- Forked from Artefact2
- Tests were run in single-user mode, one at a time.
- Data was read from and written to tmpfs.
- I did not have the dataset from Artefact2 for
zstd
-
-
Save suuuehgi/f38ad8d220d1e06e43d1c44efcd035a8 to your computer and use it in GitHub Desktop.
Borg compression benchmark/comparison
Benchmark date: 2017-09-09
Borg version: borg-linux64 1.1.0rc2
Python version: Python 3.6.2
Kernel version: Linux 4.12.8-2-ARCH
CPU model: AMD FX(tm)-8150 Eight-Core Processor
Borg init command: %s init --error -e repokey %s
Borg create command: %s create --error -C %s %s::foo %s
Dataset path: /tmp/borg-dataset (tmpfs)
Repository path: /tmp/borg-repo (tmpfs)
Averaging algos over 1 run(s)
algo | cputime | reposize | bytes/s | timeratio | compratio
----------------+------------+-----------+-----------+-----------+----------
none | 31.10 | 792.654M | 25.487M | 1.00 | 1.000
lz4 | 29.54 | 311.050M | 26.833M | 0.95 | 0.392
zlib,0 | 33.00 | 793.244M | 24.020M | 1.06 | 1.001
zlib,1 | 36.82 | 228.762M | 21.528M | 1.18 | 0.289
zlib,2 | 37.74 | 221.245M | 21.003M | 1.21 | 0.279
zlib,3 | 39.05 | 216.240M | 20.298M | 1.26 | 0.273
zlib,4 | 41.51 | 205.451M | 19.095M | 1.33 | 0.259
zlib,5 | 44.57 | 199.655M | 17.784M | 1.43 | 0.252
zlib,6 | 50.48 | 197.501M | 15.702M | 1.62 | 0.249
zlib,7 | 55.10 | 196.702M | 14.386M | 1.77 | 0.248
zlib,8 | 66.13 | 196.170M | 11.986M | 2.13 | 0.247
zlib,9 | 66.77 | 196.137M | 11.871M | 2.15 | 0.247
lzma,0 | 79.57 | 201.884M | 9961.717K | 2.56 | 0.255
lzma,1 | 96.85 | 195.178M | 8184.345K | 3.11 | 0.246
lzma,2 | 114.55 | 192.754M | 6919.719K | 3.68 | 0.243
lzma,3 | 154.87 | 191.959M | 5118.188K | 4.98 | 0.242
lzma,4 | 307.70 | 189.075M | 2576.060K | 9.89 | 0.239
lzma,5 | 487.64 | 185.676M | 1625.489K | 15.68 | 0.234
lzma,6 | 543.37 | 184.078M | 1458.773K | 17.47 | 0.232
lzma,7 | 764.68 | 184.058M | 1036.582K | 24.59 | 0.232
lzma,8 | 1218.62 | 184.082M | 650.452K | 39.18 | 0.232
lzma,9 | 1218.07 | 184.095M | 650.745K | 39.17 | 0.232
auto,lz4 | 32.19 | 311.050M | 24.624M | 1.04 | 0.392
auto,zlib,0 | 36.56 | 793.240M | 21.681M | 1.18 | 1.001
auto,zlib,1 | 39.51 | 228.762M | 20.062M | 1.27 | 0.289
auto,zlib,2 | 41.02 | 221.250M | 19.324M | 1.32 | 0.279
auto,zlib,3 | 41.67 | 216.248M | 19.022M | 1.34 | 0.273
auto,zlib,4 | 44.01 | 205.447M | 18.011M | 1.42 | 0.259
auto,zlib,5 | 47.52 | 199.660M | 16.680M | 1.53 | 0.252
auto,zlib,6 | 53.45 | 197.497M | 14.830M | 1.72 | 0.249
auto,zlib,7 | 57.71 | 196.706M | 13.735M | 1.86 | 0.248
auto,zlib,8 | 67.28 | 196.162M | 11.781M | 2.16 | 0.247
auto,zlib,9 | 69.64 | 196.141M | 11.382M | 2.24 | 0.247
auto,lzma,0 | 82.29 | 201.830M | 9632.444K | 2.65 | 0.255
auto,lzma,1 | 100.03 | 195.105M | 7924.160K | 3.22 | 0.246
auto,lzma,2 | 116.15 | 192.668M | 6824.397K | 3.73 | 0.243
auto,lzma,3 | 159.19 | 192.012M | 4979.294K | 5.12 | 0.242
auto,lzma,4 | 314.17 | 189.080M | 2523.009K | 10.10 | 0.239
auto,lzma,5 | 486.67 | 185.709M | 1628.729K | 15.65 | 0.234
auto,lzma,6 | 542.93 | 184.013M | 1459.955K | 17.46 | 0.232
auto,lzma,7 | 763.56 | 184.054M | 1038.102K | 24.55 | 0.232
auto,lzma,8 | 1207.50 | 184.029M | 656.442K | 38.83 | 0.232
auto,lzma,9 | 1205.69 | 184.001M | 657.427K | 38.77 | 0.232
Benchmark date: 2017-09-10
Borg version: borg-linux64 1.1.0rc2
Python version: Python 3.6.2
Kernel version: Linux 4.12.8-2-ARCH
CPU model: AMD FX(tm)-8150 Eight-Core Processor
Borg init command: %s init --error -e repokey %s
Borg create command: %s create --error -C %s %s::foo %s
Dataset path: /tmp/borg-dataset (tmpfs)
Repository path: /tmp/borg-repo (tmpfs)
Averaging algos over 1 run(s)
algo | cputime | reposize | bytes/s | timeratio | compratio
----------------+------------+-----------+-----------+-----------+----------
none | 12.06 | 682.570M | 56.598M | 1.00 | 1.000
lz4 | 10.04 | 222.540M | 67.985M | 0.83 | 0.326
zlib,0 | 14.91 | 682.672M | 45.779M | 1.24 | 1.000
zlib,1 | 15.61 | 154.948M | 43.726M | 1.29 | 0.227
zlib,2 | 16.50 | 150.839M | 41.368M | 1.37 | 0.221
zlib,3 | 18.30 | 147.550M | 37.299M | 1.52 | 0.216
zlib,4 | 21.61 | 136.143M | 31.586M | 1.79 | 0.199
zlib,5 | 25.56 | 128.610M | 26.705M | 2.12 | 0.188
zlib,6 | 31.23 | 126.640M | 21.856M | 2.59 | 0.186
zlib,7 | 36.63 | 125.997M | 18.634M | 3.04 | 0.185
