arraylayout
Experiments with memory layouts for binary search.
These are the sources (LaTeX, C++, and Python) for the paper
ARRAY LAYOUTS FOR COMPARISON-BASED SEARCHING
by Paul-Virak Khuong and Pat Morin
About
This project is about trying different memory layouts for searching in an array, with a focus on improving performance for the most common-case: When the input fits entirely in RAM. The layouts we try are:
- sorted: The usual sorted array on which we apply binary search
- eytzinger: The Eytzinger (or BFS) layout most commonly used implicit binary heaps
- btree: A (B+1)-ary variant of the Eytzinger layout
- veb: The van Emde Boas layout from the cache-oblivious literature
Quick start
To run an experiment on your own (linux) machine (with g++ installed):
cd src
make main
./main uint32 uint32 100000000 10000000
This will test the cost of performing ten million searches in an array of one hundred million integers. You'd better have at least 800MB of free RAM, which you can check with
free -h
Replicated Computational Results
This work is in the process of earning a Replicated Computational Result certificate from the ACM Journal of Experimental Algorithmics. To achieve this, we have developed scripts that allow anyone with a suitable system to reproduce the computational results (in this case, figures) in the paper.
If you want to try and reproduce our results, execute
./rcr
For this to work, you will need (at least) the following software installed
- g++
- lscpu
- free
- numactl
- python with matplotlib
- pdflatex
To fully replicate the experiments in the paper, you will need at least 15GB of free RAM. The experiments will scale down to use at most 2/3 of your available RAM otherwise.
Using the preceding script will generate PDF and SVG figures in the scripts/figs-rcr directory. You can view the resulting SVG files in a browser by opening scripts/figs-rcr/index.html
Results
A webpage describing this work and with some collected results is being maintained here: