Fix elections-phragmen and proxy issue

[kianenigma]

Closes #7030
polkadot companion: paritytech/polkadot#1719

📕 Detailed description of the PR is here

[kianenigma]

Weight update report. Note that we only care about DB stuff and variables. These are native numbers on my machine.

vote

• before: #[ weight = 50 * WEIGHT_PER_MICROS + T::DbWeight::get().reads_writes(4, 2)]
• now:

(131493000 as Weight)
			.saturating_add(DbWeight::get().reads(5 as Weight))
			.saturating_add(DbWeight::get().writes(2 as Weight))

• comments: set_lock is both read and write of locks, and it was mis-counted before. Also before, we always submitted MAX_VOTES, now we have a variable v, so just more accurate.

remove_voter

• before: #[weight = 35 * WEIGHT_PER_MICROS + T::DbWeight::get().reads_writes(1, 2)]
• now:

(90000000 as Weight)
			.saturating_add(DbWeight::get().reads(2 as Weight))
			.saturating_add(DbWeight::get().writes(2 as Weight))

• comments: set_lock is both read and write of locks, and it was mis-counted before.

report_defunct_voter_incorrect

• before:

Weight::from(defunct.candidate_count).saturating_mul(2 * WEIGHT_PER_MICROS)
			.saturating_add(Weight::from(defunct.vote_count).saturating_mul(19 * WEIGHT_PER_MICROS))
			.saturating_add(T::DbWeight::get().reads_writes(6, 3))

• now:

(0 as Weight)
			.saturating_add((1664000 as Weight).saturating_mul(c as Weight))
			.saturating_add((42696000 as Weight).saturating_mul(v as Weight))
			.saturating_add(DbWeight::get().reads(4 as Weight))

• comments: The changes are expected. An invalid report is now a no-op. See the new refund scheme.

report_defunct_voter_incorrect

• before:

Weight::from(defunct.candidate_count).saturating_mul(2 * WEIGHT_PER_MICROS)
			.saturating_add(Weight::from(defunct.vote_count).saturating_mul(19 * WEIGHT_PER_MICROS))
			.saturating_add(T::DbWeight::get().reads_writes(6, 3))

• now:

(0 as Weight)
			.saturating_add((1613000 as Weight).saturating_mul(c as Weight))
			.saturating_add((40652000 as Weight).saturating_mul(v as Weight))
			.saturating_add(DbWeight::get().reads(6 as Weight))
			.saturating_add(DbWeight::get().writes(3 as Weight))

• comments: Excellent.

submit_candidacy

• before:

#[weight =
	(35 * WEIGHT_PER_MICROS)
	.saturating_add(Weight::from(*candidate_count).saturating_mul(375 * WEIGHT_PER_NANOS))
	.saturating_add(T::DbWeight::get().reads_writes(4, 1))
]

• now:

(169289000 as Weight)
			.saturating_add((351000 as Weight).saturating_mul(c as Weight))
			.saturating_add(DbWeight::get().reads(3 as Weight))
			.saturating_add(DbWeight::get().writes(1 as Weight))

• comments: Previous read count was wrong because we count candidates.decode_len() and candidates twice.

renounce_candidacy

• before:

#[weight =  match *renouncing {
	Renouncing::Candidate(count) => {
		(18 * WEIGHT_PER_MICROS)
		.saturating_add(Weight::from(count).saturating_mul(235 * WEIGHT_PER_NANOS))
		.saturating_add(T::DbWeight::get().reads_writes(1, 1))
	},
	Renouncing::Member => {
		46 * WEIGHT_PER_MICROS +
		T::DbWeight::get().reads_writes(2, 2)
	},
	Renouncing::RunnerUp => {
		46 * WEIGHT_PER_MICROS +
		T::DbWeight::get().reads_writes(1, 1)
	}
}]

• now:

// candidate
(106589000 as Weight)
			.saturating_add((216000 as Weight).saturating_mul(c as Weight))
			.saturating_add(DbWeight::get().reads(1 as Weight))
			.saturating_add(DbWeight::get().writes(1 as Weight))
// member 
(190000000 as Weight)
			.saturating_add(DbWeight::get().reads(3 as Weight))
			.saturating_add(DbWeight::get().writes(4 as Weight))
// runners-up
(56000000 as Weight)
			.saturating_add(DbWeight::get().reads(1 as Weight))
			.saturating_add(DbWeight::get().writes(1 as Weight))

• comments: previous r/w for member renounce was wrong, because we forgot that we also call into T::ChangeMambers which alters some state in the council module.

remove_member_without_replacement

• before:

T::MaximumBlockWeight::get()

• comments: Nothing else to do. Stays the same for now.

remove_member_with_replacement

• before:

50 * WEIGHT_PER_MICROS + T::DbWeight::get().reads_writes(3, 2)

• now:

(111000000 as Weight)
			.saturating_add(DbWeight::get().reads(4 as Weight))
			.saturating_add(DbWeight::get().writes(5 as Weight))

• comments: Similarly, calls into T::ChangeMember were ignored before.

remove_member_wrong_refund

• before:

6 * WEIGHT_PER_MICROS + T::DbWeight::get().reads_writes(1, 0)

• now:

(19000000 as Weight)
			.saturating_add(DbWeight::get().reads(1 as Weight))

• comments: All good.

[kianenigma]

Okay -- weights also re-done and pushed.

[gavofyork]

@kianenigma how about that polkadot PR?

[kianenigma]

This is changing the amount of deposit needed for each vote. Should we write a migration code and update the deposit of all previous voters as well? I guess yes.

[kianenigma]

This is queued for Runtime release 28 -- 27 is already drafted and does not contain this and will be launched next year. So Icing this in the meantime.

[kianenigma]

/benchmark runtime pallet pallet_elections_phragmen

[parity-benchapp]

Finished benchmark for branch: kiz-fix-proxy-election-drain

Benchmark: Benchmark Runtime Pallet

cargo run --release --features=runtime-benchmarks --manifest-path=bin/node/cli/Cargo.toml -- benchmark --chain=dev --steps=50 --repeat=20 --pallet=pallet_elections_phragmen --extrinsic="*" --execution=wasm --wasm-execution=compiled --heap-pages=4096 --output=./frame/elections-phragmen/src/weights.rs --template=./.maintain/frame-weight-template.hbs

Results

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

-- Extrinsic Time --

Model:
Time ~= 45.19
+ v 0.4
µs

Reads = 5 + (0 * v)
Writes = 2 + (0 * v)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
v mean µs sigma µs %
1 44.86 0.141 0.3%
2 45.79 0.054 0.1%
3 46.27 0.114 0.2%
4 46.66 0.135 0.2%
5 47.19 0.159 0.3%
6 47.93 0.051 0.1%
7 48.31 0.13 0.2%
8 48.85 0.106 0.2%
9 49.23 0.096 0.1%
10 49.36 0.103 0.2%
11 49.71 0.271 0.5%
12 49.94 0.123 0.2%
13 49.99 0.076 0.1%
14 50.37 0.143 0.2%
15 50.62 0.173 0.3%
16 51.67 0.185 0.3%

Quality and confidence:
param error
v 0.006

Model:
Time ~= 45.15
+ v 0.399
µs

Reads = 5 + (0 * v)
Writes = 2 + (0 * v)
Pallet: "pallet_elections_phragmen", Extrinsic: "vote_more", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 69.9
+ v 0.421
µs

Reads = 5 + (0 * v)
Writes = 2 + (0 * v)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
v mean µs sigma µs %
2 70.37 0.122 0.1%
3 71 0.181 0.2%
4 71.77 0.131 0.1%
5 72.14 0.101 0.1%
6 72.47 0.165 0.2%
7 72.73 0.072 0.0%
8 73.86 0.134 0.1%
9 73.9 0.122 0.1%
10 74.62 0.19 0.2%
11 74.13 0.141 0.1%
12 75.13 0.238 0.3%
13 75.01 0.257 0.3%
14 75.42 0.108 0.1%
15 76.22 0.186 0.2%
16 78.02 2.425 3.1%

Quality and confidence:
param error
v 0.014

Model:
Time ~= 69.73
+ v 0.45
µs

Reads = 5 + (0 * v)
Writes = 2 + (0 * v)
Pallet: "pallet_elections_phragmen", Extrinsic: "vote_less", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 72.35
+ v 0.372
µs