zlib,8 | 51.20 | 124.961M | 13.331M | 4.25 | 0.183
zlib,9 | 53.49 | 124.903M | 12.761M | 4.44 | 0.183
lzma,0 | 43.15 | 125.125M | 15.819M | 3.58 | 0.183
lzma,1 | 47.43 | 115.737M | 14.391M | 3.93 | 0.170
lzma,2 | 59.86 | 111.706M | 11.403M | 4.96 | 0.164
lzma,3 | 77.78 | 109.818M | 8775.645K | 6.45 | 0.161
lzma,4 | 129.02 | 98.345M | 5290.417K | 10.70 | 0.144
lzma,5 | 174.62 | 94.564M | 3908.886K | 14.48 | 0.139
lzma,6 | 320.48 | 89.686M | 2129.835K | 26.57 | 0.131
lzma,7 | 322.40 | 89.711M | 2117.151K | 26.73 | 0.131
lzma,8 | 326.85 | 89.489M | 2088.327K | 27.10 | 0.131
lzma,9 | 329.77 | 89.518M | 2069.835K | 27.34 | 0.131
auto,lz4 | 11.78 | 222.540M | 57.943M | 0.98 | 0.326
auto,zlib,0 | 16.60 | 682.672M | 41.119M | 1.38 | 1.000
auto,zlib,1 | 17.35 | 154.952M | 39.341M | 1.44 | 0.227
auto,zlib,2 | 18.13 | 150.847M | 37.649M | 1.50 | 0.221
auto,zlib,3 | 20.02 | 147.522M | 34.094M | 1.66 | 0.216
auto,zlib,4 | 23.53 | 136.163M | 29.008M | 1.95 | 0.199
auto,zlib,5 | 26.54 | 128.618M | 25.719M | 2.20 | 0.188
auto,zlib,6 | 34.28 | 126.648M | 19.912M | 2.84 | 0.186
auto,zlib,7 | 37.69 | 125.989M | 18.110M | 3.13 | 0.185
auto,zlib,8 | 53.51 | 124.953M | 12.756M | 4.44 | 0.183
auto,zlib,9 | 55.94 | 124.891M | 12.202M | 4.64 | 0.183
auto,lzma,0 | 45.07 | 125.088M | 15.145M | 3.74 | 0.183
auto,lzma,1 | 49.24 | 115.806M | 13.862M | 4.08 | 0.170
auto,lzma,2 | 61.87 | 111.907M | 11.032M | 5.13 | 0.164
auto,lzma,3 | 78.91 | 110.121M | 8649.977K | 6.54 | 0.161
auto,lzma,4 | 135.67 | 97.944M | 5031.102K | 11.25 | 0.143
auto,lzma,5 | 173.32 | 94.376M | 3938.205K | 14.37 | 0.138
auto,lzma,6 | 321.33 | 89.723M | 2124.201K | 26.64 | 0.131
auto,lzma,7 | 324.67 | 89.477M | 2102.349K | 26.92 | 0.131
auto,lzma,8 | 325.54 | 89.559M | 2096.730K | 26.99 | 0.131
auto,lzma,9 | 327.80 | 89.825M | 2082.274K | 27.18 | 0.132
Benchmark date: 2017-09-10
Borg version: borg-linux64 1.1.0rc2
Python version: Python 3.6.2
Kernel version: Linux 4.12.8-2-ARCH
CPU model: AMD FX(tm)-8150 Eight-Core Processor
Borg init command: %s init --error -e repokey %s
Borg create command: %s create --error -C %s %s::foo %s
Dataset path: /tmp/borg-dataset (tmpfs)
Repository path: /tmp/borg-repo (tmpfs)
Averaging algos over 1 run(s)
algo | cputime | reposize | bytes/s | timeratio | compratio
----------------+------------+-----------+-----------+-----------+----------
none | 10.08 | 552.243M | 54.786M | 1.00 | 1.000
lz4 | 10.51 | 553.198M | 52.545M | 1.04 | 1.002
zlib,0 | 12.31 | 552.329M | 44.861M | 1.22 | 1.000
zlib,1 | 31.31 | 541.327M | 17.638M | 3.11 | 0.980
zlib,2 | 30.76 | 541.123M | 17.953M | 3.05 | 0.980
zlib,3 | 32.22 | 541.028M | 17.140M | 3.20 | 0.980
zlib,4 | 32.63 | 541.639M | 16.924M | 3.24 | 0.981
zlib,5 | 33.09 | 541.610M | 16.689M | 3.28 | 0.981
zlib,6 | 34.04 | 541.594M | 16.223M | 3.38 | 0.981
zlib,7 | 32.58 | 541.594M | 16.950M | 3.23 | 0.981
zlib,8 | 33.74 | 541.594M | 16.368M | 3.35 | 0.981
zlib,9 | 32.60 | 541.598M | 16.940M | 3.23 | 0.981
lzma,0 | 114.12 | 539.759M | 4839.144K | 11.32 | 0.977
lzma,1 | 128.01 | 539.451M | 4314.062K | 12.70 | 0.977
lzma,2 | 139.48 | 539.443M | 3959.300K | 13.84 | 0.977
lzma,3 | 144.05 | 539.423M | 3833.691K | 14.29 | 0.977
lzma,4 | 152.77 | 536.760M | 3614.866K | 15.16 | 0.972
lzma,5 | 150.73 | 536.711M | 3663.790K | 14.95 | 0.972
lzma,6 | 150.52 | 536.781M | 3668.902K | 14.93 | 0.972
lzma,7 | 148.66 | 536.748M | 3714.806K | 14.75 | 0.972
lzma,8 | 149.84 | 536.805M | 3685.552K | 14.87 | 0.972
lzma,9 | 149.07 | 536.736M | 3704.589K | 14.79 | 0.972
auto,lz4 | 10.69 | 551.404M | 51.660M | 1.06 | 0.998
auto,zlib,0 | 10.99 | 552.255M | 50.250M | 1.09 | 1.000
auto,zlib,1 | 13.00 | 546.533M | 42.480M | 1.29 | 0.990
auto,zlib,2 | 13.37 | 545.362M | 41.305M | 1.33 | 0.988
auto,zlib,3 | 13.57 | 545.980M | 40.696M | 1.35 | 0.989
auto,zlib,4 | 14.26 | 545.980M | 38.727M | 1.41 | 0.989
auto,zlib,5 | 15.30 | 544.649M | 36.094M | 1.52 | 0.986
auto,zlib,6 | 13.90 | 546.619M | 39.730M | 1.38 | 0.990
auto,zlib,7 | 14.00 | 546.296M | 39.446M | 1.39 | 0.989
auto,zlib,8 | 13.67 | 546.947M | 40.398M | 1.36 | 0.990
auto,zlib,9 | 14.01 | 546.161M | 39.418M | 1.39 | 0.989
auto,lzma,0 | 21.26 | 544.084M | 25.976M | 2.11 | 0.985
auto,lzma,1 | 23.84 | 544.461M | 23.165M | 2.37 | 0.986
