46.77 0.1%
400 1000 400 49 30340 51.44 0.1%
400 1000 400 50 30260 38.23 0.1%
400 1000 400 51 30230 31.7 0.1%
400 1000 400 52 30150 32.49 0.1%
400 1000 400 53 30160 46.16 0.1%
400 1000 400 54 30290 40.85 0.1%
400 1000 400 55 30290 48.49 0.1%
400 1000 400 56 30140 40.13 0.1%
400 1000 400 57 30270 40.65 0.1%
400 1000 400 58 30140 48.56 0.1%
400 1000 400 59 30160 43.3 0.1%
400 1000 400 60 30190 45.53 0.1%
400 1000 400 61 30410 58.49 0.1%
400 1000 400 62 30260 107.6 0.3%
400 1000 400 63 30350 27.2 0.0%
400 1000 400 64 30440 57.91 0.1%
400 1000 400 65 30410 56.98 0.1%
400 1000 400 66 30300 61.86 0.2%
400 1000 400 67 30230 48.27 0.1%
400 1000 400 68 30390 44.25 0.1%
400 1000 400 69 30440 33.67 0.1%
400 1000 400 70 30420 43.31 0.1%
400 1000 400 71 30410 67.54 0.2%
400 1000 400 72 30540 53.01 0.1%
400 1000 400 73 30550 45.44 0.1%
400 1000 400 74 30490 53.71 0.1%
400 1000 400 75 30320 49.24 0.1%
400 1000 400 76 30500 43.96 0.1%
400 1000 400 77 30450 46.48 0.1%
400 1000 400 78 30470 61.88 0.2%
400 1000 400 79 30490 72.45 0.2%
400 1000 400 80 30570 46.96 0.1%
400 1000 400 81 30460 64.76 0.2%
400 1000 400 82 30590 58.69 0.1%
400 1000 400 83 30440 40.79 0.1%
400 1000 400 84 30590 21.78 0.0%
400 1000 400 85 30570 54.41 0.1%
400 1000 400 86 30570 56.44 0.1%
400 1000 400 87 30530 48.28 0.1%
400 1000 400 88 30710 40.16 0.1%
400 1000 400 89 30400 27.36 0.0%
400 1000 400 90 30550 35.51 0.1%
400 1000 400 91 30580 46.42 0.1%
400 1000 400 92 30720 67.23 0.2%
400 1000 400 93 30470 37.04 0.1%
400 1000 400 94 30710 28.65 0.0%
400 1000 400 95 30550 60.81 0.1%
400 1000 400 96 30570 55.06 0.1%
400 1000 400 97 30590 50.82 0.1%
400 1000 400 98 30620 47.57 0.1%
400 1000 400 99 30630 58.65 0.1%
400 1000 400 100 30590 34.05 0.1%

Quality and confidence:
param error
v 0.045
n 0.018
a 0.045
w 0.094

Model:
Time ~= 0
+ v 1.019
+ n 0.532
+ a 73.78
+ w 6.944
µs

Reads = 6 + (0 * v) + (0 * n) + (4 * a) + (1 * w)
Writes = 2 + (0 * v) + (0 * n) + (0 * a) + (0 * w)
Pallet: "pallet_staking", Extrinsic: "get_npos_voters", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 0
+ v 27.41
+ n 64.74
+ s 28.47
µs

Reads = 2 + (4 * v) + (3 * n) + (0 * s)
Writes = 0 + (0 * v) + (0 * n) + (0 * s)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
v n s mean µs sigma µs %
200 400 20 31390 27.49 0.0%
204 400 20 31560 50.84 0.1%
208 400 20 31520 53.67 0.1%
212 400 20 31650 42.07 0.1%
216 400 20 31770 42.28 0.1%
220 400 20 31780 72.93 0.2%
224 400 20 31970 57.39 0.1%
228 400 20 32180 61.91 0.1%
232 400 20 32320 45.49 0.1%
236 400 20 32290 51.98 0.1%
240 400 20 32260 139.3 0.4%
244 400 20 32710 49.18 0.1%
248 400 20 32330 36.36 0.1%
252 400 20 32660 60.71 0.1%
256 400 20 32950 56.9 0.1%
260 400 20 32990 41.81 0.1%
264 400 20 32860 52.94 0.1%
268 400 20 33260 78.29 0.2%
272 400 20 33180 85.14 0.2%
276 400 20 33400 93.98 0.2%
280 400 20 33470 57.06 0.1%
284 400 20 33370 61.02 0.1%
288 400 20 33880 62.14 0.1%
292 400 20 33530 62.56 0.1%
296 400 20 33940 58.59 0.1%
300 400 20 33840 73.15 0.2%
304 400 20 34350 93.33 0.2%
308 400 20 34140 96.66 0.2%
312 400 20 34150 70.84 0.2%
316 400 20 34710 64.74 0.1%
320 400 20 34390 116.8 0.3%
324 400 20 34720 86.93 0.2%
328 400 20 35110 106 0.3%
332 400 20 35090 61.08 0.1%
336 400 20 34930 77.35 0.2%
340 400 20 35070 57.29 0.1%
344 400 20 35250 89.37 0.2%
348 400 20 35110 61.25 0.1%
352 400 20 35530 83.62 0.2%
356 400 20 35790 50.79 0.1%
360 400 20 35980 102.9 0.2%
364 400 20 35410 57.1 0.1%
368 400 20 35680 69.77 0.1%
372 400 20 35830 88.37 0.2%
376 400 20 36260 71.68 0.1%
380 400 20 36460 52.95 0.1%
384 400 20 36400 56.01 0.1%
388 400 20 36630 90.51 0.2%
392 400 20 36270 46.62 0.1%
396 400 20 36700 90.8 0.2%
400 200 20 23980 37.67 0.1%
400 204 20 23880 47.41 0.1%
400 208 20 24400 70.52 0.2%
400 212 20 24350 58.39 0.2%
400 216 20 24900 55.29 0.2%
400 220 20 25380 82.87 0.3%
400 224 20 25610 49.66 0.1%
400 228 20 25440 66.13 0.2%
400 232 20 25600 42.84 0.1%
400 236 20 26240 40.87 0.1%
400 240 20 26470 44.99 0.1%
400 244 20 26970 53.83 0.1%
400 248 20 26710 40.04 0.1%
400 252 20 27200 81.05 0.2%
400 256 20 27390 59.47 0.2%
400 260 20 27790 87.64 0.3%
400 264 20 27770 74.99 0.2%
400 268 20 28000 50.16 0.1%
400 272 20 28730 68.66 0.2%
400 276 20 28960 64.06 0.2%
400 280 20 29180 44.02 0.1%
400 284 20 29320 69.07 0.2%
400 288 20 29790 54.83 0.1%
400 292 20 29960 93.49 0.3%
400 296 20 30300 88.32 0.2%
400 300 20 30270 87.52 0.2%
400 304 20 30570 33.31 0.1%
400 308 20 30670 53.25 0.1%
400 312 20 30940 66.89 0.2%
400 316 20 31570 42.51 0.1%
400 320 20 31750 62.7 0.1%
400 324 20 32170 68.87 0.2%
400 328 20 32390 74.63 0.2%
400 332 20 32650 65.52 0.2%
400 336 20 32690 66.16 0.2%
400 340 20 33070 53.87 0.1%
400 344 20 33300 78.48 0.2%
400 348 20 33220 56.49 0.1%
400 352 20 33770 93.43 0.2%
400 356 20 33780 102.7 0.3%
400 360 20 34330 52.81 0.1%
400 364 20 34200 27.74 0.0%
400 368 20 34500 68.45 0.1%
400 372 20 35080 96.6 0.2%
400 376 20 35180 71.84 0.2%
400 380 20 35500 76.81 0.2%
400 384 20 35610 89.35 0.2%
400 388 20 36010 102.4 0.2%
400 392 20 36130 55.19 0.1%
400 396 20 36540 52.23 0.1%
400 400 1 36550 84.67 0.2%
400 400 2 36450 73.72 0.2%
400 400 3 36280 78.17 0.2%
400 400 4 36210 51.47 0.1%
400 400 5 36210 76.9 0.2%
400 400 6 36750 75.04 0.2%
400 400 7 36380 94.22 0.2%
400 400 8 36370 65.23 0.1%
400 400 9 36580 46.8 0.1%
400 400 10 36320 98.42 0.2%
400 400 11 36690 87.45 0.2%
400 400 12 36830 45.3 0.1%
400 400 13 36740 43.05 0.1%
400 400 14 36700 58.42 0.1%
400 400 15 36550 110.5 0.3%
400 400 16 36460 38.94 0.1%
400 400 17 36720 42.43 0.1%
400 400 18 36880 83.29 0.2%
400 400 19 37020 73.84 0.1%
400 400 20 36850 89.96 0.2%

Quality and confidence:
param error
v 0.104
n 0.104
s 1.426

Model:
Time ~= 0
+ v 27.53
+ n 64.65
+ s 24.39
µs

Reads = 2 + (4 * v) + (3 * n) + (0 * s)
Writes = 0 + (0 * v) + (0 * n) + (0 * s)
Pallet: "pallet_staking", Extrinsic: "get_npos_targets", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 0
+ v 10.58
µs

Reads = 1 + (1 * v)
Writes = 0 + (0 * v)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
v mean µs sigma µs %
200 2034 10.59 0.5%
204 2134 8.212 0.3%
208 2158 19.73 0.9%
212 2191 13.72 0.6%
216 2218 7.762 0.3%
220 2283 20.97 0.9%
224 2346 6.915 0.2%
228 2346 13.22 0.5%
232 2383 12.55 0.5%
236 2402 12.19 0.5%
240 2468 15.44 0.6%
244 2461 10.31 0.4%
248 2603 11.32 0.4%
252 2622 10.37 0.3%
256 2638 12.21 0.4%
260 2701 22.79 0.8%
264 2714 23.42 0.8%
268 2757 14.05 0.5%
272 2842 12.58 0.4%
276 2844 14.83 0.5%
280 2857 9.975 0.3%
284 2943 19.34 0.6%
288 2961 24.17 0.8%
292 3004 17.73 0.5%
296 3087 15 0.4%
300 3125 23 0.7%
304 3193 29.31 0.9%
308 3195 11.35 0.3%
312 3261 9.118 0.2%
316 3317 26.15 0.7%
320 3355 17.3 0.5%
324 3359 24.45 0.7%
328 3400 15.02 0.4%
332 3404 11.94 0.3%
336 3482 14.54 0.4%
340 3510 9.825 0.2%
344 3587 19.87 0.5%
348 3590 14.84 0.4%
352 3686 22.58 0.6%
356 3759 12.36 0.3%
360 3713 6.095 0.1%
364 3794 20.74 0.5%
368 3827 22.86 0.5%
372 3884 22.36 0.5%
376 3912 31.72 0.8%
380 3914 21.88 0.5%
384 4018 18.14 0.4%
388 4067 19.75 0.4%
392 4082 19.41 0.4%
396 4112 27.06 0.6%
400 4236 25.01 0.5%

Quality and confidence:
param error
v 0.024

Model:
Time ~= 0
+ v 10.59
µs

Reads = 1 + (1 * v)
Writes = 0 + (0 * v)

kianenigma · 2021-03-17T18:23:34Z

/benchmark runtime kusama pallet_staking

kianenigma · 2021-03-17T18:23:44Z

/benchmark runtime westend pallet_staking

…n=polkadot-dev --steps=50 --repeat=20 --pallet=pallet_staking --extrinsic=* --execution=wasm --wasm-execution=compiled --heap-pages=4096 --header=./file_header.txt --output=./runtime/polkadot/src/weights/

parity-benchapp · 2021-03-17T19:09:14Z

Finished benchmark for branch: kiz-increase-payouts

Benchmark: Benchmark Runtime Kusama Pallet

cargo run --release --features=runtime-benchmarks -- benchmark --chain=kusama-dev --steps=50 --repeat=20 --pallet=pallet_staking --extrinsic="*" --execution=wasm --wasm-execution=compiled --heap-pages=4096 --header=./file_header.txt --output=./runtime/kusama/src/weights/

Results

Pallet: "pallet_staking", Extrinsic: "bond", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 74.69
µs

Reads = 5
Writes = 4
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 74.69
µs

Reads = 5
Writes = 4
Pallet: "pallet_staking", Extrinsic: "bond_extra", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 60.47
µs

Reads = 4
Writes = 2
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 60.47
µs

Reads = 4
Writes = 2
Pallet: "pallet_staking", Extrinsic: "unbond", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 54.94
µs

Reads = 5
Writes = 3
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 54.94
µs

Reads = 5
Writes = 3
Pallet: "pallet_staking", Extrinsic: "withdraw_unbonded_update", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 55.7
+ s 0.026
µs

Reads = 5 + (0 * s)
Writes = 3 + (0 * s)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
s mean µs sigma µs %
0 54.86 0.122 0.2%
2 55.29 0.083 0.1%
4 55.73 0.05 0.0%
6 55.68 0.117 0.2%
8 55.73 0.101 0.1%
10 55.52 0.14 0.2%
12 56.08 0.102 0.1%
14 56.09 0.143 0.2%
16 56.09 0.102 0.1%
18 56.4 0.118 0.2%
20 56.18 0.117 0.2%
22 56.62 0.093 0.1%
24 56.44 0.104 0.1%
26 56.73 0.151 0.2%
28 56.45 0.096 0.1%
30 56.68 0.152 0.2%
32 56.67 0.15 0.2%
34 56.76 0.07 0.1%
36 56.66 0.085 0.1%
38 56.82 0.129 0.2%
40 56.72 0.132 0.2%
42 56.88 0.229 0.4%
44 56.69 0.112 0.1%
46 56.79 0.147 0.2%
48 57.48 0.092 0.1%
50 57.12 0.089 0.1%
52 57.15 0.081 0.1%
54 57.03 0.161 0.2%
56 57.08 0.13 0.2%
58 57.34 0.084 0.1%
60 56.91 0.088 0.1%
62 57.33 0.131 0.2%
64 57.61 0.12 0.2%
66 58.44 0.488 0.8%
68 57.64 0.182 0.3%
70 57.61 0.093 0.1%
72 57.84 0.096 0.1%
74 57.91 0.128 0.2%
76 57.46 0.148 0.2%
78 57.79 0.103 0.1%
80 57.63 0.125 0.2%
82 57.45 0.079 0.1%
84 57.41 0.104 0.1%
86 57.83 0.139 0.2%
88 57.91 0.103 0.1%
90 57.94 0.109 0.1%
92 58.06 0.18 0.3%
94 57.99 0.093 0.1%
96 58.2 0.161 0.2%
98 58.13 0.176 0.3%
100 58.11 0.085 0.1%

Quality and confidence:
param error
s 0

Model:
Time ~= 55.68
+ s 0.026
µs

Reads = 5 + (0 * s)
Writes = 3 + (0 * s)
Pallet: "pallet_staking", Extrinsic: "withdraw_unbonded_kill", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 87.61
+ s 2.532
µs

Reads = 7 + (0 * s)
Writes = 8 + (1 * s)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
s mean µs sigma µs %
0 82.52 0.182 0.2%
2 91.79 0.185 0.2%
4 99.14 0.7 0.7%
6 102.3 0.064 0.0%
8 107.3 0.167 0.1%
10 112.5 0.237 0.2%
12 118.1 0.126 0.1%
14 123.1 0.19 0.1%
16 128.1 0.102 0.0%
18 133.2 0.193 0.1%
20 138.7 0.726 0.5%
22 143.7 0.22 0.1%
24 148.9 0.24 0.1%
26 154.6 0.337 0.2%
28 159.1 0.918 0.5%
30 163.9 0.182 0.1%
32 169.2 0.338 0.1%
34 173.8 0.377 0.2%
36 179 0.229 0.1%
38 183.7 0.211 0.1%
40 188.6 0.2 0.1%
42 194.7 0.191 0.0%
44 199.2 0.24 0.1%
46 204.5 0.313 0.1%
48 209 0.245 0.1%
50 214.2 0.219 0.1%
52 219.1 0.31 0.1%
54 224 0.299 0.1%
56 229.5 0.266 0.1%
58 233.4 0.16 0.0%
60 239 0.301 0.1%
62 244.4 0.452 0.1%
64 249.3 0.38 0.1%
66 254.7 0.538 0.2%
68 260.3 0.433 0.1%
70 264.5 0.803 0.3%
72 269.7 0.553 0.2%
74 275 0.365 0.1%
76 279.8 0.364 0.1%
78 284.2 0.337 0.1%
80 289.7 0.803 0.2%
82 294.3 0.566 0.1%
84 299.5 0.413 0.1%
86 304.6 0.55 0.1%
88 310 0.652 0.2%
90 314.1 0.496 0.1%
92 320.6 0.435 0.1%
94 326.7 0.743 0.2%
96 331.8 1.148 0.3%
98 337.2 0.534 0.1%
100 342.5 0.504 0.1%

Quality and confidence:
param error
s 0.001

Model:
Time ~= 87.33
+ s 2.537
µs

Reads = 7 + (0 * s)
Writes = 8 + (1 * s)
Pallet: "pallet_staking", Extrinsic: "validate", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 17.95
µs

Reads = 2
Writes = 2
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 17.95
µs

Reads = 2
Writes = 2
Pallet: "pallet_staking", Extrinsic: "kick", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 18.52
+ k 17.75
µs

Reads = 2 + (1 * k)
Writes = 0 + (1 * k)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
k mean µs sigma µs %
1 41.86 0.135 0.3%
3 78.29 0.139 0.1%
5 112.9 0.176 0.1%
7 147 0.26 0.1%
9 181.4 0.177 0.0%
11 215.4 0.288 0.1%
13 251.4 0.168 0.0%
15 285.2 0.563 0.1%
17 319.6 0.8 0.2%
19 357.2 0.384 0.1%
21 392.3 0.475 0.1%
23 427.4 0.941 0.2%
25 462.1 0.941 0.2%
27 497 0.542 0.1%
29 531.7 0.602 0.1%
31 566.9 1.305 0.2%
33 601.6 1.005 0.1%
35 633.5 1.222 0.1%
37 670.3 1.19 0.1%
39 706.7 2.637 0.3%
41 741.6 1.061 0.1%
43 791 12.8 1.6%
45 807.2 1.582 0.1%
47 850.2 7.279 0.8%
49 877.6 1.771 0.2%
51 920.3 2.733 0.2%
53 952.3 2.306 0.2%
55 986.8 3.004 0.3%
57 1022 1.347 0.1%
59 1062 3.064 0.2%
61 1091 1.856 0.1%
63 1136 8.275 0.7%
65 1167 2.297 0.1%
67 1207 3.238 0.2%
69 1242 2.099 0.1%
71 1275 9.181 0.7%
73 1324 15.45 1.1%
75 1346 9.968 0.7%
77 1379 4.022 0.2%
79 1423 9.532 0.6%
81 1450 7.024 0.4%
83 1490 11.71 0.7%
85 1512 1.556 0.1%
87 1569 9.974 0.6%
89 1614 12.8 0.7%
91 1625 4.662 0.2%
93 1664 12.81 0.7%
95 1702 9.219 0.5%
97 1757 14.1 0.8%
99 1770 5.771 0.3%
101 1807 4.522 0.2%
103 1846 9.288 0.5%
105 1880 9.032 0.4%
107 1925 6.386 0.3%
109 1954 9.396 0.4%
111 2007 11.23 0.5%
113 2034 7.015 0.3%
115 2070 5.426 0.2%
117 2106 8.219 0.3%
119 2147 7.453 0.3%
121 2192 13.72 0.6%
123 2232 5.269 0.2%
125 2250 11.98 0.5%
127 2286 10.21 0.4%