Reads = 5 + (0 * v)
Writes = 2 + (0 * v)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
v mean µs sigma µs %
2 79.17 3.364 4.2%
3 75.35 2.639 3.5%
4 71.49 0.136 0.1%
5 73.15 0.618 0.8%
6 74.53 0.081 0.1%
7 75.35 0.088 0.1%
8 75.1 0.135 0.1%
9 76.1 0.247 0.3%
10 76.2 0.191 0.2%
11 76.69 0.133 0.1%
12 76.27 0.594 0.7%
13 76.63 0.119 0.1%
14 77.29 0.261 0.3%
15 78.28 0.309 0.3%
16 78.32 0.266 0.3%

Quality and confidence:
param error
v 0.038

Model:
Time ~= 73.95
+ v 0.227
µs

Reads = 5 + (0 * v)
Writes = 2 + (0 * v)
Pallet: "pallet_elections_phragmen", Extrinsic: "remove_voter", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 68.39
µs

Reads = 2
Writes = 2
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 68.39
µs

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

-- Extrinsic Time --

Model:
Time ~= 59.31
+ c 0.399
µs

Reads = 3 + (0 * c)
Writes = 1 + (0 * c)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
c mean µs sigma µs %
1 62.77 0.51 0.8%
4 61.39 1.943 3.1%
7 64.33 0.729 1.1%
10 65.61 0.475 0.7%
13 65.58 1.128 1.7%
16 67.46 0.405 0.6%
19 65.18 0.233 0.3%
22 68.72 0.44 0.6%
25 69.48 0.699 1.0%
28 73.35 1.446 1.9%
31 72.75 1.52 2.0%
34 73.45 1.321 1.7%
37 75.07 1.514 2.0%
40 75.37 1.153 1.5%
43 79.23 2.059 2.5%
46 77.74 1.734 2.2%
49 78.71 0.893 1.1%
52 80.41 1.93 2.4%
55 83.26 2.192 2.6%
58 84 2.218 2.6%
61 84.18 1.837 2.1%
64 83.77 0.307 0.3%
67 85.29 0.261 0.3%
70 86.48 0.258 0.2%
73 87.66 0.146 0.1%
76 88.97 0.355 0.3%
79 90.35 0.454 0.5%
82 92.16 1.479 1.6%
85 93.03 1.51 1.6%
88 99.2 0.42 0.4%
91 99.98 1.558 1.5%
94 100 2.406 2.4%
97 100.6 3.036 3.0%
100 97.95 0.433 0.4%
103 101.3 2.614 2.5%
106 105.4 2.291 2.1%
109 105.2 2.543 2.4%
112 105.8 2.949 2.7%
115 105.3 3.011 2.8%
118 104.8 0.176 0.1%
121 105.7 0.272 0.2%
124 107.1 0.903 0.8%
127 107.7 0.335 0.3%
130 109.9 2.065 1.8%
133 110.3 0.718 0.6%
136 112.5 1.961 1.7%
139 116.5 3.523 3.0%
142 119.6 2.537 2.1%
145 110.9 0.435 0.3%
148 113.8 1.86 1.6%
151 124 4.641 3.7%
154 121.2 5.267 4.3%
157 120.5 5.132 4.2%
160 121.7 3.864 3.1%
163 126.1 4.822 3.8%
166 130.6 1.653 1.2%
169 132.2 2.386 1.8%
172 133.1 3.284 2.4%
175 128.1 5.685 4.4%
178 136.3 0.584 0.4%
181 137.7 0.264 0.1%
184 137.4 3.343 2.4%
187 140.2 0.371 0.2%
190 136.8 3.785 2.7%
193 142.2 0.505 0.3%
196 144.5 0.314 0.2%
199 145.3 0.409 0.2%

Quality and confidence:
param error
c 0.002

Model:
Time ~= 59.29
+ c 0.412
µs

Reads = 3 + (0 * c)
Writes = 1 + (0 * c)
Pallet: "pallet_elections_phragmen", Extrinsic: "renounce_candidacy_candidate", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 54.77
+ c 0.225
µs