auto,lzma,2 | 24.53 | 544.211M | 22.513M | 2.43 | 0.985
auto,lzma,3 | 25.64 | 544.330M | 21.538M | 2.54 | 0.986
auto,lzma,4 | 31.46 | 542.163M | 17.554M | 3.12 | 0.982
auto,lzma,5 | 28.74 | 542.806M | 19.215M | 2.85 | 0.983
auto,lzma,6 | 27.20 | 543.543M | 20.303M | 2.70 | 0.984
auto,lzma,7 | 25.30 | 544.756M | 21.828M | 2.51 | 0.986
auto,lzma,8 | 26.69 | 543.953M | 20.691M | 2.65 | 0.985
auto,lzma,9 | 27.53 | 542.675M | 20.060M | 2.73 | 0.983
Benchmark date: 2018-04-09
Borg version: borg 1.1.5
Python version: Python 3.6.4
Kernel version: Linux 4.15.15-1-ARCH
CPU model: Intel(R) Core(TM) i5-3317U CPU @ 1.70GHz
Borg init command: %s init --error -e repokey %s
Borg create command: %s create --error -C %s %s::foo %s
Dataset path: /tmp/borgdataset (tmpfs)
Repository path: /tmp/borg-repo (tmpfs)
Averaging algos over 1 run(s)
algo | cputime | reposize | bytes/s | timeratio | compratio
----------------+------------+-----------+-----------+-----------+----------
none | 2.36 | 117.527M | 49.799M | 1.00 | 1.000
lz4 | 1.90 | 22.360M | 61.856M | 0.81 | 0.190
zlib,0 | 2.47 | 117.535M | 47.582M | 1.05 | 1.000
zlib,1 | 2.63 | 14.262M | 44.687M | 1.11 | 0.121
zlib,2 | 2.64 | 13.529M | 44.518M | 1.12 | 0.115
zlib,3 | 3.34 | 13.464M | 35.188M | 1.42 | 0.115
zlib,4 | 3.37 | 13.173M | 34.874M | 1.43 | 0.112
zlib,5 | 3.96 | 13.017M | 29.678M | 1.68 | 0.111
zlib,6 | 5.36 | 12.931M | 21.927M | 2.27 | 0.110
zlib,7 | 6.82 | 12.902M | 17.233M | 2.89 | 0.110
zlib,8 | 13.77 | 12.952M | 8534.969K | 5.83 | 0.110
zlib,9 | 36.13 | 12.980M | 3252.879K | 15.31 | 0.110
lzma,0 | 5.07 | 8687.616K | 23.181M | 2.15 | 0.074
lzma,1 | 5.23 | 6987.776K | 22.472M | 2.22 | 0.059
lzma,2 | 5.85 | 7282.688K | 20.090M | 2.48 | 0.062
lzma,3 | 6.45 | 6795.264K | 18.221M | 2.73 | 0.058
lzma,4 | 15.95 | 6250.496K | 7368.434K | 6.76 | 0.053
lzma,5 | 20.91 | 6037.504K | 5620.589K | 8.86 | 0.051
lzma,6 | 27.10 | 6057.984K | 4336.772K | 11.48 | 0.052
lzma,7 | 29.24 | 6311.936K | 4019.375K | 12.39 | 0.054
lzma,8 | 28.81 | 5947.392K | 4079.365K | 12.21 | 0.051
lzma,9 | 29.36 | 5926.912K | 4002.947K | 12.44 | 0.050
zstd,1 | 2.02 | 9875.456K | 58.181M | 0.86 | 0.084
zstd,2 | 1.92 | 10.211M | 61.212M | 0.81 | 0.087
zstd,3 | 2.00 | 8953.856K | 58.763M | 0.85 | 0.076
zstd,4 | 2.11 | 8671.232K | 55.700M | 0.89 | 0.074
zstd,5 | 2.42 | 9052.160K | 48.565M | 1.03 | 0.077
zstd,6 | 2.79 | 9318.400K | 42.124M | 1.18 | 0.079
zstd,7 | 2.71 | 7933.952K | 43.368M | 1.15 | 0.068
zstd,8 | 3.00 | 7680.000K | 39.176M | 1.27 | 0.065
zstd,9 | 3.04 | 7897.088K | 38.660M | 1.29 | 0.067
zstd,10 | 3.70 | 8794.112K | 31.764M | 1.57 | 0.075
zstd,11 | 3.52 | 7806.976K | 33.388M | 1.49 | 0.066
zstd,12 | 3.83 | 7483.392K | 30.686M | 1.62 | 0.064
zstd,13 | 5.74 | 7712.768K | 20.475M | 2.43 | 0.066
zstd,14 | 7.16 | 7593.984K | 16.414M | 3.03 | 0.065
zstd,15 | 12.23 | 7761.920K | 9609.691K | 5.18 | 0.066
zstd,16 | 17.84 | 7499.776K | 6587.809K | 7.56 | 0.064
zstd,17 | 19.10 | 7294.976K | 6153.221K | 8.09 | 0.062
zstd,18 | 27.62 | 7258.112K | 4255.124K | 11.70 | 0.062
zstd,19 | 31.60 | 7798.784K | 3719.193K | 13.39 | 0.066
zstd,20 | 36.01 | 7868.416K | 3263.719K | 15.26 | 0.067
zstd,21 | 33.29 | 7118.848K | 3530.385K | 14.11 | 0.061
zstd,22 | 86.91 | 6742.016K | 1352.278K | 36.83 | 0.057
auto,lz4 | 2.15 | 22.356M | 54.664M | 0.91 | 0.190
auto,zlib,0 | 2.07 | 22.356M | 56.776M | 0.88 | 0.190
auto,zlib,1 | 2.80 | 14.053M | 41.974M | 1.19 | 0.120
auto,zlib,2 | 2.84 | 13.533M | 41.383M | 1.20 | 0.115
auto,zlib,3 | 2.90 | 13.238M | 40.526M | 1.23 | 0.113
auto,zlib,4 | 3.35 | 13.173M | 35.083M | 1.42 | 0.112
auto,zlib,5 | 3.76 | 13.021M | 31.257M | 1.59 | 0.111
auto,zlib,6 | 5.44 | 12.911M | 21.604M | 2.31 | 0.110
auto,zlib,7 | 6.80 | 12.894M | 17.283M | 2.88 | 0.110
auto,zlib,8 | 13.74 | 12.956M | 8553.604K | 5.82 | 0.110
auto,zlib,9 | 36.80 | 12.984M | 3193.655K | 15.59 | 0.110
auto,lzma,0 | 5.31 | 8761.344K | 22.133M | 2.25 | 0.075
auto,lzma,1 | 5.84 | 7966.720K | 20.124M | 2.47 | 0.068
auto,lzma,2 | 5.74 | 6750.208K | 20.475M | 2.43 | 0.057
auto,lzma,3 | 6.83 | 7069.696K | 17.207M | 2.89 | 0.060
auto,lzma,4 | 15.97 | 6316.032K | 7359.206K | 6.77 | 0.054
auto,lzma,5 | 21.95 | 6008.832K | 5354.283K | 9.30 | 0.051
auto,lzma,6 | 28.20 | 6234.112K | 4167.607K | 11.95 | 0.053
