This is a follow-up to an experiment that I started on May 8, 2015.
If you just came to help out, then skip to the bottom.
If you just came to read about the result, then you can read the paper.
It turns out that, because of cache effects, sorted order is usually not the most efficient way to store data for searching. In this project, we study the performance of different memory layouts for binary searching:
sorted: A usual sorted array with binary search applied to it.
eytzinger: An implicit binary search tree packed into an array using the Eytzinger (a.k.a. BFS) layout usually seen with binary heaps.
btree: An implicit B-tree packed into an array using the obvious generalization of the Eytzinger layout. The value B here is chosen so that B-1 data items fit into 64 bytes (the most common cache line width).
veb: An implicit binary search tree packed into an array using the van Emde Boas layout seen in the cache-oblivious literature.
The last time I asked this question, my answer was the
The answer is complicated, and it seems to depend
on the data size, the cache size, the cache line width, and the
relative cache speed. Since then I've been working with Paul Khuong, and we've come to the
conclusion that the answer is not really that complicated:
The Eytzinger layout offers the best all-around performance
over a wide range of array lengths. To understand why, you can
read the paper.
Spoiler: It has to do do with hiding latency by overusing bandwidth. (Hacker News user derf_9 has a nice summary.)
Most of the data we've collected so far supports our hypothesis, but it's always nice to have more data. If you have a Linux machine and would like to contribute to this effort, then please follow the instructions below.
$ tar xzvf arraylayout.tgz arraylayout-0.1/ arraylayout-0.1/Makefile arraylayout-0.1/eytzinger_array.h arraylayout-0.1/main.cpp arraylayout-0.1/btree_array.h arraylayout-0.1/sorted_array.h arraylayout-0.1/cacher.cpp arraylayout-0.1/README arraylayout-0.1/veb_array.h arraylayout-0.1/base_array.hand then run it like this:
$ cd arraylayout-0.1 ; make dataIf you're on a Mac, the procedure is similar:
$ cd arraylayout-0.1 ; make -f Makefile.macos dataThis will create file called
run_data.tgzthat you can just email me. If you want to be extra helpful, you can also run:
sudo dmidecode --type 17and include the output in your email. This will give me detailed information about the amount, type, and speed of memory installed in your computer, as described here.