7cf772d968
Lots of changes: - Article about IPC - New TUWF release - New ncdu release - Atom feeds for the bug tracker - Bug tracker switch to sqlite
84 lines
3.7 KiB
Text
84 lines
3.7 KiB
Text
=pod
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I<2013-07-05> - One of my favourite data structures in C is the ordered vector
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(or array, whatever you call them). Incredibly simple to implement, very low
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memory overhead, and can provide O(log n) lookup with a simple binary search.
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However, ordered vectors have one very weak point: insertion and deletion of
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items is O(n). For small n that doesn't really matter, but if the number of
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items in the list can grow a bit, you may run into performance issues. If
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you're not careful, this could even turn your ordered vector into an attack
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vector (apologies for the terrible pun).
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My goal with this benchmark is to get a feeling on how, exactly, insertion
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performance behaves with an ordered vector. What values of n are "small"? And
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how much worse does insertion performance get compared to more complex data
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structures?
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For comparison, I chose the B-tree and hash table implementations from
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L<klib|https://github.com/attractivechaos/klib> (from commit fff70758, to be
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precise). My goal wasn't to benchmark the performance of different
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implementations, so I simply chose two implementations that I suspect are among
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the fastest. The vector implementation in the benchmarks is my own creation:
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L<vec.h|http://g.blicky.net/globster.git/tree/src/util/vec.h?id=2c11d2a> from
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the L<Globster|https://dev.yorhel.nl/globster> code base.
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B<Source code:> L<ins-bench.c|http://p.blicky.net/r746e>
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=head2 Best case & worst case
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For a start, I decided to benchmark the best and worst case performance of
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inserting elements into a vector. The best case happens when inserting all
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items at the end of the vector, the worst case when inserting them in front.
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The B-tree and hash table benchmarks provided for comparison have all items
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inserted in order.
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I'm cheating here with the vector implementation, because all elements are
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inserted in the list without first finding out the position with a binary
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search. Actual performance will be thus be a bit worse, depending on whether
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the final application needs that binary search or whether it can assume its
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input to be already sorted.
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L<[img graph insbench-bench-thumb.png ]|https://dev.yorhel.nl/img/insbench-bench.png>
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Gnuplot script: (The awk(ward) part can likely be done natively in gnuplot as
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well, but I was too lazy to figure out how)
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set terminal png size 1000, 1500
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set output "bench.png"
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set logscale xy
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set xlabel "number of items"
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set ylabel "average time per insert (ms)"
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set grid mxtics xtics mytics ytics
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plot "< awk '{print $1, $2/$1*1000}' bench-vec" title 'vector, worst case',\
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"< awk '{print $1, $2/$1*1000}' bench-best" title 'vector, best case',\
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"< awk '{print $1, $2/$1*1000}' bench-hash" title 'khash',\
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"< awk '{print $1, $2/$1*1000}' bench-btree" title 'kbtree'
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=head2 Average case
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For the second benchmark I inserted values created with C<rand()>, which should
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be a more accurate simulation of some real-world applications. This time I'm
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not cheating with the vector implementation, a binary search is performed in
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order to insert the items in the correct location.
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L<[img graph insbench-rand-thumb.png ]|https://dev.yorhel.nl/img/insbench-rand.png>
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set terminal png size 1000, 1500
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set output "bench-rand.png"
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set logscale xy
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set xlabel "number of items"
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set ylabel "average time per insert (ms)"
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set grid mxtics xtics mytics ytics
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plot "< awk '{print $1, $2/$1*1000}' rand-vec" title 'vector',\
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"< awk '{print $1, $2/$1*1000}' rand-hash" title 'khash',\
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"< awk '{print $1, $2/$1*1000}' rand-btree" title 'kbtree'
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=head2 Benchmarking setup
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All benchmarks were performed on a 3 GHz Core Duo E8400 with a 6 MiB cache.
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Compiled with the Gentoo-provided gcc 4.6.3 at -O3, linked against glibc 2.15,
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and run on a Linux 3.8.13-gentoo kernel. Boring details, but somehow good to
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document.
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