Quality and confidence:
param error
k 0.011

Model:
Time ~= 14.16
+ k 17.84
µs

Reads = 2 + (1 * k)
Writes = 0 + (1 * k)
Pallet: "pallet_staking", Extrinsic: "nominate", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 27.74
+ n 5.514
µs

Reads = 4 + (1 * n)
Writes = 2 + (0 * n)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
n mean µs sigma µs %
1 32.49 0.143 0.4%
2 38.9 0.048 0.1%
3 44.13 0.149 0.3%
4 49.74 0.116 0.2%
5 55.63 0.085 0.1%
6 61.31 0.262 0.4%
7 66.17 0.069 0.1%
8 70.95 0.168 0.2%
9 77.5 0.2 0.2%
10 83.34 0.241 0.2%
11 89.44 0.282 0.3%
12 94.19 0.193 0.2%
13 99.5 0.338 0.3%
14 104.6 0.205 0.1%
15 110 0.289 0.2%
16 115.1 0.355 0.3%

Quality and confidence:
param error
n 0.009

Model:
Time ~= 27.73
+ n 5.511
µs

Reads = 4 + (1 * n)
Writes = 2 + (0 * n)
Pallet: "pallet_staking", Extrinsic: "chill", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 17.43
µs

Reads = 2
Writes = 2
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 17.43
µs

Reads = 2
Writes = 2
Pallet: "pallet_staking", Extrinsic: "set_payee", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 11.88
µs

Reads = 1
Writes = 1
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 11.88
µs

Reads = 1
Writes = 1
Pallet: "pallet_staking", Extrinsic: "set_controller", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 26.01
µs

Reads = 3
Writes = 3
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 26.01
µs

Reads = 3
Writes = 3
Pallet: "pallet_staking", Extrinsic: "set_validator_count", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 2.018
µs

Reads = 0
Writes = 1
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 2.018
µs

Reads = 0
Writes = 1
Pallet: "pallet_staking", Extrinsic: "force_no_eras", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 2.321
µs

Reads = 0
Writes = 1
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 2.321
µs

Reads = 0
Writes = 1
Pallet: "pallet_staking", Extrinsic: "force_new_era", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 2.316
µs

Reads = 0
Writes = 1
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 2.316
µs

Reads = 0
Writes = 1
Pallet: "pallet_staking", Extrinsic: "force_new_era_always", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 2.29
µs

Reads = 0
Writes = 1
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 2.29
µs

Reads = 0
Writes = 1
Pallet: "pallet_staking", Extrinsic: "set_invulnerables", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 2.316
+ v 0.035
µs

Reads = 0 + (0 * v)
Writes = 1 + (0 * v)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
v mean µs sigma µs %
0 2.297 0.012 0.5%
20 3.189 0.014 0.4%
40 3.9 0.017 0.4%
60 4.6 0.015 0.3%
80 5.31 0.01 0.1%
100 5.997 0.022 0.3%
120 6.691 0.021 0.3%
140 7.398 0.013 0.1%
160 8.12 0.022 0.2%
180 8.783 0.02 0.2%
200 9.525 0.023 0.2%
220 10.21 0.035 0.3%
240 10.92 0.018 0.1%
260 11.7 0.037 0.3%
280 12.38 0.026 0.2%
300 13.06 0.017 0.1%
320 13.84 0.031 0.2%
340 14.49 0.015 0.1%
360 15.22 0.026 0.1%
380 15.9 0.019 0.1%
400 16.57 0.021 0.1%
420 17.31 0.012 0.0%
440 18.03 0.039 0.2%
460 18.74 0.024 0.1%
480 19.44 0.028 0.1%
500 20.11 0.015 0.0%
520 21.02 0.023 0.1%
540 21.68 0.026 0.1%
560 22.48 0.029 0.1%
580 23.21 0.043 0.1%
600 23.87 0.036 0.1%
620 24.54 0.016 0.0%
640 25.37 0.043 0.1%
660 26.06 0.017 0.0%
680 26.79 0.023 0.0%
700 27.49 0.03 0.1%
720 28.17 0.024 0.0%
740 28.86 0.027 0.0%
760 29.6 0.029 0.0%
780 30.44 0.025 0.0%
800 31.12 0.048 0.1%
820 31.83 0.013 0.0%
840 32.53 0.015 0.0%
860 33.26 0.024 0.0%
880 33.92 0.025 0.0%
900 34.78 0.102 0.2%
920 35.6 0.187 0.5%
940 37 0.037 0.0%
960 37.71 0.025 0.0%
980 38.37 0.022 0.0%
1000 39.01 0.226 0.5%

Quality and confidence:
param error
v 0

Model:
Time ~= 2.224
+ v 0.036
µs

Reads = 0 + (0 * v)
Writes = 1 + (0 * v)
Pallet: "pallet_staking", Extrinsic: "force_unstake", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 59.2
+ s 2.526
µs

Reads = 4 + (0 * s)
Writes = 8 + (1 * s)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
s mean µs sigma µs %
0 54.69 0.059 0.1%
2 63.41 0.062 0.0%
4 69.29 0.121 0.1%
6 74.2 0.11 0.1%
8 79.35 0.132 0.1%
10 84.31 0.1 0.1%
12 89.89 0.171 0.1%
14 94.9 0.111 0.1%
16 99.83 0.125 0.1%
18 104.9 0.071 0.0%
20 109.9 0.19 0.1%
22 115 0.109 0.0%
24 120.4 0.079 0.0%
26 125.4 0.223 0.1%
28 130.1 0.163 0.1%
30 135.4 0.141 0.1%
32 140.3 0.165 0.1%
34 145 0.196 0.1%
36 150.1 0.21 0.1%
38 155.1 0.262 0.1%
40 160.1 0.141 0.0%
42 165.3 0.237 0.1%
44 170.5 0.365 0.2%
46 175.6 0.236 0.1%
48 180.7 0.26 0.1%
50 184.9 0.201 0.1%
52 190.2 0.249 0.1%
54 195.3 0.335 0.1%
56 200.2 0.298 0.1%
58 204.8 0.477 0.2%
60 210 1.049 0.4%
62 215.7 0.453 0.2%
64 220.5 0.506 0.2%
66 225.9 0.649 0.2%
68 230.8 0.463 0.2%
70 235.4 0.446 0.1%
72 240.4 0.474 0.1%
74 246.1 0.392 0.1%
76 251 0.574 0.2%
78 255.5 0.501 0.1%
80 260.4 0.545 0.2%
82 265.4 0.262 0.0%
84 270.7 0.474 0.1%
86 276.2 0.806 0.2%
88 280.9 0.327 0.1%
90 285.2 0.742 0.2%
92 292.7 0.891 0.3%
94 296.8 0.376 0.1%
96 302.6 0.953 0.3%
98 309.8 0.789 0.2%
100 314.9 0.492 0.1%

Quality and confidence:
param error
s 0.001

Model:
Time ~= 58.75
+ s 2.534
µs

Reads = 4 + (0 * s)
Writes = 8 + (1 * s)
Pallet: "pallet_staking", Extrinsic: "cancel_deferred_slash", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 6734
+ s 34.7
µs

Reads = 1 + (0 * s)
Writes = 1 + (0 * s)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
s mean µs sigma µs %
1 437.4 0.738 0.1%
20 1736 12.6 0.7%
39 3002 12.42 0.4%
58 4265 10.79 0.2%
77 5485 13.56 0.2%
96 6666 10.21 0.1%
115 7865 19.8 0.2%
134 8983 12.53 0.1%
153 10110 13.92 0.1%
172 11220 14.53 0.1%
191 12280 12.9 0.1%
210 13330 11.81 0.0%
229 14360 16.59 0.1%
248 15360 15.44 0.1%
267 16330 14.81 0.0%
286 17290 12.49 0.0%
305 18190 9.105 0.0%
324 19090 18.9 0.0%
343 19980 14.39 0.0%
362 20810 18.23 0.0%
381 21650 23.51 0.1%
400 22580 162 0.7%
419 23200 23.54 0.1%
438 23950 18.85 0.0%
457 24660 24.59 0.0%
476 25360 20.57 0.0%
495 26070 23.46 0.0%
514 26680 14.73 0.0%
533 27300 27.26 0.0%
552 27890 22.78 0.0%
571 28500 13.94 0.0%
590 29040 16.88 0.0%
609 29550 19.47 0.0%
628 30030 30.12 0.1%
647 30500 13.94 0.0%
666 30940 16.29 0.0%
685 31400 35.03 0.1%
704 31780 19.28 0.0%
723 32150 20.58 0.0%
742 32520 23.11 0.0%
761 32830 37.7 0.1%
780 33340 190.5 0.5%
799 33390 25.85 0.0%
818 33630 23.11 0.0%
837 33870 20.75 0.0%
856 34070 21.56 0.0%
875 34270 20.04 0.0%
894 34410 35.99 0.1%
913 34530 41.14 0.1%
932 34620 16.15 0.0%
951 34690 19.21 0.0%
970 34750 19.1 0.0%
989 34740 28.5 0.0%

Quality and confidence:
param error
s 0.391

Model:
Time ~= 5931
+ s 34.74
µs

Reads = 1 + (0 * s)
Writes = 1 + (0 * s)
Pallet: "pallet_staking", Extrinsic: "payout_stakers_dead_controller", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 128.8
+ n 49.32
µs

Reads = 11 + (3 * n)
Writes = 2 + (1 * n)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
n mean µs sigma µs %
1 161.7 1.945 1.2%
6 409.1 0.862 0.2%
11 660 2.21 0.3%
16 915.7 1.675 0.1%
21 1161 8.162 0.7%
26 1405 6.308 0.4%
31 1650 12.41 0.7%
36 1899 8.834 0.4%
41 2170 12.93 0.5%
46 2421 18.57 0.7%
51 2645 8.682 0.3%
56 2899 16.1 0.5%
61 3143 13.56 0.4%
66 3403 14.54 0.4%
71 3637 13.13 0.3%
76 3892 8.858 0.2%
81 4153 7.579 0.1%
86 4371 10.97 0.2%
91 4626 8.643 0.1%
96 4891 6.227 0.1%
101 5125 15.3 0.2%
106 5374 4.381 0.0%
111 5592 12.15 0.2%
116 5855 11.77 0.2%
121 6070 11.5 0.1%
126 6312 12.2 0.1%
131 6588 11.54 0.1%
136 6818 5.784 0.0%
141 7036 10.63 0.1%
146 7354 18.85 0.2%
151 7628 13.09 0.1%
156 7835 8.909 0.1%
161 8071 20.33 0.2%
166 8339 8.5 0.1%
171 8557 14.41 0.1%
176 8817 20.95 0.2%
181 9049 10.46 0.1%
186 9347 20.07 0.2%
191 9517 15.29 0.1%
196 9863 86.86 0.8%
201 10060 12.55 0.1%
206 10270 9.595 0.0%
211 10490 12.35 0.1%
216 10860 10.5 0.0%
221 11010 21.67 0.1%
226 11250 15.42 0.1%
231 11500 11.76 0.1%
236 11820 22.53 0.1%
241 12020 23.67 0.1%
246 12240 17.94 0.1%
251 12520 15.12 0.1%
256 12740 31.19 0.2%

Quality and confidence:
param error
n 0.018

Model:
Time ~= 129.5
+ n 49.35
µs

Reads = 11 + (3 * n)
Writes = 2 + (1 * n)
Pallet: "pallet_staking", Extrinsic: "payout_stakers_alive_staked", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 154.7
+ n 63.81
µs

Reads = 12 + (5 * n)
Writes = 3 + (3 * n)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
n mean µs sigma µs %
1 200.4 0.411 0.2%
6 523.1 1.817 0.3%
11 851.3 2.127 0.2%
16 1177 8.394 0.7%
21 1495 1.363 0.0%
26 1812 5.405 0.2%
31 2126 7.944 0.3%
36 2457 9.524 0.3%
41 2759 11.78 0.4%
46 3100 11.62 0.3%
51 3430 16.32 0.4%
56 3737 10.61 0.2%
61 4057 16.95 0.4%
66 4382 17.78 0.4%
71 4703 17.31 0.3%
76 5013 12.19 0.2%
81 5284 13.51 0.2%
86 5626 9.716 0.1%
91 5989 10.97 0.1%
96 6288 11.97 0.1%
101 6639 47.14 0.7%
106 6932 9.039 0.1%
111 7232 20.83 0.2%
116 7537 9.121 0.1%
121 7900 13.25 0.1%
126 8187 12.82 0.1%
131 8539 16.86 0.1%
136 8836 22.63 0.2%
141 9138 10.86 0.1%
146 9468 17.4 0.1%
151 9762 13.61 0.1%
156 10130 14.5 0.1%
161 10410 16.92 0.1%
166 10650 12.68 0.1%
171 11030 23.42 0.2%
176 11370 13.59 0.1%
181 11720 18.05 0.1%
186 12030 12.74 0.1%
191 12320 18.58 0.1%
196 12710 13.32 0.1%
201 13030 30.74 0.2%
206 13350 24.28 0.1%
211 13620 19.38 0.1%
216 13940 18.87 0.1%
221 14250 22.62 0.1%
226 14540 30.92 0.2%
231 14940 30.8 0.2%
236 15300 87.51 0.5%
241 15430 26.06 0.1%
246 15890 19.12 0.1%
251 16140 25.98 0.1%
256 16400 34.97 0.2%

Quality and confidence:
param error
n 0.023

Model:
Time ~= 156.5
+ n 63.81
µs

Reads = 12 + (5 * n)
Writes = 3 + (3 * n)
Pallet: "pallet_staking", Extrinsic: "rebond", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 38.24
+ l 0.086
µs

Reads = 4 + (0 * l)
Writes = 3 + (0 * l)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
l mean µs sigma µs %
1 38.23 0.228 0.5%
2 38.18 0.104 0.2%
3 38.06 0.077 0.2%
4 38.18 0.093 0.2%
5 38.58 0.053 0.1%
6 38.7 0.068 0.1%
7 38.62 0.105 0.2%
8 38.73 0.099 0.2%
9 39.27 0.087 0.2%
10 39.26 0.122 0.3%
11 39.32 0.095 0.2%
12 39.61 0.097 0.2%
13 39.6 0.096 0.2%
14 39.6 0.118 0.2%
15 39.56 0.117 0.2%
16 39.64 0.077 0.1%
17 40.14 0.08 0.1%
18 40.27 0.132 0.3%
19 40.3 0.141 0.3%
20 40.29 0.064 0.1%
21 40.43 0.101 0.2%
22 40.31 0.108 0.2%
23 40.4 0.106 0.2%
24 40.34 0.062 0.1%
25 40.34 0.077 0.1%
26 40.32 0.097 0.2%
27 40.47 0.076 0.1%
28 40.37 0.093 0.2%
29 40.56 0.106 0.2%
30 40.25 0.066 0.1%
31 40.65 0.065 0.1%
32 40.65 0.088 0.2%

Quality and confidence:
param error
l 0.001

Model:
Time ~= 38.26
+ l 0.085
µs

Reads = 4 + (0 * l)
Writes = 3 + (0 * l)
Pallet: "pallet_staking", Extrinsic: "set_history_depth", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 0
+ e 30.8
µs

Reads = 2 + (0 * e)
Writes = 4 + (7 * e)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
e mean µs sigma µs %
1 36.6 0.289 0.7%
2 60.81 0.166 0.2%
3 83.06 0.165 0.1%
4 107.5 0.218 0.2%
5 130.8 0.189 0.1%
6 152.6 0.177 0.1%
7 177 0.206 0.1%
8 200.1 0.273 0.1%
9 222.8 0.32 0.1%
10 245.8 0.435 0.1%
11 271.8 0.519 0.1%
12 295.5 0.432 0.1%
13 320.5 0.711 0.2%
14 347.9 0.617 0.1%
15 370.2 0.47 0.1%
16 393.3 1.666 0.4%
17 423 0.853 0.2%
18 448.4 1.142 0.2%
19 471.9 1.167 0.2%
20 504.8 8.201 1.6%
21 524.8 1.129 0.2%
22 548.8 1.11 0.2%
23 577.8 1.114 0.1%
24 607.7 1.331 0.2%
25 636.8 1.272 0.1%
26 663.3 1.297 0.1%
27 681.6 0.985 0.1%
28 715.1 1.225 0.1%
29 739 2.162 0.2%
30 769.4 1.261 0.1%
31 794.5 1.763 0.2%
32 835 7.709 0.9%
33 859.8 8.202 0.9%
34 884.4 9.896 1.1%
35 917.1 3.521 0.3%
36 944.2 3.246 0.3%
37 974.9 1.48 0.1%
38 996.7 1.812 0.1%
39 1020 2.121 0.2%
40 1055 3.72 0.3%
41 1084 3.441 0.3%
42 1116 3.026 0.2%
43 1142 2.155 0.1%
44 1181 3.523 0.2%
45 1207 3.959 0.3%
46 1238 2.882 0.2%
47 1261 6.186 0.4%
48 1304 2.996 0.2%
49 1318 5.799 0.4%
50 1367 9.205 0.6%
51 1396 7.553 0.5%
52 1432 9.16 0.6%
53 1467 10.1 0.6%
54 1481 9.97 0.6%
55 1525 9.343 0.6%
56 1559 9.332 0.5%
57 1569 3.696 0.2%
58 1628 9.951 0.6%
59 1658 5.784 0.3%
60 1683 10.54 0.6%
61 1712 6.084 0.3%
62 1747 8.689 0.4%
63 1778 5.655 0.3%
64 1814 11.2 0.6%
65 1830 9.04 0.4%
66 1890 9.891 0.5%
67 1901 3.864 0.2%
68 1936 11.76 0.6%
69 1950 7.238 0.3%
70 1984 8.203 0.4%
71 2036 7.219 0.3%
72 2063 8.446 0.4%
73 2110 7.829 0.3%
74 2149 11.31 0.5%
75 2179 4.307 0.1%
76 2214 12.53 0.5%
77 2252 16.09 0.7%
78 2299 13.16 0.5%
79 2324 14.55 0.6%
80 2355 14.04 0.5%
81 2391 14.9 0.6%
82 2434 17.18 0.7%
83 2465 13.41 0.5%
84 2504 13.03 0.5%
85 2542 10.73 0.4%
86 2581 16.83 0.6%
87 2613 12.87 0.4%
88 2632 16.23 0.6%
89 2705 8.223 0.3%
90 2713 14.64 0.5%
91 2762 12.64 0.4%
92 2796 12.98 0.4%
93 2821 9.681 0.3%
94 2849 8.943 0.3%
95 2887 5.548 0.1%
96 2970 13.62 0.4%
97 2936 10.73 0.3%
98 2994 4.006 0.1%
99 3044 12.88 0.4%
100 3088 18.38 0.5%

Quality and confidence:
param error
e 0.062

Model:
Time ~= 0
+ e 30.92
µs

Reads = 2 + (0 * e)
Writes = 4 + (7 * e)
Pallet: "pallet_staking", Extrinsic: "reap_stash", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 62.38
+ s 2.518
µs

Reads = 4 + (0 * s)
Writes = 8 + (1 * s)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
s mean µs sigma µs %
1 63.46 0.111 0.1%
2 66.25 0.115 0.1%
3 68.91 0.135 0.1%
4 72.24 0.068 0.0%
5 74.56 0.171 0.2%
6 77.12 0.121 0.1%
7 79.65 0.142 0.1%
8 82.29 0.134 0.1%
9 84.66 0.084 0.0%
10 87.42 0.12 0.1%
11 89.67 0.071 0.0%
12 92.44 0.131 0.1%
13 95.41 0.135 0.1%
14 97.81 0.203 0.2%
15 100.2 0.079 0.0%
16 102.4 0.128 0.1%
17 105.4 0.135 0.1%
18 108 0.136 0.1%
19 110.7 0.163 0.1%
20 113.1 0.12 0.1%
21 115.6 0.198 0.1%
22 118.3 0.163 0.1%
23 120.7 0.248 0.2%
24 123.6 0.188 0.1%
25 126 0.255 0.2%
26 128.5 0.179 0.1%
27 130.5 0.235 0.1%
28 133.2 0.132 0.0%
29 135.8 0.222 0.1%
30 138.7 1.164 0.8%
31 140.8 0.245 0.1%
32 143.1 0.2 0.1%
33 145.5 0.17 0.1%
34 148.1 0.195 0.1%
35 150.7 0.294 0.1%
36 153.1 0.237 0.1%
37 155.5 0.159 0.1%
38 158.1 0.119 0.0%
39 160.9 0.225 0.1%
40 163.2 0.219 0.1%
41 165.8 0.417 0.2%
42 168.4 0.213 0.1%
43 170.6 0.21 0.1%
44 173.4 0.227 0.1%
45 175.8 0.213 0.1%
46 178.3 0.318 0.1%
47 180.8 0.244 0.1%
48 183.5 0.231 0.1%
49 185.9 0.192 0.1%
50 188.2 0.271 0.1%
51 190.2 0.205 0.1%
52 192.6 0.3 0.1%
53 195.5 0.298 0.1%
54 197.8 0.159 0.0%
55 200.9 0.289 0.1%
56 202.8 0.283 0.1%
57 205.4 0.291 0.1%
58 208.2 0.263 0.1%
59 210.7 0.292 0.1%
60 213.2 0.475 0.2%
61 215.8 0.343 0.1%
62 218.7 0.475 0.2%
63 221.1 0.327 0.1%
64 223.5 0.501 0.2%
65 226.6 0.625 0.2%
66 228.4 0.36 0.1%
67 230.8 0.27 0.1%
68 233.5 0.314 0.1%
69 236 0.497 0.2%
70 238.3 0.546 0.2%
71 241.4 0.205 0.0%
72 243.2 0.409 0.1%
73 245.6 0.375 0.1%
74 248.5 0.282 0.1%
75 252.5 1.918 0.7%
76 253.2 0.389 0.1%
77 255.9 0.207 0.0%
78 258.1 0.487 0.1%
79 261.1 0.446 0.1%
80 263.3 0.554 0.2%
81 265.2 0.528 0.1%
82 268.9 1.436 0.5%
83 271.4 0.508 0.1%
84 273.2 0.385 0.1%
85 276 0.492 0.1%
86 278.1 0.339 0.1%
87 282.4 2.173 0.7%
88 282.9 0.435 0.1%
89 286.9 1.059 0.3%
90 288 0.448 0.1%
91 290.5 0.384 0.1%
92 295 0.607 0.2%
93 297.3 0.573 0.1%
94 299.6 0.365 0.1%
95 300.8 0.65 0.2%
96 304.8 0.573 0.1%
97 308.3 0.341 0.1%
98 311.4 0.636 0.2%
99 313.8 1.385 0.4%
100 315.6 0.527 0.1%

Quality and confidence:
param error
s 0

Model:
Time ~= 62.25
+ s 2.522
µs

Reads = 4 + (0 * s)
Writes = 8 + (1 * s)
Pallet: "pallet_staking", Extrinsic: "new_era", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 0
+ v 552.5
+ n 79.58
µs

Reads = 9 + (4 * v) + (3 * n)
Writes = 13 + (3 * v) + (0 * n)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
v n mean µs sigma µs %
1 100 3571 11.24 0.3%
2 100 4169 10.75 0.2%
3 100 4606 10.23 0.2%
4 100 5067 14.44 0.2%
5 100 5687 13.38 0.2%
6 100 6239 15.34 0.2%
7 100 6721 14.82 0.2%
8 100 7252 13.37 0.1%
9 100 7943 12.2 0.1%
10 1 682.3 0.736 0.1%
10 2 767.4 1.687 0.2%
10 3 845.6 1.297 0.1%
10 4 928.4 4.456 0.4%
10 5 1003 1.452 0.1%
10 6 1082 2.853 0.2%
10 7 1165 7.529 0.6%
10 8 1248 10.3 0.8%
10 9 1358 24 1.7%
10 10 1440 9.737 0.6%
10 11 1524 5.576 0.3%
10 12 1591 16.61 1.0%
10 13 1651 13.56 0.8%
10 14 1732 12.36 0.7%
10 15 1810 11.72 0.6%
10 16 1890 14.53 0.7%
10 17 1956 12.33 0.6%
10 18 2038 11.22 0.5%
10 19 2117 12.61 0.5%
10 20 2207 14.62 0.6%
10 21 2280 2.73 0.1%
10 22 2361 10.16 0.4%
10 23 2451 11.61 0.4%
10 24 2541 15.69 0.6%
10 25 2604 8.945 0.3%
10 26 2687 11.18 0.4%
10 27 2766 12.66 0.4%
10 28 2839 8.425 0.2%
10 29 2923 8.189 0.2%
10 30 2998 10.57 0.3%
10 31 3083 13.4 0.4%
10 32 3180 6.254 0.1%
10 33 3252 11.82 0.3%
10 34 3320 7.178 0.2%
10 35 3388 8.227 0.2%
10 36 3480 10.19 0.2%
10 37 3555 12.91 0.3%
10 38 3642 8.205 0.2%
10 39 3730 8.313 0.2%
10 40 3808 6.265 0.1%
10 41 3888 7.262 0.1%
10 42 3967 9.782 0.2%
10 43 4061 10.15 0.2%
10 44 4116 12.23 0.2%
10 45 4217 18.52 0.4%
10 46 4274 10.54 0.2%
10 47 4340 6.855 0.1%
10 48 4429 11.77 0.2%
10 49 4525 14.23 0.3%
10 50 4577 7.303 0.1%
10 51 4694 25.48 0.5%
10 52 4735 5.183 0.1%
10 53 4821 10.31 0.2%
10 54 4893 5.794 0.1%
10 55 4986 10.61 0.2%
10 56 5073 12.98 0.2%
10 57 5151 15.24 0.2%
10 58 5255 11.15 0.2%
10 59 5326 9.641 0.1%
10 60 5391 4.827 0.0%
10 61 5468 12.12 0.2%
10 62 5538 9.092 0.1%
10 63 5621 11.76 0.2%
10 64 5746 12.6 0.2%
10 65 5799 11.08 0.1%
10 66 5883 4.441 0.0%
10 67 5966 9.016 0.1%
10 68 6027 5.731 0.0%
10 69 6119 12.61 0.2%
10 70 6163 12.53 0.2%
10 71 6273 7.161 0.1%
10 72 6324 10.87 0.1%
10 73 6436 13.68 0.2%
10 74 6485 6.871 0.1%
10 75 6595 15.33 0.2%
10 76 6641 7.875 0.1%
10 77 6704 8.759 0.1%
10 78 6804 7.353 0.1%
10 79 6902 13.12 0.1%
10 80 7021 7.139 0.1%
10 81 7084 10.87 0.1%
10 82 7162 8.524 0.1%
10 83 7237 8.421 0.1%
10 84 7328 14.3 0.1%
10 85 7392 14.26 0.1%
10 86 7475 8.202 0.1%
10 87 7573 12.51 0.1%
10 88 7640 16.65 0.2%
10 89 7685 7.906 0.1%
10 90 7786 15.19 0.1%
10 91 7864 13 0.1%
10 92 7938 11.63 0.1%
10 93 8011 9.262 0.1%
10 94 8088 15.05 0.1%
10 95 8161 14.99 0.1%
10 96 8214 16.41 0.1%
10 97 8331 15.81 0.1%
10 98 8409 14.8 0.1%
10 99 8487 9.388 0.1%
10 100 8584 19.67 0.2%

Quality and confidence:
param error
v 0.755
n 0.037

Model:
Time ~= 0
+ v 565.5
+ n 79.39
µs

Reads = 9 + (4 * v) + (3 * n)
Writes = 13 + (3 * v) + (0 * n)
Pallet: "pallet_staking", Extrinsic: "submit_solution_better", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 0
+ v 0.788
+ n 0.451
+ a 70.49
+ w 7.378
µs

Reads = 6 + (0 * v) + (0 * n) + (4 * a) + (1 * w)
Writes = 2 + (0 * v) + (0 * n) + (0 * a) + (0 * w)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
v n a w mean µs sigma µs %
200 1000 400 100 29100 36.1 0.1%
204 1000 400 100 29030 33.6 0.1%
208 1000 400 100 29120 35.74 0.1%
212 1000 400 100 29150 28.46 0.0%
216 1000 400 100 29180 39.41 0.1%
220 1000 400 100 29190 37.6 0.1%
224 1000 400 100 29200 42.92 0.1%
228 1000 400 100 29260 53.48 0.1%
232 1000 400 100 29280 83.04 0.2%
236 1000 400 100 29110 66.96 0.2%
240 1000 400 100 29260 51.87 0.1%
244 1000 400 100 29190 15.39 0.0%
248 1000 400 100 29180 75.66 0.2%
252 1000 400 100 29240 24.25 0.0%
256 1000 400 100 29110 42.84 0.1%
260 1000 400 100 29160 52.6 0.1%
264 1000 400 100 29260 48.94 0.1%
268 1000 400 100 29260 19.1 0.0%
272 1000 400 100 29380 37.02 0.1%
276 1000 400 100 29300 37.8 0.1%
280 1000 400 100 29110 25.15 0.0%
284 1000 400 100 29210 44.15 0.1%
288 1000 400 100 29210 33.86 0.1%
292 1000 400 100 29160 43.19 0.1%
296 1000 400 100 29300 33.45 0.1%
300 1000 400 100 29340 39.47 0.1%
304 1000 400 100 29110 31.22 0.1%
308 1000 400 100 29370 62.03 0.2%
312 1000 400 100 29280 28.66 0.0%
316 1000 400 100 29230 39.1 0.1%
320 1000 400 100 29130 36.5 0.1%
324 1000 400 100 29310 31.12 0.1%
328 1000 400 100 29110 37.56 0.1%
332 1000 400 100 29340 30.77 0.1%
336 1000 400 100 29410 54.16 0.1%
340 1000 400 100 29200 64.75 0.2%
344 1000 400 100 29260 48.8 0.1%
348 1000 400 100 29160 74.05 0.2%
352 1000 400 100 29410 47.2 0.1%
356 1000 400 100 29320 24.1 0.0%
360 1000 400 100 29370 32.59 0.1%
364 1000 400 100 29330 33.12 0.1%
368 1000 400 100 29310 52.81 0.1%
372 1000 400 100 29180 29.91 0.1%
376 1000 400 100 29080 26.83 0.0%
380 1000 400 100 29410 44.25 0.1%
384 1000 400 100 29390 51.2 0.1%
388 1000 400 100 29250 62.28 0.2%
392 1000 400 100 29210 43.58 0.1%
396 1000 400 100 29380 33.34 0.1%
400 500 400 100 29020 37.03 0.1%
400 510 400 100 29140 37.89 0.1%
400 520 400 100 29160 48.3 0.1%
400 530 400 100 29080 32.57 0.1%
400 540 400 100 29120 35.74 0.1%
400 550 400 100 29180 45.6 0.1%
400 560 400 100 29130 43.35 0.1%
400 570 400 100 29150 40.74 0.1%
400 580 400 100 29110 41.51 0.1%
400 590 400 100 29060 56.07 0.1%
400 600 400 100 29220 27.81 0.0%
400 610 400 100 29230 40.01 0.1%
400 620 400 100 29030 62.33 0.2%
400 630 400 100 29220 72.95 0.2%
400 640 400 100 29220 47.4 0.1%
400 650 400 100 29050 36.42 0.1%
400 660 400 100 29200 42.88 0.1%
400 670 400 100 29210 29.67 0.1%
400 680 400 100 29290 50.26 0.1%
400 690 400 100 29150 41.81 0.1%
400 700 400 100 29190 69.85 0.2%
400 710 400 100 29330 50.52 0.1%
400 720 400 100 29220 33.27 0.1%
400 730 400 100 29050 38.89 0.1%
400 740 400 100 29300 96.12 0.3%
400 750 400 100 29220 27.38 0.0%
400 760 400 100 29330 72.86 0.2%
400 770 400 100 29210 28.11 0.0%
400 780 400 100 29230 63.69 0.2%
400 790 400 100 29380 30.62 0.1%
400 800 400 100 29140 61.94 0.2%
400 810 400 100 29280 47.4 0.1%
400 820 400 100 29200 76.85 0.2%
400 830 400 100 29120 49.6 0.1%
400 840 400 100 29100 49.05 0.1%
400 850 400 100 29190 48.11 0.1%
400 860 400 100 29270 52 0.1%
400 870 400 100 29230 33.64 0.1%
400 880 400 100 29300 67.65 0.2%
400 890 400 100 29260 54.52 0.1%
400 900 400 100 29400 35.71 0.1%
400 910 400 100 29390 54.5 0.1%
400 920 400 100 29270 42.63 0.1%
400 930 400 100 29200 53.48 0.1%
400 940 400 100 29180 52.48 0.1%
400 950 400 100 29320 57.69 0.1%
400 960 400 100 29440 30.4 0.1%
400 970 400 100 29380 34.76 0.1%
400 980 400 100 29320 54.29 0.1%
400 990 400 100 29390 65.37 0.2%
400 1000 200 100 15140 26.51 0.1%
400 1000 204 100 15470 27.09 0.1%
400 1000 208 100 15690 55.57 0.3%
400 1000 212 100 15970 16.05 0.1%
400 1000 216 100 16210 22 0.1%
400 1000 220 100 16530 27.52 0.1%
400 1000 224 100 16840 31.74 0.1%
400 1000 228 100 17090 31.13 0.1%
400 1000 232 100 17470 36.92 0.2%
400 1000 236 100 17820 42.88 0.2%
400 1000 240 100 18070 24.35 0.1%
400 1000 244 100 18330 54.05 0.2%
400 1000 248 100 18620 58.14 0.3%
400 1000 252 100 18910 30.66 0.1%
400 1000 256 100 19160 47.05 0.2%
400 1000 260 100 19460 44.78 0.2%
400 1000 264 100 19740 37.52 0.1%
400 1000 268 100 20050 38.85 0.1%
400 1000 272 100 20290 44.78 0.2%
400 1000 276 100 20580 34.52 0.1%
400 1000 280 100 20880 42.23 0.2%
400 1000 284 100 21180 38 0.1%
400 1000 288 100 21450 47.09 0.2%
400 1000 292 100 21710 41.18 0.1%
400 1000 296 100 21960 49.62 0.2%
400 1000 300 100 22320 49.87 0.2%
400 1000 304 100 22560 35.36 0.1%
400 1000 308 100 22780 32.92 0.1%
400 1000 312 100 23070 34.03 0.1%
400 1000 316 100 23350 32.92 0.1%
400 1000 320 100 23620 39.69 0.1%
400 1000 324 100 23930 46.19 0.1%
400 1000 328 100 24170 30.46 0.1%
400 1000 332 100 24520 46.76 0.1%
400 1000 336 100 24810 25.41 0.1%
400 1000 340 100 25020 66.6 0.2%
400 1000 344 100 25330 26.4 0.1%
400 1000 348 100 25630 42.41 0.1%
400 1000 352 100 25930 37.37 0.1%
400 1000 356 100 26220 45.48 0.1%
400 1000 360 100 26460 67.86 0.2%
400 1000 364 100 26720 36.1 0.1%
400 1000 368 100 27010 72.6 0.2%
400 1000 372 100 27310 35.29 0.1%
400 1000 376 100 27600 59.03 0.2%
400 1000 380 100 27870 46.53 0.1%
400 1000 384 100 28150 22.09 0.0%
400 1000 388 100 28390 31.54 0.1%
400 1000 392 100 28660 63.38 0.2%
400 1000 396 100 28970 57.42 0.1%
400 1000 400 16 28760 42.86 0.1%
400 1000 400 17 28640 56.14 0.1%
400 1000 400 18 28760 36.15 0.1%
400 1000 400 19 28760 33.02 0.1%
400 1000 400 20 28940 62.72 0.2%
400 1000 400 21 28620 44.66 0.1%
400 1000 400 22 28900 60.17 0.2%
400 1000 400 23 28850 50.27 0.1%
400 1000 400 24 28960 65.75 0.2%
400 1000 400 25 28780 50.66 0.1%
400 1000 400 26 28750 49.28 0.1%
400 1000 400 27 28810 70.42 0.2%
400 1000 400 28 28740 64.72 0.2%
400 1000 400 29 28670 51.6 0.1%
400 1000 400 30 28800 64.89 0.2%
400 1000 400 31 28700 40.29 0.1%
400 1000 400 32 28850 57.67 0.1%
400 1000 400 33 28980 20.68 0.0%
400 1000 400 34 28920 55.78 0.1%
400 1000 400 35 28710 34.19 0.1%
400 1000 400 36 28930 48.92 0.1%
400 1000 400 37 28890 29.56 0.1%
400 1000 400 38 28970 25.75 0.0%
400 1000 400 39 28850 46.27 0.1%
400 1000 400 40 28840 44.27 0.1%
400 1000 400 41 29000 69.07 0.2%
400 1000 400 42 28990 32.17 0.1%
400 1000 400 43 29080 36.08 0.1%
400 1000 400 44 29130 44.22 0.1%
400 1000 400 45 29140 39.93 0.1%
400 1000 400 46 29150 35.82 0.1%
400 1000 400 47 29040 50.52 0.1%
400 1000 400 48 29170 33.46 0.1%
400 1000 400 49 29000 82.3 0.2%
400 1000 400 50 29090 69.36 0.2%
400 1000 400 51 28810 27.49 0.0%
400 1000 400 52 28880 53.2 0.1%
400 1000 400 53 28880 46.22 0.1%
400 1000 400 54 29070 40.28 0.1%
400 1000 400 55 29050 31.42 0.1%
400 1000 400 56 28870 39.36 0.1%
400 1000 400 57 28900 53.23 0.1%
400 1000 400 58 28840 37.28 0.1%
400 1000 400 59 28930 41.07 0.1%
400 1000 400 60 29000 53.06 0.1%
400 1000 400 61 29110 65.92 0.2%
400 1000 400 62 29080 64.56 0.2%
400 1000 400 63 28870 33.21 0.1%
400 1000 400 64 29010 41.78 0.1%
400 1000 400 65 29060 40.52 0.1%
400 1000 400 66 29100 47.73 0.1%
400 1000 400 67 29070 100.8 0.3%
400 1000 400 68 29080 30.48 0.1%
400 1000 400 69 29060 28.07 0.0%
400 1000 400 70 29170 67.56 0.2%
400 1000 400 71 29030 41.02 0.1%
400 1000 400 72 29270 37.23 0.1%
400 1000 400 73 29190 42.21 0.1%
400 1000 400 74 29060 56.12 0.1%
400 1000 400 75 29150 40.23 0.1%
400 1000 400 76 29190 39.74 0.1%
400 1000 400 77 29260 39.01 0.1%
400 1000 400 78 29200 45.88 0.1%
400 1000 400 79 29130 50.12 0.1%
400 1000 400 80 29260 25.81 0.0%
400 1000 400 81 29060 33.22 0.1%
400 1000 400 82 29350 43.31 0.1%
400 1000 400 83 29220 46.55 0.1%
400 1000 400 84 29310 66.66 0.2%
400 1000 400 85 29330 45.87 0.1%
400 1000 400 86 29350 55.67 0.1%
400 1000 400 87 29290 22.63 0.0%
400 1000 400 88 29360 35.63 0.1%
400 1000 400 89 29380 51.59 0.1%
400 1000 400 90 29410 29.45 0.1%
400 1000 400 91 29200 35.29 0.1%
400 1000 400 92 29330 36.69 0.1%
400 1000 400 93 29320 34.01 0.1%
400 1000 400 94 29310 35.66 0.1%
400 1000 400 95 29340 51.48 0.1%
400 1000 400 96 29310 47.41 0.1%
400 1000 400 97 29400 53.65 0.1%
400 1000 400 98 29410 41.62 0.1%
400 1000 400 99 29400 25.07 0.0%
400 1000 400 100 29310 46.3 0.1%