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

-- Extrinsic Time --

Data points distribution:
c mean µs sigma µs %
1 52.76 0.108 0.2%
4 53.98 0.156 0.2%
7 54.64 0.096 0.1%
10 56.2 0.117 0.2%
13 56.45 0.125 0.2%
16 57.12 0.188 0.3%
19 58.13 0.185 0.3%
22 59.4 0.165 0.2%
25 59.87 0.178 0.2%
28 60.79 0.132 0.2%
31 61.58 0.242 0.3%
34 61.61 0.221 0.3%
37 63.23 0.081 0.1%
40 64.11 0.122 0.1%
43 64.94 0.127 0.1%
46 65.36 0.125 0.1%
49 66.05 0.19 0.2%
52 66.91 0.135 0.2%
55 67.74 0.174 0.2%
58 68.21 0.198 0.2%
61 68.89 0.145 0.2%
64 69.52 0.108 0.1%
67 70.69 0.147 0.2%
70 71.16 0.207 0.2%
73 71.75 0.124 0.1%
76 72.17 0.176 0.2%
79 73.05 0.163 0.2%
82 73.82 0.26 0.3%
85 73.97 0.25 0.3%
88 74.66 0.082 0.1%
91 75.75 0.213 0.2%
94 76.32 0.345 0.4%
97 76.92 0.291 0.3%
100 77.78 0.12 0.1%
103 78.35 0.219 0.2%
106 79.27 0.462 0.5%
109 79.43 0.183 0.2%
112 80.53 0.352 0.4%
115 80.72 0.314 0.3%
118 80.73 2.102 2.6%
121 82.58 0.27 0.3%
124 78.78 3.316 4.2%
127 78.53 2.731 3.4%
130 78.58 2.051 2.6%
133 82.65 3.2 3.8%
136 78.7 0.348 0.4%
139 79.61 1.669 2.0%
142 82.41 3.287 3.9%
145 75.42 0.657 0.8%
148 76.34 0.262 0.3%
151 81.03 5.328 6.5%
154 77.59 0.928 1.1%
157 80.84 2.069 2.5%
160 86.02 3.49 4.0%
163 89.36 2.564 2.8%
166 91.93 0.589 0.6%
169 92.97 0.702 0.7%
172 93.16 0.808 0.8%
175 94.87 0.545 0.5%
178 91.64 3.978 4.3%
181 93.23 3.912 4.1%
184 93.5 4.363 4.6%
187 98.06 0.87 0.8%
190 94.74 4.002 4.2%
193 98.83 0.386 0.3%
196 99.03 0.405 0.4%
199 100.8 0.336 0.3%

Quality and confidence:
param error
c 0.002

Model:
Time ~= 55.02
+ c 0.207
µs

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

-- Extrinsic Time --

Model:
Time ~= 77.84
µs

Reads = 4
Writes = 4
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 77.84
µs

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

-- Extrinsic Time --

Model:
Time ~= 54.55
µs

Reads = 1
Writes = 1
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 54.55
µs

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

-- Extrinsic Time --

Model:
Time ~= 84.31
µs

Reads = 5
Writes = 5
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 84.31
µs

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

-- Extrinsic Time --

Model:
Time ~= 7.677
µs

Reads = 1
Writes = 0
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 7.677
µs