auto,lzma,7 | 28.30 | 6021.120K | 4152.880K | 11.99 | 0.051
auto,lzma,8 | 30.76 | 6230.016K | 3820.758K | 13.03 | 0.053
auto,lzma,9 | 31.45 | 6348.800K | 3736.932K | 13.33 | 0.054
auto,zstd,1 | 2.04 | 9900.032K | 57.611M | 0.86 | 0.084
auto,zstd,2 | 2.11 | 10.191M | 55.700M | 0.89 | 0.087
auto,zstd,3 | 2.09 | 8773.632K | 56.233M | 0.89 | 0.075
auto,zstd,4 | 2.14 | 8724.480K | 54.919M | 0.91 | 0.074
auto,zstd,5 | 2.52 | 8810.496K | 46.638M | 1.07 | 0.075
auto,zstd,6 | 2.79 | 8052.736K | 42.124M | 1.18 | 0.069
auto,zstd,7 | 2.91 | 7888.896K | 40.387M | 1.23 | 0.067
auto,zstd,8 | 3.17 | 8015.872K | 37.075M | 1.34 | 0.068
auto,zstd,9 | 3.19 | 7589.888K | 36.842M | 1.35 | 0.065
auto,zstd,10 | 3.41 | 7569.408K | 34.465M | 1.44 | 0.064
auto,zstd,11 | 3.77 | 8028.160K | 31.174M | 1.60 | 0.068
auto,zstd,12 | 3.83 | 7512.064K | 30.686M | 1.62 | 0.064
auto,zstd,13 | 5.88 | 7864.320K | 19.988M | 2.49 | 0.067
auto,zstd,14 | 7.26 | 7614.464K | 16.188M | 3.08 | 0.065
auto,zstd,15 | 11.96 | 7663.616K | 9826.632K | 5.07 | 0.065
auto,zstd,16 | 22.26 | 7962.624K | 5279.718K | 9.43 | 0.068
auto,zstd,17 | 19.19 | 7237.632K | 6124.363K | 8.13 | 0.062
auto,zstd,18 | 27.37 | 7364.608K | 4293.990K | 11.60 | 0.063
auto,zstd,19 | 32.82 | 7909.376K | 3580.942K | 13.91 | 0.067
auto,zstd,20 | 36.20 | 7258.112K | 3246.589K | 15.34 | 0.062
auto,zstd,21 | 35.74 | 7950.336K | 3288.375K | 15.14 | 0.068
auto,zstd,22 | 86.46 | 6963.200K | 1359.316K | 36.64 | 0.059
Benchmark date: 2018-04-09
Borg version: borg 1.1.5
Python version: Python 3.6.4
Kernel version: Linux 4.15.15-1-ARCH
CPU model: Intel(R) Core(TM) i5-3317U CPU @ 1.70GHz
Borg init command: %s init --error -e repokey %s
Borg create command: %s create --error -C %s %s::foo %s
Dataset path: /tmp/borgdataset (tmpfs)
Repository path: /tmp/borg-repo (tmpfs)
Averaging algos over 1 run(s)
algo | cputime | reposize | bytes/s | timeratio | compratio
----------------+------------+-----------+-----------+-----------+----------
none | 4.23 | 269.820M | 63.787M | 1.00 | 1.000
lz4 | 4.49 | 265.060M | 60.094M | 1.06 | 0.982
zlib,0 | 4.36 | 269.840M | 61.885M | 1.03 | 1.000
zlib,1 | 13.83 | 262.844M | 19.510M | 3.27 | 0.974
zlib,2 | 13.96 | 262.779M | 19.328M | 3.30 | 0.974
zlib,3 | 13.99 | 262.746M | 19.287M | 3.31 | 0.974
zlib,4 | 14.37 | 262.672M | 18.777M | 3.40 | 0.974
zlib,5 | 14.67 | 262.636M | 18.393M | 3.47 | 0.973
zlib,6 | 14.70 | 262.595M | 18.355M | 3.48 | 0.973
zlib,7 | 14.60 | 262.590M | 18.481M | 3.45 | 0.973
zlib,8 | 14.77 | 262.599M | 18.268M | 3.49 | 0.973
zlib,9 | 14.83 | 262.578M | 18.194M | 3.51 | 0.973
lzma,0 | 75.97 | 262.431M | 3551.663K | 17.96 | 0.973
lzma,1 | 104.71 | 261.935M | 2576.830K | 24.75 | 0.971
lzma,2 | 104.03 | 261.390M | 2593.673K | 24.59 | 0.969
lzma,3 | 97.22 | 261.308M | 2775.353K | 22.98 | 0.968
lzma,4 | 97.76 | 260.997M | 2760.023K | 23.11 | 0.967
lzma,5 | 96.46 | 260.465M | 2797.220K | 22.80 | 0.965
lzma,6 | 92.41 | 260.923M | 2919.812K | 21.85 | 0.967
lzma,7 | 93.01 | 260.792M | 2900.977K | 21.99 | 0.967
lzma,8 | 95.05 | 260.813M | 2838.715K | 22.47 | 0.967
lzma,9 | 94.23 | 261.014M | 2863.418K | 22.28 | 0.967
zstd,1 | 4.78 | 263.500M | 56.448M | 1.13 | 0.977
zstd,2 | 4.87 | 263.369M | 55.404M | 1.15 | 0.976
zstd,3 | 5.12 | 263.094M | 52.699M | 1.21 | 0.975
zstd,4 | 5.32 | 262.828M | 50.718M | 1.26 | 0.974
zstd,5 | 10.08 | 262.345M | 26.768M | 2.38 | 0.972
zstd,6 | 12.17 | 261.976M | 22.171M | 2.88 | 0.971
zstd,7 | 13.71 | 261.800M | 19.681M | 3.24 | 0.970
zstd,8 | 14.42 | 262.119M | 18.712M | 3.41 | 0.971
zstd,9 | 14.49 | 262.001M | 18.621M | 3.43 | 0.971
zstd,10 | 14.06 | 261.976M | 19.191M | 3.32 | 0.971
zstd,11 | 15.71 | 261.722M | 17.175M | 3.71 | 0.970
zstd,12 | 14.52 | 261.865M | 18.583M | 3.43 | 0.971
zstd,13 | 21.22 | 261.648M | 12.715M | 5.02 | 0.970
zstd,14 | 21.70 | 261.825M | 12.434M | 5.13 | 0.970
zstd,15 | 22.60 | 261.407M | 11.939M | 5.34 | 0.969
zstd,16 | 34.25 | 261.710M | 7877.953K | 8.10 | 0.970
zstd,17 | 36.28 | 261.763M | 7437.152K | 8.58 | 0.970
zstd,18 | 44.19 | 261.157M | 6105.904K | 10.45 | 0.968
zstd,19 | 45.87 | 261.542M | 5882.273K | 10.84 | 0.969
zstd,20 | 41.59 | 261.566M | 6487.614K | 9.83 | 0.969
zstd,21 | 42.27 | 261.419M | 6383.248K | 9.99 | 0.969
zstd,22 | 43.17 | 261.497M | 6250.171K | 10.21 | 0.969