Quality and confidence:
param error
v 0.046
n 0.018
a 0.046
w 0.096

Model:
Time ~= 0
+ v 0.898
+ n 0.459
+ a 70.79
+ w 6.969
µs

Reads = 6 + (0 * v) + (0 * n) + (4 * a) + (1 * w)
Writes = 2 + (0 * v) + (0 * n) + (0 * a) + (0 * w)
Pallet: "pallet_staking", Extrinsic: "get_npos_voters", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 0
+ v 25.88
+ n 62.24
+ s 13.31
µs

Reads = 2 + (4 * v) + (3 * n) + (0 * s)
Writes = 0 + (0 * v) + (0 * n) + (0 * s)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
v n s mean µs sigma µs %
200 400 20 30210 62.24 0.2%
204 400 20 30190 81.77 0.2%
208 400 20 30420 84.44 0.2%
212 400 20 30540 45.03 0.1%
216 400 20 30610 88.93 0.2%
220 400 20 30720 111.3 0.3%
224 400 20 30770 49.18 0.1%
228 400 20 30660 63.23 0.2%
232 400 20 30860 55.99 0.1%
236 400 20 30950 46.69 0.1%
240 400 20 31010 62.57 0.2%
244 400 20 31200 50.2 0.1%
248 400 20 31360 63.16 0.2%
252 400 20 31470 61.62 0.1%
256 400 20 31250 79.77 0.2%
260 400 20 31550 55.19 0.1%
264 400 20 31830 59.16 0.1%
268 400 20 31850 52.53 0.1%
272 400 20 31880 56.34 0.1%
276 400 20 32310 44.22 0.1%
280 400 20 32260 99.76 0.3%
284 400 20 32380 75.77 0.2%
288 400 20 32250 66.9 0.2%
292 400 20 32660 97.5 0.2%
296 400 20 32410 75.66 0.2%
300 400 20 32740 59.79 0.1%
304 400 20 32550 98.36 0.3%
308 400 20 32930 62.05 0.1%
312 400 20 33270 117.2 0.3%
316 400 20 33300 37.89 0.1%
320 400 20 33260 67.85 0.2%
324 400 20 33490 45.72 0.1%
328 400 20 33210 68.75 0.2%
332 400 20 33350 66.06 0.1%
336 400 20 33630 77.26 0.2%
340 400 20 33820 52.16 0.1%
344 400 20 33790 61.16 0.1%
348 400 20 33950 77.42 0.2%
352 400 20 34180 112.7 0.3%
356 400 20 33820 42.75 0.1%
360 400 20 34330 93.59 0.2%
364 400 20 34110 80.63 0.2%
368 400 20 34560 78.44 0.2%
372 400 20 34760 116.4 0.3%
376 400 20 34790 84.08 0.2%
380 400 20 34900 80.98 0.2%
384 400 20 34890 79.5 0.2%
388 400 20 35080 74.44 0.2%
392 400 20 35220 62.19 0.1%
396 400 20 35110 104.4 0.2%
400 200 20 22960 24.82 0.1%
400 204 20 23230 65.04 0.2%
400 208 20 23590 87.81 0.3%
400 212 20 23520 83.81 0.3%
400 216 20 23600 32 0.1%
400 220 20 24110 74.12 0.3%
400 224 20 24430 50.37 0.2%
400 228 20 24740 88.11 0.3%
400 232 20 24830 42.2 0.1%
400 236 20 25010 47.12 0.1%
400 240 20 25480 64.41 0.2%
400 244 20 25710 78.96 0.3%
400 248 20 26010 96.77 0.3%
400 252 20 25790 84.22 0.3%
400 256 20 26180 64.46 0.2%
400 260 20 26720 55.8 0.2%
400 264 20 27010 78.54 0.2%
400 268 20 26930 80.86 0.3%
400 272 20 27430 51.64 0.1%
400 276 20 27440 115 0.4%
400 280 20 27950 61.47 0.2%
400 284 20 28210 104.2 0.3%
400 288 20 28350 67.41 0.2%
400 292 20 28670 70.63 0.2%
400 296 20 28970 45.51 0.1%
400 300 20 29290 50.4 0.1%
400 304 20 29550 97.36 0.3%
400 308 20 29530 77.15 0.2%
400 312 20 30000 75.28 0.2%
400 316 20 30200 102.7 0.3%
400 320 20 30450 79.98 0.2%
400 324 20 30630 48.18 0.1%
400 328 20 30980 85.17 0.2%
400 332 20 31400 69.98 0.2%
400 336 20 31500 71.37 0.2%
400 340 20 31780 117 0.3%
400 344 20 31770 65.81 0.2%
400 348 20 32250 87.91 0.2%
400 352 20 32380 105.6 0.3%
400 356 20 32430 100.9 0.3%
400 360 20 32490 43.11 0.1%
400 364 20 33130 77.65 0.2%
400 368 20 33430 89.19 0.2%
400 372 20 33780 94.74 0.2%
400 376 20 33930 52.66 0.1%
400 380 20 34160 63.84 0.1%
400 384 20 34450 37.67 0.1%
400 388 20 34630 66.62 0.1%
400 392 20 34930 68.58 0.1%
400 396 20 34900 54.88 0.1%
400 400 1 35080 90.57 0.2%
400 400 2 35080 83.71 0.2%
400 400 3 35120 92.65 0.2%
400 400 4 35150 96.49 0.2%
400 400 5 35100 66.44 0.1%
400 400 6 34690 34.75 0.1%
400 400 7 35060 109.6 0.3%
400 400 8 34870 63.1 0.1%
400 400 9 35250 84.65 0.2%
400 400 10 35230 88.88 0.2%
400 400 11 35220 90.41 0.2%
400 400 12 34900 87.81 0.2%
400 400 13 35510 82.46 0.2%
400 400 14 35370 82.04 0.2%
400 400 15 35490 43.15 0.1%
400 400 16 35350 89.02 0.2%
400 400 17 35380 58.53 0.1%
400 400 18 35340 94.61 0.2%
400 400 19 35080 66.36 0.1%
400 400 20 35180 89.74 0.2%

Quality and confidence:
param error
v 0.094
n 0.094
s 1.291

Model:
Time ~= 0
+ v 26.28
+ n 62.12
+ s 17.31
µs

Reads = 2 + (4 * v) + (3 * n) + (0 * s)
Writes = 0 + (0 * v) + (0 * n) + (0 * s)
Pallet: "pallet_staking", Extrinsic: "get_npos_targets", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 0
+ v 10.12
µs

Reads = 1 + (1 * v)
Writes = 0 + (0 * v)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
v mean µs sigma µs %
200 1947 15.65 0.8%
204 2012 11.05 0.5%
208 2036 12.88 0.6%
212 2057 21.18 1.0%
216 2102 11.5 0.5%
220 2157 16.47 0.7%
224 2173 11.65 0.5%
228 2187 19.82 0.9%
232 2226 10.78 0.4%
236 2328 9.2 0.3%
240 2383 11.98 0.5%
244 2375 7.725 0.3%
248 2377 8.932 0.3%
252 2497 22.13 0.8%
256 2466 13.2 0.5%
260 2556 19.7 0.7%
264 2614 9.243 0.3%
268 2633 15.46 0.5%
272 2668 16.48 0.6%
276 2724 14.44 0.5%
280 2744 9.357 0.3%
284 2765 16.65 0.6%
288 2814 24.66 0.8%
292 2915 11.05 0.3%
296 2910 19.11 0.6%
300 2994 16.57 0.5%
304 2939 19.35 0.6%
308 3035 14.12 0.4%
312 3038 12.6 0.4%
316 3121 12.94 0.4%
320 3151 16.25 0.5%
324 3137 23.7 0.7%
328 3212 10.82 0.3%
332 3251 16.06 0.4%
336 3338 20.72 0.6%
340 3377 27.45 0.8%
344 3418 13.04 0.3%
348 3384 13.3 0.3%
352 3484 15.59 0.4%
356 3504 20.08 0.5%
360 3503 13.64 0.3%
364 3565 11.67 0.3%
368 3695 26.92 0.7%
372 3696 20.82 0.5%
376 3702 18.76 0.5%
380 3819 16.01 0.4%
384 3836 26.5 0.6%
388 3915 20.68 0.5%
392 3856 15.81 0.4%
396 3921 44.93 1.1%
400 3964 20.8 0.5%

Quality and confidence:
param error
v 0.026

Model:
Time ~= 0
+ v 10.13
µs

Reads = 1 + (1 * v)
Writes = 0 + (0 * v)

…n=kusama-dev --steps=50 --repeat=20 --pallet=pallet_staking --extrinsic=* --execution=wasm --wasm-execution=compiled --heap-pages=4096 --header=./file_header.txt --output=./runtime/kusama/src/weights/

parity-benchapp · 2021-03-17T19:53:18Z

Finished benchmark for branch: kiz-increase-payouts

Benchmark: Benchmark Runtime Westend Pallet

cargo run --release --features=runtime-benchmarks -- benchmark --chain=westend-dev --steps=50 --repeat=20 --pallet=pallet_staking --extrinsic="*" --execution=wasm --wasm-execution=compiled --heap-pages=4096 --header=./file_header.txt --output=./runtime/westend/src/weights/

Results

Pallet: "pallet_staking", Extrinsic: "bond", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 77.94
µs

Reads = 5
Writes = 4
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 77.94
µs

Reads = 5
Writes = 4
Pallet: "pallet_staking", Extrinsic: "bond_extra", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 63.02
µs

Reads = 4
Writes = 2
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 63.02
µs

Reads = 4
Writes = 2
Pallet: "pallet_staking", Extrinsic: "unbond", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 57.39
µs

Reads = 5
Writes = 3
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 57.39
µs

Reads = 5
Writes = 3
Pallet: "pallet_staking", Extrinsic: "withdraw_unbonded_update", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 57.55
+ s 0.029
µs

Reads = 5 + (0 * s)
Writes = 3 + (0 * s)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
s mean µs sigma µs %
0 57.02 0.065 0.1%
2 57.49 0.095 0.1%
4 57.63 0.08 0.1%
6 57.61 0.097 0.1%
8 58.09 0.08 0.1%
10 57.75 0.135 0.2%
12 57.93 0.124 0.2%
14 57.73 0.187 0.3%
16 58.07 0.046 0.0%
18 58.1 0.119 0.2%
20 58.22 0.226 0.3%
22 58.15 0.164 0.2%
24 58.42 0.123 0.2%
26 58.63 0.169 0.2%
28 58.32 0.205 0.3%
30 58.63 0.184 0.3%
32 58.65 0.145 0.2%
34 58.79 0.113 0.1%
36 58.58 0.088 0.1%
38 58.67 0.079 0.1%
40 58.54 0.094 0.1%
42 58.83 0.103 0.1%
44 58.88 0.191 0.3%
46 58.83 0.158 0.2%
48 58.77 0.131 0.2%
50 59.16 0.109 0.1%
52 59.04 0.136 0.2%
54 58.86 0.143 0.2%
56 59.33 0.186 0.3%
58 59.42 0.141 0.2%
60 59.35 0.119 0.2%
62 59.21 0.133 0.2%
64 59.74 0.223 0.3%
66 60.04 0.082 0.1%
68 59.89 0.118 0.1%
70 59.76 0.113 0.1%
72 59.87 0.166 0.2%
74 59.69 0.113 0.1%
76 59.84 0.093 0.1%
78 60.06 0.146 0.2%
80 59.93 0.124 0.2%
82 59.96 0.173 0.2%
84 60.26 0.098 0.1%
86 60.09 0.132 0.2%
88 59.9 0.123 0.2%
90 60.26 0.123 0.2%
92 60.55 0.294 0.4%
94 59.97 0.099 0.1%
96 60.4 0.163 0.2%
98 60.17 0.141 0.2%
100 60.16 0.133 0.2%

Quality and confidence:
param error
s 0

Model:
Time ~= 57.55
+ s 0.03
µs

Reads = 5 + (0 * s)
Writes = 3 + (0 * s)
Pallet: "pallet_staking", Extrinsic: "withdraw_unbonded_kill", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 90.01
+ s 2.528
µs

Reads = 7 + (0 * s)
Writes = 8 + (1 * s)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
s mean µs sigma µs %
0 86.02 0.28 0.3%
2 95.2 0.227 0.2%
4 100.3 0.112 0.1%
6 105.3 0.179 0.1%
8 110.2 0.143 0.1%
10 115.4 0.201 0.1%
12 120.8 0.12 0.0%
14 125.6 0.176 0.1%
16 130.5 0.184 0.1%
18 135.2 0.318 0.2%
20 140.5 0.136 0.0%
22 145.5 0.14 0.0%
24 151.1 0.236 0.1%
26 155.9 0.289 0.1%
28 160.7 0.361 0.2%
30 165.9 0.194 0.1%
32 170.7 0.291 0.1%
34 175.6 0.254 0.1%
36 180.9 0.314 0.1%
38 185.5 0.305 0.1%
40 191.2 1.335 0.6%
42 195.9 0.24 0.1%
44 200.9 0.203 0.1%
46 207.3 1.945 0.9%
48 212.1 0.683 0.3%
50 215.7 0.687 0.3%
52 221.3 0.142 0.0%
54 225.9 0.636 0.2%
56 231.6 0.456 0.1%
58 238.7 2.123 0.8%
60 241.5 0.476 0.1%
62 246.6 0.418 0.1%
64 252.2 0.565 0.2%
66 256.2 0.378 0.1%
68 263.5 1.895 0.7%
70 268.6 1.832 0.6%
72 271.9 0.357 0.1%
74 277.6 0.76 0.2%
76 282.4 0.868 0.3%
78 286.3 0.466 0.1%
80 293.4 0.978 0.3%
82 296.5 0.331 0.1%
84 302.3 0.509 0.1%
86 306.8 0.774 0.2%
88 312 0.917 0.2%
90 316.3 0.496 0.1%
92 323 0.887 0.2%
94 329.4 0.692 0.2%
96 333 0.465 0.1%
98 337.4 0.853 0.2%
100 344.7 1.146 0.3%

Quality and confidence:
param error
s 0.001

Model:
Time ~= 89.71
+ s 2.536
µs

Reads = 7 + (0 * s)
Writes = 8 + (1 * s)
Pallet: "pallet_staking", Extrinsic: "validate", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 19.16
µs

Reads = 2
Writes = 2
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 19.16
µs

Reads = 2
Writes = 2
Pallet: "pallet_staking", Extrinsic: "kick", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 19.18
+ k 18.56
µs