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

-- Extrinsic Time --

Model:
Time ~= 0
+ v 114.3
+ d 0.496
µs

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

-- Extrinsic Time --

Data points distribution:
v d mean µs sigma µs %
250 250 28030 75.13 0.2%
255 250 28690 43.54 0.1%
260 250 29130 35.33 0.1%
265 250 29700 13.28 0.0%
270 250 30330 42.66 0.1%
275 250 30980 41.17 0.1%
280 250 31450 37.38 0.1%
285 250 31910 28.21 0.0%
290 250 32550 31.15 0.0%
295 250 33200 51.66 0.1%
300 250 33750 38.26 0.1%
305 250 34400 27.81 0.0%
310 250 34950 74.87 0.2%
315 250 35490 36.58 0.1%
320 250 36120 94.93 0.2%
325 250 36610 44.88 0.1%
330 250 37190 60.39 0.1%
335 250 37850 30.37 0.0%
340 250 38330 47.78 0.1%
345 250 38910 69.13 0.1%
350 250 39470 50.07 0.1%
355 250 39880 39.41 0.0%
360 250 40570 51.71 0.1%
365 250 41210 22 0.0%
370 250 41820 30.86 0.0%
375 250 42350 73.43 0.1%
380 250 42830 23.9 0.0%
385 250 43480 48.81 0.1%
390 250 44150 51.11 0.1%
395 250 44690 65.69 0.1%
400 250 45180 23.21 0.0%
405 250 45820 41.84 0.0%
410 250 46370 49.82 0.1%
415 250 46800 28.76 0.0%
420 250 47360 68.81 0.1%
425 250 48120 35.76 0.0%
430 250 48660 57.05 0.1%
435 250 49160 67.52 0.1%
440 250 49720 30.04 0.0%
445 250 50270 56.95 0.1%
450 250 51090 62.61 0.1%
455 250 51530 65.28 0.1%
460 250 52070 59.41 0.1%
465 250 52610 47.57 0.0%
470 250 53190 73.89 0.1%
475 250 53860 63.87 0.1%
480 250 54340 54.27 0.0%
485 250 54690 49.34 0.0%
490 250 55210 81.35 0.1%
495 250 56140 63.9 0.1%
500 1 56710 76.73 0.1%
500 5 56870 101 0.1%
500 9 56720 55.18 0.0%
500 13 56690 73.75 0.1%
500 17 56710 53.2 0.0%
500 21 56650 52.76 0.0%
500 25 56780 65.96 0.1%
500 29 56790 122.5 0.2%
500 33 56820 71.22 0.1%
500 37 56640 58.35 0.1%
500 41 56660 48.41 0.0%
500 45 56600 50.99 0.0%
500 49 56560 32.07 0.0%
500 53 56700 62.3 0.1%
500 57 56610 70.34 0.1%
500 61 56650 40.17 0.0%
500 65 56630 44.31 0.0%
500 69 56740 80.9 0.1%
500 73 56640 81.01 0.1%
500 77 56670 43.66 0.0%
500 81 56800 64.75 0.1%
500 85 56750 63.4 0.1%
500 89 56920 58.88 0.1%
500 93 56660 80.71 0.1%
500 97 56620 70.75 0.1%
500 101 56590 29.67 0.0%
500 105 56620 52.64 0.0%
500 109 56690 65.75 0.1%
500 113 56730 52.12 0.0%
500 117 56770 99.39 0.1%
500 121 56650 56.43 0.0%
500 125 56670 70.72 0.1%
500 129 56700 69.96 0.1%
500 133 56640 73 0.1%
500 137 56760 67.96 0.1%
500 141 56780 63.34 0.1%
500 145 56720 60.28 0.1%
500 149 56600 60.49 0.1%
500 153 56600 82.35 0.1%
500 157 56650 39.39 0.0%
500 161 56600 62.33 0.1%
500 165 56560 54.02 0.0%
500 169 56790 44.33 0.0%
500 173 56660 58.19 0.1%
500 177 56630 138.6 0.2%
500 181 56620 65.62 0.1%
500 185 56670 102.8 0.1%
500 189 56820 72.23 0.1%
500 193 56680 51.13 0.0%
500 197 56660 73.24 0.1%
500 201 56810 61.39 0.1%
500 205 56750 95.34 0.1%
500 209 56640 76.38 0.1%
500 213 56780 63.48 0.1%
500 217 56830 80.76 0.1%
500 221 56760 70.07 0.1%
500 225 56750 61.39 0.1%
500 229 56660 72.63 0.1%
500 233 56930 97.56 0.1%
500 237 56900 59.79 0.1%
500 241 57140 56.58 0.0%
500 245 56920 73.6 0.1%
500 249 56860 54.33 0.0%
500 250 56800 70.21 0.1%

Quality and confidence:
param error
v 0.055
d 0.053

Model:
Time ~= 0
+ v 114.8
+ d 0.049
µs

Reads = 4 + (3 * v) + (0 * d)
Writes = 0 + (3 * v) + (0 * d)
Pallet: "pallet_elections_phragmen", Extrinsic: "election_phragmen", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 0
+ c 21.85
+ v 62.51
+ e 3.472
µs