auto,lz4 | 4.48 | 264.585M | 60.228M | 1.06 | 0.981
auto,zlib,0 | 4.39 | 264.528M | 61.462M | 1.04 | 0.980
auto,zlib,1 | 8.29 | 263.430M | 32.548M | 1.96 | 0.976
auto,zlib,2 | 8.54 | 263.328M | 31.595M | 2.02 | 0.976
auto,zlib,3 | 8.26 | 263.324M | 32.666M | 1.95 | 0.976
auto,zlib,4 | 8.64 | 263.238M | 31.229M | 2.04 | 0.976
auto,zlib,5 | 8.66 | 263.193M | 31.157M | 2.05 | 0.975
auto,zlib,6 | 8.61 | 263.160M | 31.338M | 2.04 | 0.975
auto,zlib,7 | 8.73 | 263.143M | 30.907M | 2.06 | 0.975
auto,zlib,8 | 9.12 | 263.107M | 29.586M | 2.16 | 0.975
auto,zlib,9 | 8.74 | 263.152M | 30.872M | 2.07 | 0.975
auto,lzma,0 | 26.77 | 262.918M | 10.079M | 6.33 | 0.974
auto,lzma,1 | 40.72 | 262.623M | 6626.225K | 9.63 | 0.973
auto,lzma,2 | 42.34 | 262.758M | 6372.694K | 10.01 | 0.974
auto,lzma,3 | 39.33 | 262.550M | 6860.409K | 9.30 | 0.973
auto,lzma,4 | 43.29 | 262.230M | 6232.846K | 10.23 | 0.972
auto,lzma,5 | 39.78 | 262.377M | 6782.803K | 9.40 | 0.972
auto,lzma,6 | 42.14 | 262.103M | 6402.940K | 9.96 | 0.971
auto,lzma,7 | 40.94 | 262.463M | 6590.618K | 9.68 | 0.973
auto,lzma,8 | 42.23 | 262.279M | 6389.294K | 9.98 | 0.972
auto,lzma,9 | 42.08 | 262.349M | 6412.069K | 9.95 | 0.972
auto,zstd,1 | 4.72 | 263.541M | 57.165M | 1.12 | 0.977
auto,zstd,2 | 4.73 | 263.574M | 57.044M | 1.12 | 0.977
auto,zstd,3 | 4.79 | 263.348M | 56.330M | 1.13 | 0.976
auto,zstd,4 | 5.08 | 263.049M | 53.114M | 1.20 | 0.975
auto,zstd,5 | 6.11 | 262.885M | 44.160M | 1.44 | 0.974
auto,zstd,6 | 6.74 | 262.816M | 40.033M | 1.59 | 0.974
auto,zstd,7 | 7.38 | 262.971M | 36.561M | 1.74 | 0.975
auto,zstd,8 | 7.70 | 262.881M | 35.042M | 1.82 | 0.974
auto,zstd,9 | 7.90 | 263.098M | 34.154M | 1.87 | 0.975
auto,zstd,10 | 8.29 | 262.689M | 32.548M | 1.96 | 0.974
auto,zstd,11 | 8.15 | 262.795M | 33.107M | 1.93 | 0.974
auto,zstd,12 | 8.42 | 262.816M | 32.045M | 1.99 | 0.974
auto,zstd,13 | 9.68 | 263.004M | 27.874M | 2.29 | 0.975
auto,zstd,14 | 10.63 | 262.795M | 25.383M | 2.51 | 0.974
auto,zstd,15 | 10.90 | 262.906M | 24.754M | 2.58 | 0.974
auto,zstd,16 | 17.77 | 262.693M | 15.184M | 4.20 | 0.974
auto,zstd,17 | 18.95 | 262.529M | 14.239M | 4.48 | 0.973
auto,zstd,18 | 20.50 | 262.656M | 13.162M | 4.85 | 0.973
auto,zstd,19 | 21.87 | 262.410M | 12.337M | 5.17 | 0.973
auto,zstd,20 | 20.43 | 262.762M | 13.207M | 4.83 | 0.974
auto,zstd,21 | 21.59 | 262.517M | 12.497M | 5.10 | 0.973
auto,zstd,22 | 24.10 | 262.394M | 11.196M | 5.70 | 0.972
Benchmark date: 2018-04-09
Borg version: borg 1.1.5
Python version: Python 3.6.4
Kernel version: Linux 4.15.15-1-ARCH
CPU model: Intel(R) Core(TM) i5-3317U CPU @ 1.70GHz
Borg init command: %s init --error -e repokey %s
Borg create command: %s create --error -C %s %s::foo %s
Dataset path: /tmp/borgdataset (tmpfs)
Repository path: /tmp/borg-repo (tmpfs)
Averaging algos over 1 run(s)
algo | cputime | reposize | bytes/s | timeratio | compratio
----------------+------------+-----------+-----------+-----------+----------
none | 3.50 | 152.375M | 43.536M | 1.00 | 1.000
lz4 | 3.49 | 114.737M | 43.661M | 1.00 | 0.753
zlib,0 | 3.57 | 152.400M | 42.682M | 1.02 | 1.000
zlib,1 | 7.55 | 104.919M | 20.182M | 2.16 | 0.689
zlib,2 | 7.75 | 104.542M | 19.661M | 2.21 | 0.686
zlib,3 | 8.16 | 104.014M | 18.673M | 2.33 | 0.683
zlib,4 | 8.46 | 103.633M | 18.011M | 2.42 | 0.680
zlib,5 | 9.40 | 103.064M | 16.210M | 2.69 | 0.676
zlib,6 | 11.25 | 102.593M | 13.544M | 3.21 | 0.673
zlib,7 | 13.32 | 101.413M | 11.440M | 3.81 | 0.666
zlib,8 | 21.11 | 102.236M | 7218.157K | 6.03 | 0.671
zlib,9 | 32.94 | 102.207M | 4625.843K | 9.41 | 0.671
lzma,0 | 29.96 | 99.594M | 5085.957K | 8.56 | 0.654
lzma,1 | 37.16 | 99.033M | 4100.519K | 10.62 | 0.650
lzma,2 | 37.57 | 98.804M | 4055.770K | 10.73 | 0.648
lzma,3 | 39.16 | 98.693M | 3891.095K | 11.19 | 0.648
lzma,4 | 56.38 | 97.382M | 2702.648K | 16.11 | 0.639
lzma,5 | 61.89 | 97.055M | 2462.034K | 17.68 | 0.637
lzma,6 | 64.31 | 96.936M | 2369.387K | 18.37 | 0.636
lzma,7 | 95.28 | 96.895M | 1599.236K | 27.22 | 0.636
lzma,8 | 126.31 | 96.932M | 1206.359K | 36.09 | 0.636
lzma,9 | 123.99 | 96.920M | 1228.932K | 35.43 | 0.636
zstd,1 | 3.98 | 104.194M | 38.285M | 1.14 | 0.684
zstd,2 | 4.03 | 103.293M | 37.810M | 1.15 | 0.678
zstd,3 | 4.19 | 102.470M | 36.366M | 1.20 | 0.672