Reads = 2 + (1 * k)
Writes = 0 + (1 * k)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
k mean µs sigma µs %
1 43.93 0.082 0.1%
3 81.79 0.304 0.3%
5 117.8 0.309 0.2%
7 154.4 0.216 0.1%
9 189.7 0.363 0.1%
11 226.7 0.289 0.1%
13 261.3 0.292 0.1%
15 297.1 0.458 0.1%
17 334.5 0.743 0.2%
19 372.1 0.584 0.1%
21 410.4 1.427 0.3%
23 445.5 0.481 0.1%
25 479.5 0.851 0.1%
27 519 0.815 0.1%
29 556 1.571 0.2%
31 586.7 0.656 0.1%
33 625.5 1.323 0.2%
35 662.2 1.399 0.2%
37 701.5 1.612 0.2%
39 737.5 4.365 0.5%
41 775 0.89 0.1%
43 808.1 1.296 0.1%
45 848.2 0.832 0.0%
47 899.4 13.4 1.4%
49 925 1.666 0.1%
51 965.9 1.374 0.1%
53 999.9 1.597 0.1%
55 1035 2.894 0.2%
57 1071 2.234 0.2%
59 1130 11.91 1.0%
61 1168 12.38 1.0%
63 1183 8.378 0.7%
65 1225 3.47 0.2%
67 1262 2.635 0.2%
69 1302 9.361 0.7%
71 1336 12.47 0.9%
73 1369 7.508 0.5%
75 1407 3.382 0.2%
77 1444 10.81 0.7%
79 1483 6.892 0.4%
81 1512 3.368 0.2%
83 1563 15.73 1.0%
85 1586 6.286 0.3%
87 1629 11.63 0.7%
89 1669 10.93 0.6%
91 1700 10.21 0.6%
93 1738 4.956 0.2%
95 1784 13.33 0.7%
97 1814 10.48 0.5%
99 1863 10.64 0.5%
101 1933 12.87 0.6%
103 1940 11.24 0.5%
105 1980 12.44 0.6%
107 2021 12.55 0.6%
109 2048 3.407 0.1%
111 2094 8.814 0.4%
113 2137 13.04 0.6%
115 2159 10.65 0.4%
117 2200 7.18 0.3%
119 2244 11.9 0.5%
121 2271 9.428 0.4%
123 2316 6.881 0.2%
125 2365 12.4 0.5%
127 2378 10.45 0.4%

Quality and confidence:
param error
k 0.012

Model:
Time ~= 15.31
+ k 18.65
µs

Reads = 2 + (1 * k)
Writes = 0 + (1 * k)
Pallet: "pallet_staking", Extrinsic: "nominate", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 29.62
+ n 5.899
µs

Reads = 4 + (1 * n)
Writes = 2 + (0 * n)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
n mean µs sigma µs %
1 34.86 0.138 0.3%
2 41.52 0.066 0.1%
3 47.15 0.117 0.2%
4 53.09 0.129 0.2%
5 59.11 0.136 0.2%
6 65.26 0.306 0.4%
7 71.07 0.225 0.3%
8 76.26 0.309 0.4%
9 83.73 0.189 0.2%
10 88.84 0.17 0.1%
11 95.09 0.379 0.3%
12 100.6 0.169 0.1%
13 106.1 0.199 0.1%
14 112.5 0.252 0.2%
15 116.6 0.308 0.2%
16 123.6 0.236 0.1%

Quality and confidence:
param error
n 0.01

Model:
Time ~= 29.66
+ n 5.889
µs

Reads = 4 + (1 * n)
Writes = 2 + (0 * n)
Pallet: "pallet_staking", Extrinsic: "chill", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 18.37
µs

Reads = 2
Writes = 2
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 18.37
µs

Reads = 2
Writes = 2
Pallet: "pallet_staking", Extrinsic: "set_payee", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 12.42
µs

Reads = 1
Writes = 1
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 12.42
µs

Reads = 1
Writes = 1
Pallet: "pallet_staking", Extrinsic: "set_controller", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 27.52
µs

Reads = 3
Writes = 3
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 27.52
µs

Reads = 3
Writes = 3
Pallet: "pallet_staking", Extrinsic: "set_validator_count", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 2.166
µs

Reads = 0
Writes = 1
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 2.166
µs

Reads = 0
Writes = 1
Pallet: "pallet_staking", Extrinsic: "force_no_eras", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 2.327
µs

Reads = 0
Writes = 1
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 2.327
µs

Reads = 0
Writes = 1
Pallet: "pallet_staking", Extrinsic: "force_new_era", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 2.331
µs

Reads = 0
Writes = 1
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 2.331
µs

Reads = 0
Writes = 1
Pallet: "pallet_staking", Extrinsic: "force_new_era_always", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 2.368
µs

Reads = 0
Writes = 1
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 2.368
µs

Reads = 0
Writes = 1
Pallet: "pallet_staking", Extrinsic: "set_invulnerables", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 2.458
+ v 0.034
µs

Reads = 0 + (0 * v)
Writes = 1 + (0 * v)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
v mean µs sigma µs %
0 2.381 0.02 0.8%
20 3.239 0.015 0.4%
40 4.007 0.029 0.7%
60 4.616 0.01 0.2%
80 5.33 0.02 0.3%
100 6.01 0.039 0.6%
120 6.796 0.023 0.3%
140 7.397 0.022 0.2%
160 8.072 0.022 0.2%
180 8.74 0.041 0.4%
200 9.405 0.02 0.2%
220 10.07 0.023 0.2%
240 10.77 0.03 0.2%
260 11.43 0.043 0.3%
280 12.15 0.02 0.1%
300 12.8 0.036 0.2%
320 13.51 0.023 0.1%
340 14.14 0.048 0.3%
360 14.91 0.023 0.1%
380 15.54 0.014 0.0%
400 16.27 0.027 0.1%
420 16.94 0.02 0.1%
440 17.61 0.034 0.1%
460 18.35 0.035 0.1%
480 18.97 0.03 0.1%
500 19.64 0.031 0.1%
520 20.45 0.017 0.0%
540 21.1 0.012 0.0%
560 21.79 0.035 0.1%
580 22.51 0.021 0.0%
600 23.2 0.025 0.1%
620 23.86 0.03 0.1%
640 24.72 0.028 0.1%
660 25.43 0.024 0.0%
680 26.1 0.012 0.0%
700 26.73 0.029 0.1%
720 27.43 0.041 0.1%
740 28.1 0.016 0.0%
760 28.74 0.044 0.1%
780 29.6 0.035 0.1%
800 30.27 0.019 0.0%
820 30.96 0.026 0.0%
840 31.63 0.021 0.0%
860 32.3 0.031 0.0%
880 33 0.026 0.0%
900 33.82 0.057 0.1%
920 34.46 0.021 0.0%
940 35.14 0.031 0.0%
960 35.83 0.019 0.0%
980 36.5 0.017 0.0%
1000 37.17 0.025 0.0%

Quality and confidence:
param error
v 0

Model:
Time ~= 2.457
+ v 0.035
µs

Reads = 0 + (0 * v)
Writes = 1 + (0 * v)
Pallet: "pallet_staking", Extrinsic: "force_unstake", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 60.37
+ s 2.524
µs

Reads = 4 + (0 * s)
Writes = 8 + (1 * s)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
s mean µs sigma µs %
0 55.91 0.126 0.2%
2 64.6 0.099 0.1%
4 70.47 0.097 0.1%
6 75.55 0.067 0.0%
8 80.53 0.246 0.3%
10 85.41 0.166 0.1%
12 91 0.114 0.1%
14 95.86 0.131 0.1%
16 100.8 0.265 0.2%
18 105.9 0.156 0.1%
20 110.8 0.136 0.1%
22 116.1 0.193 0.1%
24 121.6 0.158 0.1%
26 126.4 0.079 0.0%
28 131.5 0.222 0.1%
30 136 0.251 0.1%
32 140.8 0.199 0.1%
34 146.2 0.273 0.1%
36 151.1 0.195 0.1%
38 156.1 0.352 0.2%
40 161.6 0.361 0.2%
42 166.6 0.368 0.2%
44 171.6 0.337 0.1%
46 176.7 0.599 0.3%
48 182.3 0.59 0.3%
50 186.7 0.359 0.1%
52 191.1 0.269 0.1%
54 196.2 0.244 0.1%
56 201.4 0.389 0.1%
58 205.9 0.406 0.1%
60 211.6 0.536 0.2%
62 216.7 0.415 0.1%
64 221.6 0.762 0.3%
66 227.4 0.359 0.1%
68 231.8 0.654 0.2%
70 237.1 0.852 0.3%
72 247.8 0.793 0.3%
74 250.9 1.891 0.7%
76 251.6 0.785 0.3%
78 257.7 0.608 0.2%
80 261.8 0.773 0.2%
82 266.8 0.774 0.2%
84 271.4 0.591 0.2%
86 275.9 0.81 0.2%
88 286.3 5.645 1.9%
90 286.1 0.958 0.3%
92 292 0.698 0.2%
94 297.6 1.003 0.3%
96 302.4 0.68 0.2%
98 308.5 1.006 0.3%
100 313.3 0.708 0.2%

Quality and confidence:
param error
s 0.002

Model:
Time ~= 60.02
+ s 2.533
µs

Reads = 4 + (0 * s)
Writes = 8 + (1 * s)
Pallet: "pallet_staking", Extrinsic: "cancel_deferred_slash", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 6577
+ s 34.93
µs

Reads = 1 + (0 * s)
Writes = 1 + (0 * s)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
s mean µs sigma µs %
1 436.7 0.244 0.0%
20 1728 4.608 0.2%
39 3002 13.33 0.4%
58 4252 4.63 0.1%
77 5470 3.823 0.0%
96 6674 13.35 0.2%
115 7842 11.29 0.1%
134 8977 12.76 0.1%
153 10100 12.21 0.1%
172 11200 10.99 0.0%
191 12280 23.22 0.1%
210 13320 15.19 0.1%
229 14330 23.28 0.1%
248 15340 11.54 0.0%
267 16290 9.986 0.0%
286 17230 11.76 0.0%
305 18180 11.71 0.0%
324 19060 14.07 0.0%
343 19950 22.48 0.1%
362 20990 146 0.6%
381 21610 15.8 0.0%
400 22420 21.54 0.0%
419 23180 21.77 0.0%
438 23960 20.97 0.0%
457 24660 15.98 0.0%
476 25370 15.24 0.0%
495 26030 17.52 0.0%
514 26670 27.75 0.1%
533 27340 36.48 0.1%
552 27920 27 0.0%
571 28480 17.51 0.0%
590 29020 27.38 0.0%
609 30040 28.37 0.0%
628 30040 14.57 0.0%
647 30490 20.98 0.0%
666 30960 20.02 0.0%
685 31410 28.05 0.0%
704 31780 29.88 0.0%
723 32150 13.5 0.0%
742 32490 16.48 0.0%
761 32850 23.52 0.0%
780 33140 39.04 0.1%
799 33420 26.91 0.0%
818 33690 57.48 0.1%
837 33920 45.55 0.1%
856 34110 23.23 0.0%
875 34270 26.99 0.0%
894 34410 30.54 0.0%
913 34520 19.45 0.0%
932 34630 29.85 0.0%
951 34680 19.39 0.0%
970 34750 22.05 0.0%
989 34790 44.18 0.1%

Quality and confidence:
param error
s 0.392

Model:
Time ~= 5917
+ s 34.78
µs

Reads = 1 + (0 * s)
Writes = 1 + (0 * s)
Pallet: "pallet_staking", Extrinsic: "payout_stakers_dead_controller", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 110.9
+ n 50.66
µs

Reads = 11 + (3 * n)
Writes = 2 + (1 * n)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
n mean µs sigma µs %
1 158.7 0.298 0.1%
2 207.7 0.243 0.1%
3 260.7 0.497 0.1%
4 312.7 0.544 0.1%
5 364.3 0.777 0.2%
6 412.9 0.366 0.0%
7 461.5 0.598 0.1%
8 512.4 0.686 0.1%
9 566 0.556 0.0%
10 617.5 0.723 0.1%
11 667.3 1.246 0.1%
12 717.6 0.69 0.0%
13 770 1.117 0.1%
14 820.7 2.081 0.2%
15 875 2.165 0.2%
16 923.9 1.567 0.1%
17 975.7 0.907 0.0%
18 1023 1.446 0.1%
19 1084 12.56 1.1%
20 1127 4.254 0.3%
21 1177 1.805 0.1%
22 1227 2.088 0.1%
23 1274 2.337 0.1%
24 1328 15.28 1.1%
25 1394 17.25 1.2%
26 1434 11 0.7%
27 1471 3.405 0.2%
28 1526 2.825 0.1%
29 1586 10.52 0.6%
30 1633 11.53 0.7%
31 1684 14.27 0.8%
32 1735 9.864 0.5%
33 1794 10.96 0.6%
34 1835 8.977 0.4%
35 1894 9.79 0.5%
36 1937 6.067 0.3%
37 1983 4.418 0.2%
38 2047 14.2 0.6%
39 2082 7.37 0.3%
40 2141 7.775 0.3%
41 2186 8.867 0.4%
42 2242 7.919 0.3%
43 2317 3.719 0.1%
44 2366 7.439 0.3%
45 2389 13.17 0.5%
46 2445 8.908 0.3%
47 2495 10.71 0.4%
48 2535 14.1 0.5%
49 2595 10.65 0.4%
50 2650 10.75 0.4%
51 2689 15.69 0.5%
52 2761 7.396 0.2%
53 2808 12.33 0.4%
54 2840 12.22 0.4%
55 2893 14.46 0.4%
56 2949 15.46 0.5%
57 3005 13.01 0.4%
58 3053 10.25 0.3%
59 3091 14.17 0.4%
60 3151 10.03 0.3%
61 3218 7.995 0.2%
62 3265 17.75 0.5%
63 3286 14.33 0.4%
64 3342 15.73 0.4%

Quality and confidence:
param error
n 0.025

Model:
Time ~= 111.4
+ n 50.72
µs

Reads = 11 + (3 * n)
Writes = 2 + (1 * n)
Pallet: "pallet_staking", Extrinsic: "payout_stakers_alive_staked", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 137.7
+ n 64.47
µs

Reads = 12 + (5 * n)
Writes = 3 + (3 * n)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
n mean µs sigma µs %
1 199.7 0.23 0.1%
2 265.8 0.359 0.1%
3 329.9 0.757 0.2%
4 394 0.706 0.1%
5 456.5 1.123 0.2%
6 541.9 13.83 2.5%
7 600.7 10.24 1.7%
8 657.2 8.584 1.3%
9 716.6 1.976 0.2%
10 782.4 1.744 0.2%
11 848.6 5.735 0.6%
12 912.5 6.571 0.7%
13 971.1 1.82 0.1%
14 1028 1.463 0.1%
15 1102 8.426 0.7%
16 1166 12.02 1.0%
17 1230 3.936 0.3%
18 1294 10.97 0.8%
19 1370 12.3 0.8%
20 1437 11.09 0.7%
21 1507 10.4 0.6%
22 1547 7.491 0.4%
23 1634 9.975 0.6%
24 1688 8.921 0.5%
25 1752 12.11 0.6%
26 1810 1.418 0.0%
27 1866 3.442 0.1%
28 1948 14.24 0.7%
29 2005 11.71 0.5%
30 2081 15.19 0.7%
31 2142 11.87 0.5%
32 2203 12.09 0.5%
33 2265 12.53 0.5%
34 2329 15.25 0.6%
35 2382 11.18 0.4%
36 2464 14.66 0.5%
37 2528 12.45 0.4%
38 2590 16.64 0.6%
39 2659 9.661 0.3%
40 2715 12.33 0.4%
41 2829 6.953 0.2%
42 2877 20.31 0.7%
43 2924 16.23 0.5%
44 3009 14.64 0.4%
45 3055 9.359 0.3%
46 3113 13.6 0.4%
47 3183 10.9 0.3%
48 3248 5.462 0.1%
49 3339 3.537 0.1%
50 3390 3.968 0.1%
51 3429 14.63 0.4%
52 3515 18.4 0.5%
53 3553 14.11 0.3%
54 3602 13.57 0.3%
55 3678 12.68 0.3%
56 3753 15.21 0.4%
57 3805 14.07 0.3%
58 3872 14.33 0.3%
59 3935 18.25 0.4%
60 3999 11.34 0.2%
61 4092 12.26 0.2%
62 4124 5.793 0.1%
63 4204 5.897 0.1%
64 4270 11.48 0.2%

Quality and confidence:
param error
n 0.035

Model:
Time ~= 138.2
+ n 64.61
µs

Reads = 12 + (5 * n)
Writes = 3 + (3 * n)
Pallet: "pallet_staking", Extrinsic: "rebond", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 39.8
+ l 0.086
µs

Reads = 4 + (0 * l)
Writes = 3 + (0 * l)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
l mean µs sigma µs %
1 39.59 0.21 0.5%
2 39.46 0.111 0.2%
3 39.54 0.08 0.2%
4 39.58 0.056 0.1%
5 40.19 0.153 0.3%
6 40.33 0.118 0.2%
7 40.28 0.092 0.2%
8 40.29 0.145 0.3%
9 40.71 0.093 0.2%
10 40.75 0.101 0.2%
11 40.75 0.067 0.1%
12 41.14 0.132 0.3%
13 41.02 0.109 0.2%
14 41.17 0.061 0.1%
15 41.1 0.104 0.2%
16 41.21 0.07 0.1%
17 41.62 0.124 0.2%
18 41.82 0.077 0.1%
19 41.82 0.065 0.1%
20 41.9 0.126 0.3%
21 41.84 0.07 0.1%
22 41.98 0.105 0.2%
23 41.91 0.132 0.3%
24 41.77 0.093 0.2%
25 41.97 0.115 0.2%
26 42.03 0.062 0.1%
27 42.04 0.094 0.2%
28 42.04 0.087 0.2%
29 41.91 0.094 0.2%
30 42.11 0.113 0.2%
31 41.94 0.089 0.2%
32 41.92 0.087 0.2%

Quality and confidence:
param error
l 0.001

Model:
Time ~= 39.74
+ l 0.087
µs

Reads = 4 + (0 * l)
Writes = 3 + (0 * l)
Pallet: "pallet_staking", Extrinsic: "set_history_depth", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 0
+ e 32.29
µs