Reads = 0 + (2 * c) + (1 * v) + (0 * e)
Writes = 0 + (1 * c) + (0 * v) + (0 * e)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
c v e mean µs sigma µs %
1 500 8000 6108 28.14 0.4%
4 500 8000 8810 47.76 0.5%
7 500 8000 12180 26.12 0.2%
10 500 8000 16560 32.41 0.1%
13 500 8000 21480 30.05 0.1%
16 500 8000 27190 30.7 0.1%
19 500 8000 29720 30.22 0.1%
22 500 8000 32000 27.9 0.0%
25 500 8000 33380 36.89 0.1%
28 500 8000 34330 46.23 0.1%
31 500 8000 34540 34.76 0.1%
34 500 8000 36020 43.2 0.1%
37 500 8000 37560 28.88 0.0%
40 500 8000 37630 37.52 0.0%
43 500 8000 37760 29.3 0.0%
46 500 8000 36230 29.89 0.0%
49 500 8000 36860 30.32 0.0%
52 500 8000 39190 21.84 0.0%
55 500 8000 36920 31.2 0.0%
58 500 8000 39270 24.14 0.0%
61 500 8000 38180 41.95 0.1%
64 500 8000 37780 21.51 0.0%
67 500 8000 40160 45.66 0.1%
70 500 8000 37620 15.22 0.0%
73 500 8000 37970 33.38 0.0%
76 500 8000 39690 77.88 0.1%
79 500 8000 40000 33.66 0.0%
82 500 8000 37610 32.99 0.0%
85 500 8000 38050 39.29 0.1%
88 500 8000 40150 36.43 0.0%
91 500 8000 39110 43.76 0.1%
94 500 8000 39660 44.15 0.1%
97 500 8000 38880 36.2 0.0%
100 500 8000 37840 33.98 0.0%
103 500 8000 38980 48.72 0.1%
106 500 8000 39670 44.99 0.1%
109 500 8000 39900 58.38 0.1%
112 500 8000 39880 38.43 0.0%
115 500 8000 39540 36.44 0.0%
118 500 8000 40080 32.05 0.0%
121 500 8000 38810 48.6 0.1%
124 500 8000 37570 32.76 0.0%
127 500 8000 37770 35.94 0.0%
130 500 8000 39190 32.87 0.0%
133 500 8000 39480 42.3 0.1%
136 500 8000 39480 42.96 0.1%
139 500 8000 39990 39.16 0.0%
142 500 8000 39350 53.91 0.1%
145 500 8000 39550 33.35 0.0%
148 500 8000 39740 35.94 0.0%
151 500 8000 39540 30.65 0.0%
154 500 8000 39400 43.76 0.1%
157 500 8000 39600 69.04 0.1%
160 500 8000 39200 40.72 0.1%
163 500 8000 37920 21.9 0.0%
166 500 8000 37330 49.65 0.1%
169 500 8000 37470 52.47 0.1%
172 500 8000 38280 27.67 0.0%
175 500 8000 38620 48.25 0.1%
178 500 8000 38540 32.32 0.0%
181 500 8000 38740 40.42 0.1%
184 500 8000 38920 27.67 0.0%
187 500 8000 38700 53.58 0.1%
190 500 8000 38670 23.75 0.0%
193 500 8000 38820 94.64 0.2%
196 500 8000 38870 48.47 0.1%
199 500 8000 39150 35.77 0.0%
200 1 8000 6753 12.51 0.1%
200 10 8000 7332 17.23 0.2%
200 19 8000 8061 16.88 0.2%
200 28 8000 8834 15.59 0.1%
200 37 8000 9720 72.57 0.7%
200 46 8000 10210 14.4 0.1%
200 55 8000 10860 12.74 0.1%
200 64 8000 11520 15.81 0.1%
200 73 8000 12160 21.36 0.1%
200 82 8000 12650 27.38 0.2%
200 91 8000 13330 13.64 0.1%
200 100 8000 13810 13.27 0.0%
200 109 8000 14420 25.29 0.1%
200 118 8000 14860 15.38 0.1%
200 127 8000 15380 10.01 0.0%
200 136 8000 15910 19.98 0.1%
200 145 8000 16460 15.68 0.0%
200 154 8000 17040 28.1 0.1%
200 163 8000 17500 15.18 0.0%
200 172 8000 17890 18.45 0.1%
200 181 8000 18370 24.11 0.1%
200 190 8000 18690 12.93 0.0%
200 199 8000 19050 15.38 0.0%