zstd,4 | 4.47 | 102.486M | 34.088M | 1.28 | 0.673
zstd,5 | 5.45 | 102.052M | 27.959M | 1.56 | 0.670
zstd,6 | 6.15 | 101.356M | 24.776M | 1.76 | 0.665
zstd,7 | 6.70 | 101.118M | 22.743M | 1.91 | 0.664
zstd,8 | 7.38 | 100.540M | 20.647M | 2.11 | 0.660
zstd,9 | 7.98 | 100.475M | 19.095M | 2.28 | 0.659
zstd,10 | 8.61 | 100.381M | 17.697M | 2.46 | 0.659
zstd,11 | 8.92 | 100.401M | 17.082M | 2.55 | 0.659
zstd,12 | 10.18 | 100.372M | 14.968M | 2.91 | 0.659
zstd,13 | 14.20 | 100.192M | 10.731M | 4.06 | 0.658
zstd,14 | 17.02 | 100.065M | 8952.720K | 4.86 | 0.657
zstd,15 | 18.42 | 100.016M | 8272.274K | 5.26 | 0.656
zstd,16 | 26.25 | 99.234M | 5804.773K | 7.50 | 0.651
zstd,17 | 28.08 | 99.115M | 5426.470K | 8.02 | 0.650
zstd,18 | 32.29 | 98.759M | 4718.962K | 9.23 | 0.648
zstd,19 | 32.89 | 98.824M | 4632.876K | 9.40 | 0.649
zstd,20 | 36.71 | 98.693M | 4150.784K | 10.49 | 0.648
zstd,21 | 36.05 | 98.787M | 4226.776K | 10.30 | 0.648
zstd,22 | 38.81 | 98.750M | 3926.186K | 11.09 | 0.648
auto,lz4 | 3.67 | 114.565M | 41.519M | 1.05 | 0.752
auto,zlib,0 | 3.51 | 114.561M | 43.412M | 1.00 | 0.752
auto,zlib,1 | 5.49 | 105.312M | 27.755M | 1.57 | 0.691
auto,zlib,2 | 5.93 | 104.825M | 25.696M | 1.69 | 0.688
auto,zlib,3 | 6.34 | 104.346M | 24.034M | 1.81 | 0.685
auto,zlib,4 | 6.34 | 104.055M | 24.034M | 1.81 | 0.683
auto,zlib,5 | 7.34 | 103.408M | 20.760M | 2.10 | 0.679
auto,zlib,6 | 9.45 | 102.887M | 16.124M | 2.70 | 0.675
auto,zlib,7 | 11.41 | 102.687M | 13.355M | 3.26 | 0.674
auto,zlib,8 | 19.16 | 102.662M | 7952.781K | 5.47 | 0.674
auto,zlib,9 | 31.11 | 101.507M | 4897.952K | 8.89 | 0.666
auto,lzma,0 | 13.53 | 99.324M | 11.262M | 3.87 | 0.652
auto,lzma,1 | 18.34 | 99.344M | 8308.358K | 5.24 | 0.652
auto,lzma,2 | 18.59 | 99.336M | 8196.627K | 5.31 | 0.652
auto,lzma,3 | 20.88 | 99.000M | 7297.667K | 5.97 | 0.650
auto,lzma,4 | 36.88 | 97.886M | 4131.651K | 10.54 | 0.642
auto,lzma,5 | 41.39 | 97.313M | 3681.451K | 11.83 | 0.639
auto,lzma,6 | 44.97 | 97.227M | 3388.376K | 12.85 | 0.638
auto,lzma,7 | 69.86 | 97.317M | 2181.152K | 19.96 | 0.639
auto,lzma,8 | 97.20 | 97.251M | 1567.647K | 27.77 | 0.638
auto,lzma,9 | 98.05 | 97.219M | 1554.057K | 28.01 | 0.638
auto,zstd,1 | 4.10 | 104.325M | 37.165M | 1.17 | 0.685
auto,zstd,2 | 4.20 | 103.440M | 36.280M | 1.20 | 0.679
auto,zstd,3 | 4.40 | 102.875M | 34.631M | 1.26 | 0.675
auto,zstd,4 | 4.59 | 102.650M | 33.197M | 1.31 | 0.674
auto,zstd,5 | 5.24 | 102.158M | 29.079M | 1.50 | 0.670
auto,zstd,6 | 5.85 | 101.585M | 26.047M | 1.67 | 0.667
auto,zstd,7 | 6.30 | 101.315M | 24.187M | 1.80 | 0.665
auto,zstd,8 | 6.63 | 100.729M | 22.983M | 1.89 | 0.661
auto,zstd,9 | 7.00 | 100.659M | 21.768M | 2.00 | 0.661
auto,zstd,10 | 7.84 | 100.618M | 19.436M | 2.24 | 0.660
auto,zstd,11 | 8.09 | 100.573M | 18.835M | 2.31 | 0.660
auto,zstd,12 | 9.44 | 100.647M | 16.141M | 2.70 | 0.661
auto,zstd,13 | 13.02 | 100.458M | 11.703M | 3.72 | 0.659
auto,zstd,14 | 15.79 | 100.241M | 9650.113K | 4.51 | 0.658
auto,zstd,15 | 18.04 | 100.225M | 8446.524K | 5.15 | 0.658
auto,zstd,16 | 20.32 | 99.521M | 7498.784K | 5.81 | 0.653
auto,zstd,17 | 21.93 | 99.217M | 6948.257K | 6.27 | 0.651
auto,zstd,18 | 25.25 | 99.000M | 6034.665K | 7.21 | 0.650
auto,zstd,19 | 25.25 | 99.152M | 6034.665K | 7.21 | 0.651
auto,zstd,20 | 28.97 | 98.841M | 5259.761K | 8.28 | 0.649
auto,zstd,21 | 29.42 | 98.984M | 5179.309K | 8.41 | 0.650
auto,zstd,22 | 32.85 | 98.918M | 4638.517K | 9.39 | 0.649
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| #!/usr/bin/env php | |
| <?php | |
| /* Author: Romain "Artefact2" Dal Maso <[email protected]> */ | |
| /* This program is free software. It comes without any warranty, to the | |
| * extent permitted by applicable law. You can redistribute it and/or | |
| * modify it under the terms of the Do What The Fuck You Want To Public | |
| * License, Version 2, as published by Sam Hocevar. See | |
| * http://sam.zoy.org/wtfpl/COPYING for more details. */ | |
| const BORG_BINARY = '/home/romain/Downloads/borg-linux64'; | |
| const DATASET_DIR = '/tmp/borg-dataset'; | |
| const BORG_REPO = '/tmp/borg-repo'; | |
| const STATE_FILE = '/tmp/borg-compressbench.state'; | |
| const INIT_COMMAND = '%s init --error -e repokey %s'; | |
| const CREATE_COMMAND = '%s create --error -C %s %s::foo %s'; | |