Reads = 2 + (0 * e)
Writes = 4 + (7 * e)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
e mean µs sigma µs %
1 39.15 0.152 0.3%
2 64.14 0.142 0.2%
3 87.9 0.263 0.2%
4 113.5 0.151 0.1%
5 137.6 0.207 0.1%
6 161.6 0.16 0.0%
7 186.5 0.277 0.1%
8 211.1 0.206 0.0%
9 234.8 0.4 0.1%
10 263.1 0.297 0.1%
11 286.5 0.429 0.1%
12 315.7 0.864 0.2%
13 338.9 0.541 0.1%
14 365.6 0.549 0.1%
15 391.9 0.614 0.1%
16 417.2 0.344 0.0%
17 450 0.572 0.1%
18 473.3 0.321 0.0%
19 500.7 0.904 0.1%
20 530.7 0.838 0.1%
21 559 1.474 0.2%
22 586.5 0.929 0.1%
23 612.7 0.8 0.1%
24 643.4 2.47 0.3%
25 675.1 5.705 0.8%
26 701.9 0.598 0.0%
27 726.6 1.305 0.1%
28 763 1.833 0.2%
29 784.6 2.125 0.2%
30 818.6 8.073 0.9%
31 849.2 1.683 0.1%
32 876.2 1.69 0.1%
33 901.6 0.879 0.0%
34 935.5 1.485 0.1%
35 969.4 3.11 0.3%
36 992.9 1.705 0.1%
37 1026 2.926 0.2%
38 1058 2.748 0.2%
39 1081 1.833 0.1%
40 1112 2.406 0.2%
41 1144 1.811 0.1%
42 1174 2.539 0.2%
43 1205 3.804 0.3%
44 1232 3.803 0.3%
45 1290 7.434 0.5%
46 1296 7.544 0.5%
47 1350 12 0.8%
48 1372 1.995 0.1%
49 1403 2.084 0.1%
50 1445 11.28 0.7%
51 1476 6.228 0.4%
52 1508 8.321 0.5%
53 1528 8.232 0.5%
54 1574 12.97 0.8%
55 1619 8.514 0.5%
56 1644 7.078 0.4%
57 1695 13.83 0.8%
58 1715 3.577 0.2%
59 1753 13.11 0.7%
60 1772 9.38 0.5%
61 1813 8.307 0.4%
62 1842 8.969 0.4%
63 1866 9.076 0.4%
64 1900 11.25 0.5%
65 1929 10.62 0.5%
66 1981 11.38 0.5%
67 1994 12.29 0.6%
68 2031 9.001 0.4%
69 2067 8.86 0.4%
70 2098 10.08 0.4%
71 2146 14.12 0.6%
72 2187 13.04 0.5%
73 2226 12.08 0.5%
74 2238 9.983 0.4%
75 2282 9.891 0.4%
76 2330 12.89 0.5%
77 2372 12.47 0.5%
78 2390 11.14 0.4%
79 2448 8.888 0.3%
80 2472 15.06 0.6%
81 2488 7.155 0.2%
82 2568 12.94 0.5%
83 2588 10.01 0.3%
84 2640 14.1 0.5%
85 2659 13.94 0.5%
86 2689 10.96 0.4%
87 2729 13.6 0.4%
88 2765 15.34 0.5%
89 2800 13.2 0.4%
90 2841 12.89 0.4%
91 2868 5.123 0.1%
92 2958 5.437 0.1%
93 2936 4.295 0.1%
94 2987 5.635 0.1%
95 3027 7.83 0.2%
96 3075 4.184 0.1%
97 3128 8.452 0.2%
98 3158 9.58 0.3%
99 3181 11.17 0.3%
100 3236 7.623 0.2%

Quality and confidence:
param error
e 0.061

Model:
Time ~= 0
+ e 32.36
µs

Reads = 2 + (0 * e)
Writes = 4 + (7 * e)
Pallet: "pallet_staking", Extrinsic: "reap_stash", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 63.88
+ s 2.53
µs

Reads = 4 + (0 * s)
Writes = 8 + (1 * s)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
s mean µs sigma µs %
1 65.6 0.164 0.2%
2 68.03 0.072 0.1%
3 70.75 0.116 0.1%
4 74.08 0.112 0.1%
5 76.57 0.133 0.1%
6 79.16 0.117 0.1%
7 81.77 0.099 0.1%
8 84.06 0.205 0.2%
9 86.63 0.125 0.1%
10 88.88 0.059 0.0%
11 91.64 0.12 0.1%
12 94.17 0.165 0.1%
13 97.14 0.176 0.1%
14 99.54 0.187 0.1%
15 101.8 0.126 0.1%
16 104.7 0.231 0.2%
17 106.9 0.109 0.1%
18 109.2 0.203 0.1%
19 111.8 0.114 0.1%
20 114.7 0.163 0.1%
21 117.2 0.183 0.1%
22 119.4 0.287 0.2%
23 121.9 0.164 0.1%
24 125 0.342 0.2%
25 127.6 0.234 0.1%
26 129.8 0.231 0.1%
27 132.6 0.256 0.1%
28 134.8 0.236 0.1%
29 137.7 0.207 0.1%
30 139.8 0.265 0.1%
31 142.6 0.327 0.2%
32 144.9 0.243 0.1%
33 147.3 0.237 0.1%
34 149.7 0.417 0.2%
35 152.1 0.316 0.2%
36 154.7 0.345 0.2%
37 157.3 0.449 0.2%
38 159.7 0.361 0.2%
39 162.6 0.499 0.3%
40 165.4 0.419 0.2%
41 167.5 0.281 0.1%
42 170.5 0.34 0.1%
43 172.7 0.306 0.1%
44 175.2 0.306 0.1%
45 177.8 0.41 0.2%
46 180.6 0.796 0.4%
47 182.9 0.373 0.2%
48 185.5 0.448 0.2%
49 187.8 0.4 0.2%
50 190 0.568 0.2%
51 192.5 0.651 0.3%
52 195.1 0.375 0.1%
53 197.2 0.444 0.2%
54 200.1 0.52 0.2%
55 203 0.753 0.3%
56 205.5 0.343 0.1%
57 207.1 0.421 0.2%
58 209.6 0.35 0.1%
59 212.5 0.385 0.1%
60 214.9 0.819 0.3%
61 218.4 0.403 0.1%
62 220.8 0.585 0.2%
63 223.2 0.41 0.1%
64 225.4 0.469 0.2%
65 228.4 0.481 0.2%
66 230.8 0.348 0.1%
67 234.6 1.219 0.5%
68 235.7 0.317 0.1%
69 239.1 0.586 0.2%
70 241.1 0.739 0.3%
71 243.4 0.316 0.1%
72 246 0.469 0.1%
73 248.5 0.626 0.2%
74 251.6 0.478 0.1%
75 253.9 0.325 0.1%
76 255.4 0.396 0.1%
77 259.3 0.568 0.2%
78 260.9 0.656 0.2%
79 264.8 0.586 0.2%
80 266.1 0.509 0.1%
81 270.7 1.323 0.4%
82 271.6 1.005 0.3%
83 273.7 0.929 0.3%
84 276 0.489 0.1%
85 280.7 2.448 0.8%
86 281.9 1.944 0.6%
87 283.8 0.441 0.1%
88 286 0.562 0.1%
89 289.6 0.737 0.2%
90 291.1 0.589 0.2%
91 292.6 0.642 0.2%
92 298.7 1.181 0.3%
93 299.7 0.599 0.1%
94 301.6 0.697 0.2%
95 303.1 0.513 0.1%
96 307.6 0.419 0.1%
97 309.5 0.607 0.1%
98 312.3 0.728 0.2%
99 315.4 0.522 0.1%
100 316.9 0.358 0.1%

Quality and confidence:
param error
s 0

Model:
Time ~= 63.76
+ s 2.534
µs

Reads = 4 + (0 * s)
Writes = 8 + (1 * s)
Pallet: "pallet_staking", Extrinsic: "new_era", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 0
+ v 562.6
+ n 82.27
µs

Reads = 9 + (4 * v) + (3 * n)
Writes = 13 + (3 * v) + (0 * n)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
v n mean µs sigma µs %
1 100 3756 7.336 0.1%
2 100 4342 13.51 0.3%
3 100 4836 11 0.2%
4 100 5276 10.26 0.1%
5 100 5913 11.72 0.1%
6 100 6418 12.61 0.1%
7 100 6943 10.61 0.1%
8 100 7715 53.95 0.6%
9 100 8233 14.04 0.1%
10 1 707.8 1.658 0.2%
10 2 796.2 1.731 0.2%
10 3 882.5 1.778 0.2%
10 4 964.9 7.765 0.8%
10 5 1048 4.612 0.4%
10 6 1137 14.13 1.2%
10 7 1223 11.32 0.9%
10 8 1301 14.09 1.0%
10 9 1374 11.01 0.8%
10 10 1479 13.44 0.9%
10 11 1533 2.147 0.1%
10 12 1634 11.92 0.7%
10 13 1708 11.12 0.6%
10 14 1789 14.41 0.8%
10 15 1857 9.732 0.5%
10 16 1952 12.34 0.6%
10 17 2036 11.31 0.5%
10 18 2118 14.66 0.6%
10 19 2212 3.673 0.1%
10 20 2317 3.402 0.1%
10 21 2401 5.682 0.2%
10 22 2454 10.09 0.4%
10 23 2552 13.76 0.5%
10 24 2632 11.16 0.4%
10 25 2711 11.27 0.4%
10 26 2789 8.159 0.2%
10 27 2880 9.254 0.3%
10 28 2954 9.933 0.3%
10 29 3034 17.49 0.5%
10 30 3106 11.57 0.3%
10 31 3191 14.14 0.4%
10 32 3291 12.32 0.3%
10 33 3379 10.19 0.3%
10 34 3455 13.24 0.3%
10 35 3538 6.235 0.1%
10 36 3623 10.05 0.2%
10 37 3695 6.994 0.1%
10 38 3808 6.567 0.1%
10 39 3885 6.341 0.1%
10 40 3980 5.696 0.1%
10 41 4054 9.205 0.2%
10 42 4142 7.768 0.1%
10 43 4272 9.615 0.2%
10 44 4321 26.42 0.6%
10 45 4369 3.316 0.0%
10 46 4459 12.35 0.2%
10 47 4525 13.32 0.2%
10 48 4616 3.346 0.0%
10 49 4695 4.704 0.1%
10 50 4827 6.189 0.1%
10 51 4899 14.94 0.3%
10 52 4965 18.92 0.3%
10 53 5003 12.86 0.2%
10 54 5139 9.128 0.1%
10 55 5234 7.422 0.1%
10 56 5284 7.12 0.1%
10 57 5394 11.58 0.2%
10 58 5486 7.055 0.1%
10 59 5550 6.633 0.1%
10 60 5655 12.6 0.2%
10 61 5697 8.699 0.1%
10 62 5813 12.79 0.2%
10 63 5856 4.365 0.0%
10 64 5969 5.779 0.0%
10 65 6027 8.827 0.1%
10 66 6116 7.405 0.1%
10 67 6253 21.39 0.3%
10 68 6272 9.972 0.1%
10 69 6362 13 0.2%
10 70 6439 22.31 0.3%
10 71 6517 4.274 0.0%
10 72 6601 9.796 0.1%
10 73 6671 13.8 0.2%
10 74 6772 8.619 0.1%
10 75 6829 6.853 0.1%
10 76 6904 10.73 0.1%
10 77 6967 8.683 0.1%
10 78 7087 11.66 0.1%
10 79 7163 8.385 0.1%
10 80 7250 12.96 0.1%
10 81 7298 11.52 0.1%
10 82 7396 13.58 0.1%
10 83 7467 4.586 0.0%
10 84 7541 9.998 0.1%
10 85 7639 18.91 0.2%
10 86 7697 8.466 0.1%
10 87 7795 6.715 0.0%
10 88 7869 9.861 0.1%
10 89 7950 11.08 0.1%
10 90 8025 8.003 0.0%
10 91 8096 6.154 0.0%
10 92 8185 9.002 0.1%
10 93 8262 8.386 0.1%
10 94 8356 15.16 0.1%
10 95 8553 40.66 0.4%
10 96 8525 17.63 0.2%
10 97 8628 11.42 0.1%
10 98 8671 18.36 0.2%
10 99 8752 12.58 0.1%
10 100 8815 13.63 0.1%

Quality and confidence:
param error
v 0.93
n 0.046

Model:
Time ~= 0
+ v 580.6
+ n 82.06
µs

Reads = 9 + (4 * v) + (3 * n)
Writes = 13 + (3 * v) + (0 * n)
Pallet: "pallet_staking", Extrinsic: "submit_solution_better", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 0
+ v 1.26
+ n 0.614
+ a 74.27
+ w 8.096
µs

Reads = 6 + (0 * v) + (0 * n) + (4 * a) + (1 * w)
Writes = 2 + (0 * v) + (0 * n) + (0 * a) + (0 * w)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
v n a w mean µs sigma µs %
200 1000 400 100 30740 60.38 0.1%
204 1000 400 100 30720 21.44 0.0%
208 1000 400 100 30740 92.62 0.3%
212 1000 400 100 30700 41.87 0.1%
216 1000 400 100 30810 70.54 0.2%
220 1000 400 100 30600 65.58 0.2%
224 1000 400 100 30670 53.36 0.1%
228 1000 400 100 30670 25.19 0.0%
232 1000 400 100 30570 54.77 0.1%
236 1000 400 100 30730 50.54 0.1%
240 1000 400 100 30790 56.8 0.1%
244 1000 400 100 30700 36.67 0.1%
248 1000 400 100 30730 45.53 0.1%
252 1000 400 100 30710 46.14 0.1%
256 1000 400 100 30710 30.75 0.1%
260 1000 400 100 30780 45.42 0.1%
264 1000 400 100 30820 53.49 0.1%
268 1000 400 100 30700 34.86 0.1%
272 1000 400 100 30770 48.4 0.1%
276 1000 400 100 30840 50.86 0.1%
280 1000 400 100 30870 36.1 0.1%
284 1000 400 100 30830 23.95 0.0%
288 1000 400 100 30880 35.09 0.1%
292 1000 400 100 30930 65.8 0.2%
296 1000 400 100 30880 41.44 0.1%
300 1000 400 100 30780 52.72 0.1%
304 1000 400 100 30840 38.64 0.1%
308 1000 400 100 30790 43.22 0.1%
312 1000 400 100 30680 45.01 0.1%
316 1000 400 100 30620 37.31 0.1%
320 1000 400 100 30830 50.86 0.1%
324 1000 400 100 30920 41.88 0.1%
328 1000 400 100 30790 58.66 0.1%
332 1000 400 100 30740 33.37 0.1%
336 1000 400 100 30920 33.91 0.1%
340 1000 400 100 30740 53.61 0.1%
344 1000 400 100 30920 39.72 0.1%
348 1000 400 100 30700 73.63 0.2%
352 1000 400 100 30820 44.1 0.1%
356 1000 400 100 30820 64.94 0.2%
360 1000 400 100 30950 48.26 0.1%
364 1000 400 100 30850 48.24 0.1%
368 1000 400 100 30810 55.01 0.1%
372 1000 400 100 30860 39.11 0.1%
376 1000 400 100 30880 47.39 0.1%
380 1000 400 100 30930 44.2 0.1%
384 1000 400 100 30850 37.66 0.1%
388 1000 400 100 31000 42.09 0.1%
392 1000 400 100 31020 36.93 0.1%
396 1000 400 100 30790 47.79 0.1%
400 500 400 100 30620 29.1 0.0%
400 510 400 100 30610 54.93 0.1%
400 520 400 100 30620 47.45 0.1%
400 530 400 100 30570 59.23 0.1%
400 540 400 100 30740 56.63 0.1%
400 550 400 100 30700 42.58 0.1%
400 560 400 100 30610 33.86 0.1%
400 570 400 100 30710 55 0.1%
400 580 400 100 30560 40.83 0.1%
400 590 400 100 30620 53.49 0.1%
400 600 400 100 30630 56.03 0.1%
400 610 400 100 30520 45.21 0.1%
400 620 400 100 30630 56.82 0.1%
400 630 400 100 30780 53.17 0.1%
400 640 400 100 30880 44.45 0.1%
400 650 400 100 30710 48.16 0.1%
400 660 400 100 30910 54.63 0.1%
400 670 400 100 30690 53.26 0.1%
400 680 400 100 30770 48.23 0.1%
400 690 400 100 30740 25.93 0.0%
400 700 400 100 30700 51.78 0.1%
400 710 400 100 30660 50.86 0.1%
400 720 400 100 30710 46.67 0.1%
400 730 400 100 30760 30.54 0.0%
400 740 400 100 30770 43.84 0.1%
400 750 400 100 30790 57.36 0.1%
400 760 400 100 30760 54.46 0.1%
400 770 400 100 30860 44.44 0.1%
400 780 400 100 30890 59.07 0.1%
400 790 400 100 30770 43.35 0.1%
400 800 400 100 30860 38.04 0.1%
400 810 400 100 30750 22.93 0.0%
400 820 400 100 30820 51.97 0.1%
400 830 400 100 30880 54.83 0.1%
400 840 400 100 30910 65.15 0.2%
400 850 400 100 30840 39 0.1%
400 860 400 100 30810 62.7 0.2%
400 870 400 100 30870 25.86 0.0%
400 880 400 100 30820 34.09 0.1%
400 890 400 100 30870 58.72 0.1%
400 900 400 100 30730 77.46 0.2%
400 910 400 100 30840 110.1 0.3%
400 920 400 100 30940 53.89 0.1%
400 930 400 100 30680 35.98 0.1%
400 940 400 100 30860 50.5 0.1%
400 950 400 100 30780 46.99 0.1%
400 960 400 100 30880 46.9 0.1%
400 970 400 100 30810 46.24 0.1%
400 980 400 100 30840 56.89 0.1%
400 990 400 100 30960 41.46 0.1%
400 1000 200 100 15980 26.71 0.1%
400 1000 204 100 16260 36.61 0.2%
400 1000 208 100 16620 42.48 0.2%
400 1000 212 100 16890 33.2 0.1%
400 1000 216 100 17190 23.01 0.1%
400 1000 220 100 17620 33.06 0.1%
400 1000 224 100 17880 29.29 0.1%
400 1000 228 100 18190 37.94 0.2%
400 1000 232 100 18490 23.78 0.1%
400 1000 236 100 18780 33.03 0.1%
400 1000 240 100 19100 23.44 0.1%
400 1000 244 100 19430 56.33 0.2%
400 1000 248 100 19710 32.58 0.1%
400 1000 252 100 20010 40.35 0.2%
400 1000 256 100 20300 43.29 0.2%
400 1000 260 100 20570 27.83 0.1%
400 1000 264 100 20920 42.09 0.2%
400 1000 268 100 21200 21.1 0.0%
400 1000 272 100 21480 30.12 0.1%
400 1000 276 100 21810 30.62 0.1%
400 1000 280 100 22050 42.8 0.1%
400 1000 284 100 22380 37 0.1%
400 1000 288 100 22640 35.3 0.1%
400 1000 292 100 22990 18.08 0.0%
400 1000 296 100 23250 32.65 0.1%
400 1000 300 100 23540 47.15 0.2%
400 1000 304 100 23840 58.66 0.2%
400 1000 308 100 24100 45.23 0.1%
400 1000 312 100 24410 30.97 0.1%
400 1000 316 100 24710 33.74 0.1%
400 1000 320 100 24980 53.48 0.2%
400 1000 324 100 25250 29.3 0.1%
400 1000 328 100 25570 25.04 0.0%
400 1000 332 100 25870 20.12 0.0%
400 1000 336 100 26190 24.38 0.0%
400 1000 340 100 26480 51.86 0.1%
400 1000 344 100 26730 39.19 0.1%
400 1000 348 100 27050 47.97 0.1%
400 1000 352 100 27360 45.45 0.1%
400 1000 356 100 27660 52.46 0.1%
400 1000 360 100 27960 50.66 0.1%
400 1000 364 100 28260 42.67 0.1%
400 1000 368 100 28550 38.94 0.1%
400 1000 372 100 28850 37.52 0.1%
400 1000 376 100 29140 42.31 0.1%
400 1000 380 100 29430 45.72 0.1%
400 1000 384 100 29730 49.6 0.1%
400 1000 388 100 29990 38.15 0.1%
400 1000 392 100 30320 36.15 0.1%
400 1000 396 100 30680 50.99 0.1%
400 1000 400 16 30310 40.74 0.1%
400 1000 400 17 30060 62.01 0.2%
400 1000 400 18 30280 61.41 0.2%
400 1000 400 19 30280 39.99 0.1%
400 1000 400 20 30270 32.99 0.1%
400 1000 400 21 30340 47.17 0.1%
400 1000 400 22 30410 71.36 0.2%
400 1000 400 23 30490 43.84 0.1%
400 1000 400 24 30420 47.25 0.1%
400 1000 400 25 30410 60.03 0.1%
400 1000 400 26 30210 50.62 0.1%
400 1000 400 27 30160 64.76 0.2%
400 1000 400 28 30180 41.89 0.1%
400 1000 400 29 30410 46.21 0.1%
400 1000 400 30 30430 55.65 0.1%
400 1000 400 31 30300 70.9 0.2%
400 1000 400 32 30380 45.8 0.1%
400 1000 400 33 30480 37.76 0.1%
400 1000 400 34 30490 71.22 0.2%
400 1000 400 35 30310 42.24 0.1%
400 1000 400 36 30470 36.61 0.1%
400 1000 400 37 30570 52.07 0.1%
400 1000 400 38 30450 46.59 0.1%
400 1000 400 39 30400 45.72 0.1%
400 1000 400 40 30510 44.83 0.1%
400 1000 400 41 30420 31.38 0.1%
400 1000 400 42 30560 66.01 0.2%
400 1000 400 43 30550 52.03 0.1%
400 1000 400 44 30590 77.69 0.2%
400 1000 400 45 30490 32.61 0.1%
400 1000 400 46 30690 49.79 0.1%
400 1000 400 47 30560 32.82 0.1%
400 1000 400 48 30460 43.33 0.1%
400 1000 400 49 30500 47.2 0.1%
400 1000 400 50 30430 55.74 0.1%
400 1000 400 51 30550 70.05 0.2%
400 1000 400 52 30470 54.27 0.1%
400 1000 400 53 30560 35.7 0.1%
400 1000 400 54 30470 27.01 0.0%
400 1000 400 55 30510 73.61 0.2%
400 1000 400 56 30560 54.67 0.1%
400 1000 400 57 30550 51.97 0.1%
400 1000 400 58 30580 38.05 0.1%
400 1000 400 59 30590 24.22 0.0%
400 1000 400 60 30560 65.35 0.2%
400 1000 400 61 30680 27.67 0.0%
400 1000 400 62 30570 52.31 0.1%
400 1000 400 63 30680 35.65 0.1%
400 1000 400 64 30580 79.98 0.2%
400 1000 400 65 30750 14.94 0.0%
400 1000 400 66 30680 42.74 0.1%
400 1000 400 67 30650 75.28 0.2%
400 1000 400 68 30570 38.6 0.1%
400 1000 400 69 30750 86.35 0.2%
400 1000 400 70 30680 68.37 0.2%
400 1000 400 71 30710 39.06 0.1%
400 1000 400 72 30770 49.96 0.1%
400 1000 400 73 30580 38.41 0.1%
400 1000 400 74 30680 40.08 0.1%
400 1000 400 75 30790 31.34 0.1%
400 1000 400 76 30730 40.89 0.1%
400 1000 400 77 30600 44.56 0.1%
400 1000 400 78 30840 81.83 0.2%
400 1000 400 79 30720 51.74 0.1%
400 1000 400 80 30910 19.3 0.0%
400 1000 400 81 30840 48.91 0.1%
400 1000 400 82 30810 36.32 0.1%
400 1000 400 83 30730 43.16 0.1%
400 1000 400 84 30860 25.99 0.0%
400 1000 400 85 30830 41.17 0.1%
400 1000 400 86 30830 70.83 0.2%
400 1000 400 87 30940 41.61 0.1%
400 1000 400 88 30870 34.64 0.1%
400 1000 400 89 30890 63.66 0.2%
400 1000 400 90 30800 61.88 0.2%
400 1000 400 91 30840 37.71 0.1%
400 1000 400 92 30990 34.25 0.1%
400 1000 400 93 30820 62.03 0.2%
400 1000 400 94 30940 44.7 0.1%
400 1000 400 95 30940 38.6 0.1%
400 1000 400 96 30840 86.29 0.2%
400 1000 400 97 30800 59.45 0.1%
400 1000 400 98 30840 70.05 0.2%
400 1000 400 99 30900 30.53 0.0%
400 1000 400 100 30990 43.38 0.1%

