How to run these benchmarks
I ran this on an Amazon EC2
m2.4xlarge instance with the Red Hat 6.1 x86_64 AMI (
$ yum install make-3.81-19.el6.x86_64 \ gcc-4.4.5-6.el6.x86_64 \ gcc-c++-4.4.5-6.el6.x86_64 \ python-devel-2.6.6-20.el6.x86_64 \ glib2-devel-2.22.5-6.el6.x86_64 \ boost-devel-1.41.0-11.el6_1.2.x86_64 \ qt-devel-4.6.2-17.el6_1.1.x86_64 \ git $ wget http://google-sparsehash.googlecode.com/files/sparsehash-1.11-1.noarch.rpm $ rpm -i sparsehash-1.11-1.noarch.rpm $ git clone git://github.com/timonk/hash-table-shootout.git
Nick's original benchmark with higher key count and restricted to just random integer inserts:
$ cd hash-table-shootout $ mkdir build $ make $ python bench.py $ python make_chart_data.py < output | python make_html.py
Your charts are now in charts.html.
You can tweak some of the values in bench.py to make it run faster at the
expense of less granular data, and you might need to tweak some of the
To run the benchmark at the highest priority possible, do this:
$ sudo nice -n-20 ionice -c1 -n0 sudo -u $USER python bench.py
since I'm running this as
root on EC2, it's simply:
$ nice -n-20 ionice -c1 -n0 python bench.py
You might also want to disable any swap files/partitions so that swapping doesn't influence performance. (The programs will just die if they try to allocate too much memory.)
To run the throughput benchmark:
$ cd hash-table-shootout $ mkdir build_throughput $ make -f MakefileThroughput $ python bench_throughput.py
The results can be found in
output_throughput. I just hacked up my own charts, but I might add a similar utility to generate charts like Nick's.
You can use the same
ionice command line as above to run it at highest priority, but replace
Modified by Timon Karnezos in 2011.
Originally written by Nick Welch in 2010.
No copyright. This work is dedicated to the public domain.
For full details, see http://creativecommons.org/publicdomain/zero/1.0/