| const RUNS = 1; | |
| $compressions = [ 'lz4' ]; | |
| foreach([ 'zlib', 'lzma' ] as $algo) { | |
| foreach(range(0, 9) as $level) { | |
| $compressions[] = $algo.','.$level; | |
| } | |
| } | |
| foreach($compressions as $comp) { | |
| $compressions[] = 'auto,'.$comp; | |
| } | |
| array_unshift($compressions, 'none'); | |
| printf("Benchmark date: %s\n", gmdate('Y-m-d')); | |
| printf("Borg version: %s", shell_exec(sprintf('%s --version', escapeshellarg(BORG_BINARY)))); | |
| printf("Python version: %s", shell_exec('python --version')); | |
| printf("Kernel version: %s", shell_exec('uname -sr')); | |
| printf("CPU model: %s", shell_exec('grep "model name" /proc/cpuinfo | head -n1 | cut -d\' \' -f3-')); | |
| printf("\nBorg init command: %s\n", INIT_COMMAND); | |
| printf("Borg create command: %s\n", CREATE_COMMAND); | |
| printf("Dataset path: %s (%s)\n", DATASET_DIR, find_mountpoint(DATASET_DIR)); | |
| printf("Repository path: %s (%s)\n", BORG_REPO, find_mountpoint(BORG_REPO)); | |
| printf("\nAveraging algos over %d run(s)\n", RUNS); | |
| putenv('BORG_PASSPHRASE=foobar'); | |
| if(file_exists(STATE_FILE)) { | |
| $results = json_decode(file_get_contents(STATE_FILE), true); | |
| fprintf(STDERR, "\nWARNING: resuming progress from state file %s\nDelete file if you want to start over.\n", STATE_FILE); | |
| } else { | |
| $results = [];; | |
| } | |
| const FORMAT = "%15s | %10s | %9s | %9s | %9s | %9s\n"; | |
| const IFORMAT = "%15s | %10.2f | %9s | %9s | %9.2f | %9.3f\n"; | |
| printf("\n".FORMAT, "algo", "cputime", "reposize", "bytes/s", "timeratio", "compratio"); | |
| echo strtr(sprintf(FORMAT, '', '', '', '', '', '', '', ''), ' |', '-+'); | |
| foreach($compressions as $comp) { | |
| if(!isset($results[$comp])) { | |
| $results[$comp] = [ | |
| 'runs' => 0, | |
| 'cpu' => 0, | |
| 'size' => 0, | |
| ]; | |
| } | |
| while($results[$comp]['runs'] < RUNS) { | |
| passthru(sprintf('rm -Rf %s', escapeshellarg(BORG_REPO))); | |
| passthru(sprintf(INIT_COMMAND, | |
| escapeshellarg(BORG_BINARY), | |
| escapeshellarg(BORG_REPO))); | |
| $times = shell_exec('bash -c '.escapeshellarg( | |
| sprintf('time -p '.CREATE_COMMAND, | |
| escapeshellarg(BORG_BINARY), | |
| escapeshellarg($comp), | |
| escapeshellarg(BORG_REPO), | |
| escapeshellarg(DATASET_DIR)) | |
| ).' 2>&1'); | |
| preg_match_all('%^(real|user|sys) (?<time>.+)$%m', $times, $matches); | |
| $results[$comp]['cpu'] += floatval($matches['time'][1]); | |
| $results[$comp]['cpu'] += floatval($matches['time'][2]); | |
| $du = shell_exec(sprintf('du -B 1 -s %s', escapeshellarg(BORG_REPO))); | |
| preg_match('%^(?<total>[0-9]+)\s%m', $du, $match); | |
| $results[$comp]['size'] += intval($match['total']); | |
| ++$results[$comp]['runs']; | |
| $f = fopen(STATE_FILE, 'wb'); | |
| fwrite($f, json_encode($results)); | |
| fflush($f); | |
| fclose($f); | |
| } | |
| printf( | |
| IFORMAT, | |
| $comp, | |
| $results[$comp]['cpu'] / $results[$comp]['runs'], | |
| format_bytes($results[$comp]['size'] / $results[$comp]['runs']), | |
| format_bytes(($results['none']['size'] / $results['none']['runs']) / ($results[$comp]['cpu'] / $results[$comp]['runs'])), | |
| ($results[$comp]['cpu'] / $results[$comp]['runs']) / ($results['none']['cpu'] / $results['none']['runs']), | |
| ($results[$comp]['size'] / $results[$comp]['runs']) / ($results['none']['size'] / $results['none']['runs']) | |
| ); | |
| } | |
| function format_bytes(int $b, string $prec = '8.3') { | |
| static $suffixes = [ 'B', 'K', 'M', 'G', 'T', 'P', 'E' ]; | |
| list($total, $decimals) = explode('.', $prec); | |
| $cutoff = pow(10, $total - $decimals - 1); | |
| $i = 0; | |
| while($b >= $cutoff) { | |
| $b /= 1000; | |
| ++$i; | |
| } | |
| return sprintf("%".$prec."f%s", $b, $suffixes[$i]); | |
| } | |
| function find_mountpoint($path) { | |
| if($del = !file_exists($path)) { | |
| touch($path); | |
| } | |
| $dev = shell_exec(sprintf('df %s | tail -n1 | cut -d\' \' -f1', escapeshellarg($path))); | |
| if($del) unlink($path); | |
| return substr($dev, 0, -1); | |
| } |
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| #!/bin/sh | |
| # Run me in single-user mode: | |
| # systemctl isolate rescue.target | |
| for i in 1 2 3; do | |