Quality and confidence:
param error
v 0.042
n 0.017
a 0.042
w 0.088

Model:
Time ~= 0
+ v 1.211
+ n 0.608
+ a 74.09
+ w 7.78
µs

Reads = 6 + (0 * v) + (0 * n) + (4 * a) + (1 * w)
Writes = 2 + (0 * v) + (0 * n) + (0 * a) + (0 * w)
Pallet: "pallet_staking", Extrinsic: "get_npos_voters", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 0
+ v 28.39
+ n 65
+ s 23.7
µs

Reads = 2 + (4 * v) + (3 * n) + (0 * s)
Writes = 0 + (0 * v) + (0 * n) + (0 * s)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
v n s mean µs sigma µs %
200 400 20 32020 43.63 0.1%
204 400 20 31930 39.45 0.1%
208 400 20 31960 77.62 0.2%
212 400 20 31990 45.39 0.1%
216 400 20 32120 46.11 0.1%
220 400 20 32280 71.32 0.2%
224 400 20 32270 69.24 0.2%
228 400 20 32550 69.67 0.2%
232 400 20 32670 64.79 0.1%
236 400 20 32830 50.86 0.1%
240 400 20 32830 53.24 0.1%
244 400 20 33080 87.89 0.2%
248 400 20 33060 34.36 0.1%
252 400 20 33130 46.87 0.1%
256 400 20 32940 87.75 0.2%
260 400 20 33280 36.58 0.1%
264 400 20 33700 91.9 0.2%
268 400 20 33550 52.63 0.1%
272 400 20 33840 42.88 0.1%
276 400 20 34020 110.1 0.3%
280 400 20 33860 69.16 0.2%
284 400 20 34320 93.42 0.2%
288 400 20 34240 43.74 0.1%
292 400 20 34450 70.86 0.2%
296 400 20 34560 66.99 0.1%
300 400 20 34040 58.27 0.1%
304 400 20 34550 57.87 0.1%
308 400 20 34910 67.59 0.1%
312 400 20 35080 38.98 0.1%
316 400 20 35290 71.89 0.2%
320 400 20 35000 74.25 0.2%
324 400 20 35340 96.29 0.2%
328 400 20 35610 35.12 0.0%
332 400 20 35060 33.04 0.0%
336 400 20 35360 126.6 0.3%
340 400 20 35740 52.27 0.1%
344 400 20 35590 51.95 0.1%
348 400 20 35930 68.88 0.1%
352 400 20 36100 130.1 0.3%
356 400 20 36050 40.91 0.1%
360 400 20 36410 59.51 0.1%
364 400 20 36260 84.88 0.2%
368 400 20 36150 66.16 0.1%
372 400 20 36520 44.49 0.1%
376 400 20 36770 97.57 0.2%
380 400 20 36780 94.83 0.2%
384 400 20 37050 60.75 0.1%
388 400 20 37140 86.87 0.2%
392 400 20 36940 74.85 0.2%
396 400 20 37580 77.96 0.2%
400 200 20 24070 43.86 0.1%
400 204 20 24710 79.56 0.3%
400 208 20 24920 97.3 0.3%
400 212 20 25300 51.73 0.2%
400 216 20 25190 62.24 0.2%
400 220 20 25710 58.46 0.2%
400 224 20 25700 62.97 0.2%
400 228 20 26290 57.04 0.2%
400 232 20 26410 59.6 0.2%
400 236 20 26770 35.1 0.1%
400 240 20 26870 64.76 0.2%
400 244 20 27230 73.31 0.2%
400 248 20 27160 48.74 0.1%
400 252 20 27900 58.35 0.2%
400 256 20 27900 112.1 0.4%
400 260 20 28060 80.72 0.2%
400 264 20 28220 65.44 0.2%
400 268 20 28760 68.06 0.2%
400 272 20 29060 79.76 0.2%
400 276 20 29430 48.65 0.1%
400 280 20 29590 75.9 0.2%
400 284 20 29620 101.5 0.3%
400 288 20 29650 52.51 0.1%
400 292 20 30330 71.81 0.2%
400 296 20 30610 60.71 0.1%
400 300 20 30800 45.23 0.1%
400 304 20 30930 79.59 0.2%
400 308 20 31380 50.35 0.1%
400 312 20 31330 102 0.3%
400 316 20 31730 57.78 0.1%
400 320 20 31870 51.82 0.1%
400 324 20 32330 97.28 0.3%
400 328 20 32470 59.24 0.1%
400 332 20 32970 79.22 0.2%
400 336 20 33200 49.42 0.1%
400 340 20 33390 58.63 0.1%
400 344 20 33380 71.52 0.2%
400 348 20 33960 58.09 0.1%
400 352 20 34370 63.24 0.1%
400 356 20 34140 107.4 0.3%
400 360 20 34680 41.49 0.1%
400 364 20 34690 55.92 0.1%
400 368 20 35140 66.04 0.1%
400 372 20 35340 71.97 0.2%
400 376 20 35700 94.06 0.2%
400 380 20 36150 52.44 0.1%
400 384 20 36470 160.3 0.4%
400 388 20 36390 70.23 0.1%
400 392 20 36900 91.01 0.2%
400 396 20 36930 70.16 0.1%
400 400 1 36820 65.82 0.1%
400 400 2 37060 103 0.2%
400 400 3 36960 81.74 0.2%
400 400 4 37080 90.92 0.2%
400 400 5 37140 89.67 0.2%
400 400 6 37140 133.9 0.3%
400 400 7 37150 118.9 0.3%
400 400 8 36780 57.31 0.1%
400 400 9 37230 161.1 0.4%
400 400 10 36990 69.84 0.1%
400 400 11 37330 87.12 0.2%
400 400 12 37260 80.31 0.2%
400 400 13 37100 79.84 0.2%
400 400 14 37040 107.2 0.2%
400 400 15 37040 51.43 0.1%
400 400 16 37160 33.8 0.0%
400 400 17 37390 89.12 0.2%
400 400 18 37270 133.6 0.3%
400 400 19 37100 94.63 0.2%
400 400 20 37520 68.1 0.1%

Quality and confidence:
param error
v 0.102
n 0.102
s 1.4

Model:
Time ~= 0
+ v 27.75
+ n 65.21
+ s 18.98
µs

Reads = 2 + (4 * v) + (3 * n) + (0 * s)
Writes = 0 + (0 * v) + (0 * n) + (0 * s)
Pallet: "pallet_staking", Extrinsic: "get_npos_targets", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 0
+ v 10.68
µs

Reads = 1 + (1 * v)
Writes = 0 + (0 * v)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
v mean µs sigma µs %
200 2120 14.79 0.6%
204 2194 9.953 0.4%
208 2185 14.65 0.6%
212 2266 15.14 0.6%
216 2320 10.47 0.4%
220 2286 22.25 0.9%
224 2369 19.61 0.8%
228 2433 20.86 0.8%
232 2446 10.48 0.4%
236 2516 13.57 0.5%
240 2604 14.93 0.5%
244 2594 22.57 0.8%
248 2646 9.729 0.3%
252 2647 12.56 0.4%
256 2734 18.66 0.6%
260 2787 19.05 0.6%
264 2774 9.363 0.3%
268 2835 15 0.5%
272 2859 14.37 0.5%
276 3003 20.22 0.6%
280 3002 12.6 0.4%
284 3055 22.94 0.7%
288 3093 18.12 0.5%
292 3085 16.75 0.5%
296 3142 16.78 0.5%
300 3241 24.52 0.7%
304 3290 22.08 0.6%
308 3338 22.26 0.6%
312 3298 15.55 0.4%
316 3331 13.07 0.3%
320 3387 19.94 0.5%
324 3448 15.59 0.4%
328 3486 19.5 0.5%
332 3477 11.81 0.3%
336 3595 17.25 0.4%
340 3651 24.05 0.6%
344 3662 22.47 0.6%
348 3673 34.08 0.9%
352 3732 25.23 0.6%
356 3737 7.539 0.2%
360 3800 22.43 0.5%
364 3857 16.59 0.4%
368 3929 17.12 0.4%
372 4044 20.28 0.5%
376 3978 16.83 0.4%
380 4001 14.15 0.3%
384 4125 22.44 0.5%
388 4111 13.62 0.3%
392 4205 22.18 0.5%
396 4280 41.9 0.9%
400 4291 23.63 0.5%

Quality and confidence:
param error
v 0.029

Model:
Time ~= 0
+ v 10.7
µs

Reads = 1 + (1 * v)
Writes = 0 + (0 * v)

…n=westend-dev --steps=50 --repeat=20 --pallet=pallet_staking --extrinsic=* --execution=wasm --wasm-execution=compiled --heap-pages=4096 --header=./file_header.txt --output=./runtime/westend/src/weights/

runtime/kusama/src/weights/pallet_staking.rs

… into kiz-increase-payouts

runtime/kusama/src/lib.rs

kianenigma · 2021-03-23T08:50:45Z

bot merge

ghost · 2021-03-23T08:50:49Z

Missing process info; check that the PR belongs to a project column.

Merge can be attempted if:

The PR has approval from two core-devs (or one if the PR is labelled insubstantial).
The PR has approval from a member of substrateteamleads.
The PR is attached to a project column and has approval from the project owner.

See https://github.com/paritytech/parity-processbot#faq

kianenigma · 2021-03-23T08:52:54Z

The PR has approval from a member of substrateteamleads.

@shawntabrizi is a team lead but actually is not a member there, manual merging for now.

* CI: fix env file location * Increase payable nominators per validator (#2637) * inc * cargo run --release --features=runtime-benchmarks -- benchmark --chain=polkadot-dev --steps=50 --repeat=20 --pallet=pallet_staking --extrinsic=* --execution=wasm --wasm-execution=compiled --heap-pages=4096 --header=./file_header.txt --output=./runtime/polkadot/src/weights/ * cargo run --release --features=runtime-benchmarks -- benchmark --chain=kusama-dev --steps=50 --repeat=20 --pallet=pallet_staking --extrinsic=* --execution=wasm --wasm-execution=compiled --heap-pages=4096 --header=./file_header.txt --output=./runtime/kusama/src/weights/ * cargo run --release --features=runtime-benchmarks -- benchmark --chain=westend-dev --steps=50 --repeat=20 --pallet=pallet_staking --extrinsic=* --execution=wasm --wasm-execution=compiled --heap-pages=4096 --header=./file_header.txt --output=./runtime/westend/src/weights/ * Add test Co-authored-by: Parity Benchmarking Bot <admin@parity.io> * Add debug messaes to the bridge actions (#2668) * more approval voting instrumentation (#2663) * more approval voting instrumentation * fix `unapproved_candidates` * Update node/core/approval-voting/src/lib.rs Co-authored-by: Bastian Köcher <bkchr@users.noreply.github.com> * Add Parachain Onboarding to Rococo (#2660) * Add Parachain Onboarding Pallets to Rococo * remove propose_parachain * add back para sudo wrapper * Revert "remove propose_parachain" This reverts commit 38b7c33. * turn propose_parachains into validator_manager * Update runtime/rococo/src/lib.rs Co-authored-by: Bastian Köcher <bkchr@users.noreply.github.com> * update origin Co-authored-by: Bastian Köcher <bkchr@users.noreply.github.com> * Improve logging (#2669) * Improve logging * Review feedback * Fix some warning and some further logging changes * Fix up CI for new labels (#2673) * Fix up CI/CD for the new labels. * Fix labels * [CI] Require D*-audit labels for any runtime changes (#2617) * update generate_release_text.rb * add checks for audit labels for runtime changes * fix check_labels.sh * use has_runtime_changes in check_runtime.sh * add D1-trivial label * fix check_labels.sh * Tweak Rococo Settings For Onboarding Testing (#2671) * Tweak Rococo Settings * rococo: use CurrentBlockRandomness wrapper in auctions Co-authored-by: André Silva <andrerfosilva@gmail.com> Co-authored-by: Bastian Köcher <info@kchr.de> * Fix check-labels.yml for forked repos (#2677) * fix check-labels.yml for forked repos * update labels * CI: pass VERSION variable to SimNet * CI: chore * CI: fix env file location * CI: pass VERSION variable to SimNet * CI: chore Co-authored-by: Kian Paimani <5588131+kianenigma@users.noreply.github.com> Co-authored-by: Parity Benchmarking Bot <admin@parity.io> Co-authored-by: Pierre Krieger <pierre.krieger1708@gmail.com> Co-authored-by: Robert Habermeier <rphmeier@gmail.com> Co-authored-by: Bastian Köcher <bkchr@users.noreply.github.com> Co-authored-by: Shawn Tabrizi <shawntabrizi@gmail.com> Co-authored-by: Gavin Wood <gavin@parity.io> Co-authored-by: Martin Pugh <pugh@s3kr.it> Co-authored-by: André Silva <andrerfosilva@gmail.com> Co-authored-by: Bastian Köcher <info@kchr.de>

inc

be45c2b

kianenigma added A0-please_review Pull request needs code review. B7-runtimenoteworthy C1-low PR touches the given topic and has a low impact on builders. labels Mar 17, 2021

kianenigma added this to the v0.8.30 milestone Mar 17, 2021

kianenigma changed the title ~~inc~~ Increase payable nominators per validator Mar 17, 2021

paritytech-ci added the D2-breaksapi label Mar 17, 2021

cargo run --release --features=runtime-benchmarks -- benchmark --chai…

f2e979e

…n=polkadot-dev --steps=50 --repeat=20 --pallet=pallet_staking --extrinsic=* --execution=wasm --wasm-execution=compiled --heap-pages=4096 --header=./file_header.txt --output=./runtime/polkadot/src/weights/

cargo run --release --features=runtime-benchmarks -- benchmark --chai…

6728b87

…n=kusama-dev --steps=50 --repeat=20 --pallet=pallet_staking --extrinsic=* --execution=wasm --wasm-execution=compiled --heap-pages=4096 --header=./file_header.txt --output=./runtime/kusama/src/weights/

cargo run --release --features=runtime-benchmarks -- benchmark --chai…

52b3971

…n=westend-dev --steps=50 --repeat=20 --pallet=pallet_staking --extrinsic=* --execution=wasm --wasm-execution=compiled --heap-pages=4096 --header=./file_header.txt --output=./runtime/westend/src/weights/

shawntabrizi reviewed Mar 17, 2021

View reviewed changes

runtime/kusama/src/weights/pallet_staking.rs Show resolved Hide resolved

kianenigma added 3 commits March 18, 2021 09:30

Add test

e401b5e

Merge branch 'kiz-increase-payouts' of github.com:paritytech/polkadot…

67eee03

… into kiz-increase-payouts

Master.into()

f07c35e

shawntabrizi approved these changes Mar 23, 2021

View reviewed changes

shawntabrizi reviewed Mar 23, 2021

View reviewed changes

runtime/kusama/src/lib.rs Show resolved Hide resolved

kianenigma mentioned this pull request Mar 23, 2021

Payout function should refund paritytech/substrate#8428

Closed

kianenigma merged commit a2a362b into master Mar 23, 2021

kianenigma deleted the kiz-increase-payouts branch March 23, 2021 08:53

kianenigma added the D5-nicetohaveaudit ⚠️ PR contains trivial changes to logic that should be properly reviewed. label Mar 23, 2021

dubstard mentioned this pull request Apr 10, 2021

Update all.json polkadot-js/phishing#236

Merged

redzsina added D1-audited 👍 PR contains changes to critical logic that has been properly reviewed and externally audited. and removed D5-nicetohaveaudit ⚠️ PR contains trivial changes to logic that should be properly reviewed. labels Apr 20, 2021