200 208 8000 19880 44.95 0.2%
200 217 8000 20800 40.09 0.1%
200 226 8000 21310 24.1 0.1%
200 235 8000 22180 28.21 0.1%
200 244 8000 22500 22.15 0.0%
200 253 8000 23010 27.26 0.1%
200 262 8000 23670 87.24 0.3%
200 271 8000 23990 34.1 0.1%
200 280 8000 24390 33.92 0.1%
200 289 8000 25080 38.86 0.1%
200 298 8000 25920 19.21 0.0%
200 307 8000 26390 39.81 0.1%
200 316 8000 26990 22.11 0.0%
200 325 8000 27480 38.69 0.1%
200 334 8000 27750 13.27 0.0%
200 343 8000 28470 18.48 0.0%
200 352 8000 28910 31.27 0.1%
200 361 8000 29450 24.13 0.0%
200 370 8000 30100 28.52 0.0%
200 379 8000 30530 45.05 0.1%
200 388 8000 30830 24.93 0.0%
200 397 8000 31010 34.71 0.1%
200 406 8000 31620 33.33 0.1%
200 415 8000 32720 39.38 0.1%
200 424 8000 34130 26.26 0.0%
200 433 8000 34710 48.96 0.1%
200 442 8000 35070 37.53 0.1%
200 451 8000 35830 44.74 0.1%
200 460 8000 36320 39.98 0.1%
200 469 8000 36930 54.06 0.1%
200 478 8000 37440 38.88 0.1%
200 487 8000 38060 23.37 0.0%
200 496 8000 38520 36.54 0.0%
200 500 500 12360 56.02 0.4%
200 500 650 12300 24.34 0.1%
200 500 800 12300 21.58 0.1%
200 500 950 12280 21.12 0.1%
200 500 1100 13230 31.16 0.2%
200 500 1250 13270 29.38 0.2%
200 500 1400 13300 39.35 0.2%
200 500 1550 14330 21.34 0.1%
200 500 1700 14330 32.02 0.2%
200 500 1850 14310 27.05 0.1%
200 500 2000 15510 23.82 0.1%
200 500 2150 15500 17.34 0.1%
200 500 2300 15530 45.88 0.2%
200 500 2450 15490 24.29 0.1%
200 500 2600 16830 31.71 0.1%
200 500 2750 16840 19.33 0.1%
200 500 2900 16820 20.76 0.1%
200 500 3050 18440 43.92 0.2%
200 500 3200 18540 67.5 0.3%
200 500 3350 18480 51.2 0.2%
200 500 3500 20180 48.32 0.2%
200 500 3650 20190 34.61 0.1%
200 500 3800 20210 50.42 0.2%
200 500 3950 20240 27.55 0.1%
200 500 4100 21650 37.26 0.1%
200 500 4250 21640 29.61 0.1%
200 500 4400 21610 43.02 0.1%
200 500 4550 23540 36.98 0.1%
200 500 4700 23530 55.85 0.2%
200 500 4850 23520 56.37 0.2%
200 500 5000 25250 44.74 0.1%
200 500 5150 25270 65.02 0.2%
200 500 5300 25260 45.22 0.1%
200 500 5450 25280 40.88 0.1%
200 500 5600 27440 36.38 0.1%
200 500 5750 27430 26.3 0.0%
200 500 5900 27430 43.23 0.1%
200 500 6050 29670 38 0.1%
200 500 6200 29600 39.96 0.1%
200 500 6350 29570 16.82 0.0%
200 500 6500 32150 55.02 0.1%
200 500 6650 32150 48.68 0.1%
200 500 6800 32180 66.27 0.2%
200 500 6950 32250 50.2 0.1%
200 500 7100 33830 28.02 0.0%
200 500 7250 33910 40.69 0.1%
200 500 7400 33870 23.24 0.0%
200 500 7550 36460 54.56 0.1%
200 500 7700 36480 46.2 0.1%
200 500 7850 36390 37.7 0.1%
200 500 8000 39080 22.54 0.0%

Quality and confidence:
param error
c 1.94
v 0.807
e 0.055

Model:
Time ~= 0
+ c 43.55
+ v 65.84
+ e 4.206
µs

Reads = 0 + (2 * c) + (1 * v) + (0 * e)
Writes = 0 + (1 * c) + (0 * v) + (0 * e)

[gavofyork]

Needs a companion?

[kianenigma]

bot merge

[ghost]

Waiting for commit status.