# wrs

**Repository Path**: bxtx/wrs

## Basic Information

- **Project Name**: wrs
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: GPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-07-14
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Parallel Weighted Random Sampling

This is the code to accompany our eponymous paper: *Hübschle-Schneider, L., & Sanders, P. (2019). Parallel Weighted Random Sampling. In 27th Annual European Symposium on Algorithms (ESA 2019).*

The paper is freely accessible under a Creative Commons license (CC-BY) [on the publisher's website](https://drops.dagstuhl.de/opus/volltexte/2019/11180/).

If you use this library in the context of an academic publication, we ask that you cite our paper:
```bibtex
@InProceedings{HubSan2019weightedsampling,
  author =	{Lorenz H{\"u}bschle-Schneider and Peter Sanders},
  title =	{{Parallel Weighted Random Sampling}},
  booktitle =	{27th Annual European Symposium on Algorithms (ESA 2019)},
  pages =	{59:1--59:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-124-5},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{144},
  editor =	{Michael A. Bender and Ola Svensson and Grzegorz Herman},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2019/11180},
  doi =		{10.4230/LIPIcs.ESA.2019.59},
}
```

### Building

Build with cmake (version 3.9.2 or later is required). Remember to fetch the submodules before compiling: `git submodule update --init`. A compiler compatible with C++17 is required.

**Optional Dependencies:** [Intel Math Kernel Library (MKL)](https://software.intel.com/en-us/mkl) for faster random variate generation, [libnuma](https://github.com/numactl/numactl) for Non-Uniform Memory Access (NUMA) awareness, [GNU Scientific Library (GSL)](http://www.gnu.org/software/gsl/) for their alias table implementation (only for comparison in the experiments).

### Experiments

To reproduce our experiments, compile using cmake and execute [benchmark/bench_all.sh](benchmark/bench_all.sh) from your build directory.
Note that this script targets a machine with 80 cores (160 threads).  A version for 32 cores (64 threads) is provided in [benchmark/bench_all_64.sh](benchmark/bench_all_64.sh).
Adjust this to your machine by changing the values to the `-t` parameter in `benchmark/run_*.sh`.
You might also want to change the output paths in `benchmark/run_*.sh`

**[License](/LICENSE):** GPLv3