| rm -Rf /tmp/borg-dataset /tmp/borg-repo /tmp/borg-compressbench.state | |
| cp -a /tmp/borg-dataset$i /tmp/borg-dataset | |
| ./borg-compressbench | tee bcb$i.txt | |
| mv /tmp/borg-compressbench.state bcb$i.state | |
| done |
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| #!/usr/bin/python3 | |
| import sys | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| data = {} | |
| stats = {} | |
| average = {} | |
| infiles = sys.argv[1:] | |
| Ninfiles = len(infiles) | |
| for file, n in zip( infiles, range(Ninfiles) ): | |
| data[n] = np.genfromtxt(file, skip_header=19, skip_footer=1, delimiter="|", dtype='U20,f4,U20,U20,f4,f4') | |
| data[n] = data[n].tolist() | |
| data[n] = [ [cell.strip() if type(cell) == str else cell for cell in row] for row in data[n] ] | |
| data[n] = np.array(data[n]) | |
| stats[n] = {} | |
| stats[n]['algo'] = np.array(data[n][:,0], dtype=str) | |
| stats[n]['timeratio'] = np.array(data[n][:,4], dtype=float) | |
| stats[n]['compratio'] = np.array(data[n][:,5], dtype=float) | |
| stats[n]['eff'] = [ stats[n]['timeratio'][i] * stats[n]['compratio'][i] for i in range( len( stats[n]['timeratio'] ) ) ] | |
| stats[n]['eff'] = [ -i+2 for i in stats[n]['eff'] ] | |
| with open(file) as f: | |
| stats[n]['description'] = f.readline() | |
| ################## Calculate Average ################## | |
| last = len(stats) | |
| stats[last] = {} | |
| stats[last]['algo'] = stats[0]['algo'].copy() | |
| stats[last]['timeratio'] = stats[0]['timeratio'].copy() | |
| stats[last]['compratio'] = stats[0]['compratio'].copy() | |
| stats[last]['eff'] = stats[0]['eff'].copy() | |
| stats[last]['description'] = 'Mean' | |
| for i in range(len(stats[0]['algo'])): | |
| stats[last]['timeratio'][i] = np.mean( [ stats[file]['timeratio'][i] for file in range(Ninfiles) ] ) | |
| stats[last]['compratio'][i] = np.mean( [ stats[file]['compratio'][i] for file in range(Ninfiles) ] ) | |
| stats[last]['eff'][i] = np.mean( [ stats[file]['eff'][i] for file in stats ] ) | |
| ####################################################### | |
| Nplots = [ stats[key]['description'] for key in stats.keys() ] | |
| fig = plt.figure(figsize=(30,5)) | |
| xticklabels = data[0][:,0] | |
| plotx = range( len( xticklabels ) ) | |
| ax_size = fig.add_subplot(111) | |
| ax_size.set_xticks(plotx) | |
| ax_size.set_xticklabels(xticklabels) | |
| ax_size.set_title('Borg Compression Comparison') | |
| ax_size.set_ylim([0,1]) | |
| xlabel = 'Compr. Algorithm[, Compr. Factor]' | |
| ylabel_size = 'Size Relative to No Compression' | |
| ylabel_time = 'Compression Time Relative to No Compression' | |
| ylabel_efficiency = r'Efficiency: $(-1)\cdot$ Fraction Time $\cdot$ Fraction Size $+ 2$' | |
| ax_time = ax_size.twinx() | |
| ax_efficiency = ax_size.twinx() | |
| colorfracs = [i / (Ninfiles+.1) if i > 0 else 0 for i in range( len(Nplots) )] | |
| # Actual plotting | |
| lns = [] | |
| for cfrac, n in zip( colorfracs, range(Ninfiles+1) ): | |
| color = plt.cm.viridis(cfrac) | |
| l_size, = ax_size.plot(plotx, stats[n]['compratio'], | |
| color=color, linestyle='-.', | |
| label='Size Ratio: {}'.format(stats[n]['description'])) | |
| l_time, = ax_time.plot(plotx, stats[n]['timeratio'], | |
| color=color, linestyle=':', | |
| label='Time Ratio: {}'.format(stats[n]['description'])) | |
| l_eff, = ax_efficiency.plot(plotx, stats[n]['eff'], | |
| color=color, linestyle='-', | |
| label='Efficiency: {}'.format(stats[n]['description'])) | |
| lns.append(l_size) | |
| lns.append(l_time) | |
| lns.append(l_eff) | |
| ax_efficiency.spines['right'].set_position(('outward', 60)) | |
| ax_size.xaxis.grid(color='.85', linestyle='-', linewidth=.5) | |
| for tick in ax_size.get_xticklabels(): | |
| tick.set_rotation(45) | |
| tick.set_horizontalalignment('right') | |
| ax_size.set_xlabel(xlabel) | |
| ax_size.set_ylabel(ylabel_size) | |
| ax_efficiency.set_ylabel(ylabel_efficiency) | |
| ax_time.set_ylabel(ylabel_time) | |
| lgd = ax_time.legend(handles=lns, loc='upper center', ncol=2, | |
| bbox_to_anchor=(0.5, -.35), facecolor='#FFFFFF', | |
| prop={'family': 'monospace','size': 'medium'}) | |
| fig.savefig('eval.svg', bbox_inches='tight', dpi=300) |
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