# Compressor comparison for GHC binary distributions

| tagged with
• data compression

Recently I noticed that GHC’s validation script spends a significant portion of its runtime preparing a compressed binary distribution with xz. This perhaps shouldn’t be surprising as the build system uses the extremely aggressive -9e flag set.

The obvious next question is what all of this carbon emissionCPU time buys us. To quantify this I took an uncompressed GHC binary distribution tarball (around 1.1 gigabytes of tar’d binaries and some text) and compressed it with various configurations of gzip, bzip2, and xz, recording the user-space runtime, final size, and maximum resident set size of each,

(for i in $(seq 1 9); do cat ghc-8.0.0.20160111-i386-centos67-linux.tar | /usr/bin/time -f "%U %M" -o time.log gzip -$i
| wc -c >| size.log;
echo "$i$(cat time.log) \$(cat size.log)";
done) | tee results.log

After cleaning up the results I arrived at the following,

There aren’t many surprises here (with the usual caveats: this is an unscientific study of a particular workload, etc.),

• xz compresses better than bzip2, which compresses better than gzip
• Investing more time in compression helps all compressors, although to varying degrees
• Additional compression comes at a significant cost in the case of xz
• Perhaps most surprising, xz -e hardly makes a difference, despite being significantly more expensive
• Unsurprisingly, xz’s improved compression does come at a significant cost in memory consumption; while all other compressors have a roughly constant memory consumption of a few megabytes, xz continues to balloon as the compression level is raised, reaching nearly 700 megabytes at -9.

# Code and data.

The raw data of this study is available here. Results were plotted thusly.

import numpy as np
import matplotlib.pyplot as pl

d = np.genfromtxt('comparison.csv', names=True, dtype=None)
uncomp_size = d[0]['output_size_bytes']
d = d[1:]
comp_ratio = 1. * d['output_size_bytes'] / uncomp_size

compressors = np.unique(d['compressor'])
print compressors
for color, compressor in zip('rgbc', compressors):
ds = d['compressor'] == compressor
pl.scatter(d[ds]['user_time_sec'], comp_ratio[ds], color=color, label=compressor)

pl.xlabel('user time (seconds)')
pl.ylabel('compression ratio')
pl.legend()
pl.xlim(0, None)
pl.show()