Increase payable nominators per validator #2637

Increase payable nominators per validator #2637

Conversation

kianenigma commented Mar 17, 2021

kianenigma commented Mar 17, 2021

parity-benchapp bot commented Mar 17, 2021 • edited

Pallet: "pallet_staking", Extrinsic: "bond", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

Reads = 5 Writes = 4 Min Squares Analysis

Reads = 5 Writes = 4 Pallet: "pallet_staking", Extrinsic: "bond_extra", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

Reads = 4 Writes = 2 Min Squares Analysis

Reads = 4 Writes = 2 Pallet: "pallet_staking", Extrinsic: "unbond", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

Reads = 5 Writes = 3 Min Squares Analysis

Reads = 5 Writes = 3 Pallet: "pallet_staking", Extrinsic: "withdraw_unbonded_update", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

Reads = 5 + (0 * s) Writes = 3 + (0 * s) Min Squares Analysis

Reads = 5 + (0 * s) Writes = 3 + (0 * s) Pallet: "pallet_staking", Extrinsic: "withdraw_unbonded_kill", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

Reads = 7 + (0 * s) Writes = 8 + (1 * s) Min Squares Analysis

Reads = 7 + (0 * s) Writes = 8 + (1 * s) Pallet: "pallet_staking", Extrinsic: "validate", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

Reads = 2 Writes = 2 Min Squares Analysis

Reads = 2 Writes = 2 Pallet: "pallet_staking", Extrinsic: "kick", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

Reads = 2 + (1 * k) Writes = 0 + (1 * k) Min Squares Analysis

Reads = 2 + (1 * k) Writes = 0 + (1 * k) Pallet: "pallet_staking", Extrinsic: "nominate", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

Reads = 4 + (1 * n) Writes = 2 + (0 * n) Min Squares Analysis

Reads = 4 + (1 * n) Writes = 2 + (0 * n) Pallet: "pallet_staking", Extrinsic: "chill", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

Reads = 2 Writes = 2 Min Squares Analysis

Reads = 2 Writes = 2 Pallet: "pallet_staking", Extrinsic: "set_payee", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

Reads = 1 Writes = 1 Min Squares Analysis

Reads = 1 Writes = 1 Pallet: "pallet_staking", Extrinsic: "set_controller", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

Reads = 3 Writes = 3 Min Squares Analysis

Reads = 3 Writes = 3 Pallet: "pallet_staking", Extrinsic: "set_validator_count", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

Reads = 0 Writes = 1 Min Squares Analysis

Reads = 0 Writes = 1 Pallet: "pallet_staking", Extrinsic: "force_no_eras", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

Reads = 0 Writes = 1 Min Squares Analysis

Reads = 0 Writes = 1 Pallet: "pallet_staking", Extrinsic: "force_new_era", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

Reads = 0 Writes = 1 Min Squares Analysis

Reads = 0 Writes = 1 Pallet: "pallet_staking", Extrinsic: "force_new_era_always", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

Reads = 0 Writes = 1 Min Squares Analysis

Reads = 0 Writes = 1 Pallet: "pallet_staking", Extrinsic: "set_invulnerables", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

Reads = 0 + (0 * v) Writes = 1 + (0 * v) Min Squares Analysis

Reads = 0 + (0 * v) Writes = 1 + (0 * v) Pallet: "pallet_staking", Extrinsic: "force_unstake", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

Reads = 4 + (0 * s) Writes = 8 + (1 * s) Min Squares Analysis

Reads = 4 + (0 * s) Writes = 8 + (1 * s) Pallet: "pallet_staking", Extrinsic: "cancel_deferred_slash", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

Reads = 1 + (0 * s) Writes = 1 + (0 * s) Min Squares Analysis

Reads = 1 + (0 * s) Writes = 1 + (0 * s) Pallet: "pallet_staking", Extrinsic: "payout_stakers_dead_controller", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

Reads = 11 + (3 * n) Writes = 2 + (1 * n) Min Squares Analysis

Reads = 11 + (3 * n) Writes = 2 + (1 * n) Pallet: "pallet_staking", Extrinsic: "payout_stakers_alive_staked", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

Reads = 12 + (5 * n) Writes = 3 + (3 * n) Min Squares Analysis

Reads = 12 + (5 * n) Writes = 3 + (3 * n) Pallet: "pallet_staking", Extrinsic: "rebond", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

Reads = 4 + (0 * l) Writes = 3 + (0 * l) Min Squares Analysis

Reads = 4 + (0 * l) Writes = 3 + (0 * l) Pallet: "pallet_staking", Extrinsic: "set_history_depth", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

Reads = 2 + (0 * e) Writes = 4 + (7 * e) Min Squares Analysis

Reads = 2 + (0 * e) Writes = 4 + (7 * e) Pallet: "pallet_staking", Extrinsic: "reap_stash", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

Reads = 4 + (0 * s) Writes = 8 + (1 * s) Min Squares Analysis

Reads = 4 + (0 * s) Writes = 8 + (1 * s) Pallet: "pallet_staking", Extrinsic: "new_era", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

Reads = 9 + (4 * v) + (3 * n) Writes = 13 + (3 * v) + (0 * n) Min Squares Analysis

Reads = 9 + (4 * v) + (3 * n) Writes = 13 + (3 * v) + (0 * n) Pallet: "pallet_staking", Extrinsic: "submit_solution_better", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

Reads = 6 + (0 * v) + (0 * n) + (4 * a) + (1 * w) Writes = 2 + (0 * v) + (0 * n) + (0 * a) + (0 * w) Min Squares Analysis

Reads = 6 + (0 * v) + (0 * n) + (4 * a) + (1 * w) Writes = 2 + (0 * v) + (0 * n) + (0 * a) + (0 * w) Pallet: "pallet_staking", Extrinsic: "get_npos_voters", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

Reads = 2 + (4 * v) + (3 * n) + (0 * s) Writes = 0 + (0 * v) + (0 * n) + (0 * s) Min Squares Analysis

Reads = 2 + (4 * v) + (3 * n) + (0 * s) Writes = 0 + (0 * v) + (0 * n) + (0 * s) Pallet: "pallet_staking", Extrinsic: "get_npos_targets", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

Reads = 1 + (1 * v) Writes = 0 + (0 * v) Min Squares Analysis

kianenigma commented Mar 17, 2021

kianenigma commented Mar 17, 2021

parity-benchapp bot commented Mar 17, 2021 • edited

Pallet: "pallet_staking", Extrinsic: "bond", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

Reads = 5 Writes = 4 Min Squares Analysis

Reads = 5 Writes = 4 Pallet: "pallet_staking", Extrinsic: "bond_extra", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

Reads = 4 Writes = 2 Min Squares Analysis

Reads = 4 Writes = 2 Pallet: "pallet_staking", Extrinsic: "unbond", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

Reads = 5 Writes = 3 Min Squares Analysis

Reads = 5 Writes = 3 Pallet: "pallet_staking", Extrinsic: "withdraw_unbonded_update", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

Reads = 5 + (0 * s) Writes = 3 + (0 * s) Min Squares Analysis

Reads = 5 + (0 * s) Writes = 3 + (0 * s) Pallet: "pallet_staking", Extrinsic: "withdraw_unbonded_kill", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

Reads = 7 + (0 * s) Writes = 8 + (1 * s) Min Squares Analysis

Reads = 7 + (0 * s) Writes = 8 + (1 * s) Pallet: "pallet_staking", Extrinsic: "validate", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

Reads = 2 Writes = 2 Min Squares Analysis

Reads = 2 Writes = 2 Pallet: "pallet_staking", Extrinsic: "kick", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

Reads = 2 + (1 * k) Writes = 0 + (1 * k) Min Squares Analysis

Reads = 2 + (1 * k) Writes = 0 + (1 * k) Pallet: "pallet_staking", Extrinsic: "nominate", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

Reads = 4 + (1 * n) Writes = 2 + (0 * n) Min Squares Analysis

Reads = 4 + (1 * n) Writes = 2 + (0 * n) Pallet: "pallet_staking", Extrinsic: "chill", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20 Median Slopes Analysis

parity-benchapp bot commented Mar 17, 2021 •

edited

Pallet: "pallet_staking", Extrinsic: "bond", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 5
Writes = 4
Min Squares Analysis

Reads = 5
Writes = 4
Pallet: "pallet_staking", Extrinsic: "bond_extra", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 4
Writes = 2
Min Squares Analysis

Reads = 4
Writes = 2
Pallet: "pallet_staking", Extrinsic: "unbond", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 5
Writes = 3
Min Squares Analysis

Reads = 5
Writes = 3
Pallet: "pallet_staking", Extrinsic: "withdraw_unbonded_update", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 5 + (0 * s)
Writes = 3 + (0 * s)
Min Squares Analysis

Reads = 5 + (0 * s)
Writes = 3 + (0 * s)
Pallet: "pallet_staking", Extrinsic: "withdraw_unbonded_kill", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 7 + (0 * s)
Writes = 8 + (1 * s)
Min Squares Analysis

Reads = 7 + (0 * s)
Writes = 8 + (1 * s)
Pallet: "pallet_staking", Extrinsic: "validate", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 2
Writes = 2
Min Squares Analysis

Reads = 2
Writes = 2
Pallet: "pallet_staking", Extrinsic: "kick", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 2 + (1 * k)
Writes = 0 + (1 * k)
Min Squares Analysis

Reads = 2 + (1 * k)
Writes = 0 + (1 * k)
Pallet: "pallet_staking", Extrinsic: "nominate", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 4 + (1 * n)
Writes = 2 + (0 * n)
Min Squares Analysis

Reads = 4 + (1 * n)
Writes = 2 + (0 * n)
Pallet: "pallet_staking", Extrinsic: "chill", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 2
Writes = 2
Min Squares Analysis

Reads = 2
Writes = 2
Pallet: "pallet_staking", Extrinsic: "set_payee", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 1
Writes = 1
Min Squares Analysis

Reads = 1
Writes = 1
Pallet: "pallet_staking", Extrinsic: "set_controller", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 3
Writes = 3
Min Squares Analysis

Reads = 3
Writes = 3
Pallet: "pallet_staking", Extrinsic: "set_validator_count", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 0
Writes = 1
Min Squares Analysis

Reads = 0
Writes = 1
Pallet: "pallet_staking", Extrinsic: "force_no_eras", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 0
Writes = 1
Min Squares Analysis

Reads = 0
Writes = 1
Pallet: "pallet_staking", Extrinsic: "force_new_era", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 0
Writes = 1
Min Squares Analysis

Reads = 0
Writes = 1
Pallet: "pallet_staking", Extrinsic: "force_new_era_always", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 0
Writes = 1
Min Squares Analysis

Reads = 0
Writes = 1
Pallet: "pallet_staking", Extrinsic: "set_invulnerables", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 0 + (0 * v)
Writes = 1 + (0 * v)
Min Squares Analysis

Reads = 0 + (0 * v)
Writes = 1 + (0 * v)
Pallet: "pallet_staking", Extrinsic: "force_unstake", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 4 + (0 * s)
Writes = 8 + (1 * s)
Min Squares Analysis

Reads = 4 + (0 * s)
Writes = 8 + (1 * s)
Pallet: "pallet_staking", Extrinsic: "cancel_deferred_slash", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 1 + (0 * s)
Writes = 1 + (0 * s)
Min Squares Analysis

Reads = 1 + (0 * s)
Writes = 1 + (0 * s)
Pallet: "pallet_staking", Extrinsic: "payout_stakers_dead_controller", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 11 + (3 * n)
Writes = 2 + (1 * n)
Min Squares Analysis

Reads = 11 + (3 * n)
Writes = 2 + (1 * n)
Pallet: "pallet_staking", Extrinsic: "payout_stakers_alive_staked", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 12 + (5 * n)
Writes = 3 + (3 * n)
Min Squares Analysis

Reads = 12 + (5 * n)
Writes = 3 + (3 * n)
Pallet: "pallet_staking", Extrinsic: "rebond", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 4 + (0 * l)
Writes = 3 + (0 * l)
Min Squares Analysis

Reads = 4 + (0 * l)
Writes = 3 + (0 * l)
Pallet: "pallet_staking", Extrinsic: "set_history_depth", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 2 + (0 * e)
Writes = 4 + (7 * e)
Min Squares Analysis

Reads = 2 + (0 * e)
Writes = 4 + (7 * e)
Pallet: "pallet_staking", Extrinsic: "reap_stash", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 4 + (0 * s)
Writes = 8 + (1 * s)
Min Squares Analysis

Reads = 4 + (0 * s)
Writes = 8 + (1 * s)
Pallet: "pallet_staking", Extrinsic: "new_era", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 9 + (4 * v) + (3 * n)
Writes = 13 + (3 * v) + (0 * n)
Min Squares Analysis

Reads = 9 + (4 * v) + (3 * n)
Writes = 13 + (3 * v) + (0 * n)
Pallet: "pallet_staking", Extrinsic: "submit_solution_better", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 6 + (0 * v) + (0 * n) + (4 * a) + (1 * w)
Writes = 2 + (0 * v) + (0 * n) + (0 * a) + (0 * w)
Min Squares Analysis

Reads = 6 + (0 * v) + (0 * n) + (4 * a) + (1 * w)
Writes = 2 + (0 * v) + (0 * n) + (0 * a) + (0 * w)
Pallet: "pallet_staking", Extrinsic: "get_npos_voters", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 2 + (4 * v) + (3 * n) + (0 * s)
Writes = 0 + (0 * v) + (0 * n) + (0 * s)
Min Squares Analysis

Reads = 2 + (4 * v) + (3 * n) + (0 * s)
Writes = 0 + (0 * v) + (0 * n) + (0 * s)
Pallet: "pallet_staking", Extrinsic: "get_npos_targets", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 1 + (1 * v)
Writes = 0 + (0 * v)
Min Squares Analysis

parity-benchapp bot commented Mar 17, 2021 •

edited

Pallet: "pallet_staking", Extrinsic: "bond", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 5
Writes = 4
Min Squares Analysis

Reads = 5
Writes = 4
Pallet: "pallet_staking", Extrinsic: "bond_extra", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 4
Writes = 2
Min Squares Analysis

Reads = 4
Writes = 2
Pallet: "pallet_staking", Extrinsic: "unbond", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 5
Writes = 3
Min Squares Analysis

Reads = 5
Writes = 3
Pallet: "pallet_staking", Extrinsic: "withdraw_unbonded_update", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 5 + (0 * s)
Writes = 3 + (0 * s)
Min Squares Analysis

Reads = 5 + (0 * s)
Writes = 3 + (0 * s)
Pallet: "pallet_staking", Extrinsic: "withdraw_unbonded_kill", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 7 + (0 * s)
Writes = 8 + (1 * s)
Min Squares Analysis

Reads = 7 + (0 * s)
Writes = 8 + (1 * s)
Pallet: "pallet_staking", Extrinsic: "validate", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 2
Writes = 2
Min Squares Analysis

Reads = 2
Writes = 2
Pallet: "pallet_staking", Extrinsic: "kick", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 2 + (1 * k)
Writes = 0 + (1 * k)
Min Squares Analysis

Reads = 2 + (1 * k)
Writes = 0 + (1 * k)
Pallet: "pallet_staking", Extrinsic: "nominate", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 4 + (1 * n)
Writes = 2 + (0 * n)
Min Squares Analysis

Reads = 4 + (1 * n)
Writes = 2 + (0 * n)
Pallet: "pallet_staking", Extrinsic: "chill", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 2
Writes = 2
Min Squares Analysis

Reads = 2
Writes = 2
Pallet: "pallet_staking", Extrinsic: "set_payee", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 1
Writes = 1
Min Squares Analysis

Reads = 1
Writes = 1
Pallet: "pallet_staking", Extrinsic: "set_controller", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 3
Writes = 3
Min Squares Analysis

Reads = 3
Writes = 3
Pallet: "pallet_staking", Extrinsic: "set_validator_count", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

Reads = 0
Writes = 1
Min Squares Analysis