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https://github.com/lemire/SIMDCompressionAndIntersection

A C++ library to compress and intersect sorted lists of integers using SIMD instructions
https://github.com/lemire/SIMDCompressionAndIntersection

algorithms compression integer-compression intersection simd simd-instructions

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A C++ library to compress and intersect sorted lists of integers using SIMD instructions

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README 

        
SIMDCompressionAndIntersection

======================

[![Ubuntu 22.04 CI (GCC 11)](https://github.com/lemire/SIMDCompressionAndIntersection/actions/workflows/ubuntu22.yml/badge.svg)](https://github.com/lemire/SIMDCompressionAndIntersection/actions/workflows/ubuntu22.yml)

[![VisualStudio](https://github.com/lemire/SIMDCompressionAndIntersection/actions/workflows/visualstudio.yml/badge.svg)](https://github.com/lemire/SIMDCompressionAndIntersection/actions/workflows/visualstudio.yml)



As the name suggests, this is a C/C++ library for fast

compression and intersection of lists of sorted integers using

SIMD instructions. The library focuses on innovative techniques

and very fast schemes, with particular attention to differential

coding. It introduces new SIMD intersections schemes such as

SIMD Galloping.



This library can decode at least 4 billions of compressed integers per second on most

desktop or laptop processors. That is, it can decompress data at a rate of 15 GB/s.

This is significantly faster than generic codecs like gzip, LZO, Snappy or LZ4.



Authors: Leonid Boystov, Nathan Kurz,  Daniel Lemire,

Owen Kaser, Andrew Consroe, Shlomi Vaknin, Christoph Rupp, Bradley Grainger, and others.



Documentation

-----------------------



* Daniel Lemire, Nathan Kurz, Christoph Rupp, Stream VByte: Faster Byte-Oriented Integer Compression, Information Processing Letters 130, 2018 https://arxiv.org/abs/1709.08990

* Daniel Lemire, Leonid Boytsov, Nathan Kurz, SIMD Compression and the Intersection of Sorted Integers, Software Practice & Experience 46 (6), 2016 http://arxiv.org/abs/1401.6399

* Daniel Lemire and Leonid Boytsov, Decoding billions of integers per second through vectorization, Software Practice & Experience 45 (1), 2015.  http://arxiv.org/abs/1209.2137 http://onlinelibrary.wiley.com/doi/10.1002/spe.2203/abstract

* Jeff Plaisance, Nathan Kurz, Daniel Lemire, Vectorized VByte Decoding, International Symposium on Web Algorithms 2015, 2015. http://arxiv.org/abs/1503.07387

* Wayne Xin Zhao, Xudong Zhang, Daniel Lemire, Dongdong Shan, Jian-Yun Nie, Hongfei Yan, Ji-Rong Wen, A General SIMD-based Approach to Accelerating Compression Algorithms, ACM Transactions on Information Systems 33 (3), 2015. http://arxiv.org/abs/1502.01916



This work has also inspired other work such as...



* P. Damme, D. Habich, J. Hildebrandt, W. Lehner, Lightweight Data Compression Algorithms: An Experimental Survey (Experiments and Analyses), EDBT 2017 http://openproceedings.org/2017/conf/edbt/paper-146.pdf

* P. Damme, D. Habich, J. Hildebrandt, W. Lehner, Insights into the Comparative Evaluation of Lightweight Data Compression Algorithms, EDBT 2017 http://openproceedings.org/2017/conf/edbt/paper-414.pdf

* T. D. Wu, Bitpacking techniques for indexing genomes: I. Hash tables, Algorithms for Molecular Biology 11 (5), 2016. http://almob.biomedcentral.com/articles/10.1186/s13015-016-0069-5

* Jianguo Wang, Chunbin Lin, Yannis Papakonstantinou, Steven Swanson, An Experimental Study of Bitmap Compression vs. Inverted List Compression, SIGMOD 2017 http://db.ucsd.edu/wp-content/uploads/2017/03/sidm338-wangA.pdf



Simple demo

------------------------



Check out example.cpp



You can run it like so (e.g., under Linux or macOS):



Usage (Linux, macOS and similar systems)

------------------------



```

make

./unit

```



A static library file is built as `libSIMDCompressionAndIntersection.a` which you can use in your

own projects along with our header files located in the `include` subdirectory.



You may also build and run our example:



```

make example

./example

```



To run tests, you can do 

```

./testcodecs

```



(follow the instructions)



Usage (Windows users)

------------------------



Windows users wishing to build using Visual Studio should go into a Developer Powershell, which is

accessible through the menus in the Visual Studio interface, and run the following from the 

directory of the project:



```

nmake -f .\makefile.vc

 .\example.exe

 .\unit.exe

 ```



Under Windows, the static library is built as the file `simdcomp_a.lib` which you can use in your

own projects, along with our header files located in the `include` subdirectory.



For a simple C library

----------------------



This library is a C++ research library. For something simpler,

written in C, see:



https://github.com/lemire/simdcomp



Comparison with the FastPFOR C++ library

-----------------------------------------



The FastPFOR C++ Library available at https://github.com/lemire/FastPFor

implements some of the same compression schemes except that

it is not optimized for the compression of sorted lists of integers.



Other recommended libraries

---------------------------------



* libvbyte: A fast implementation for varbyte 32bit/64bit integer compression https://github.com/cruppstahl/libvbyte

* TurboPFor is a C library that offers lots of interesting optimizations. Well worth checking! (GPL license) https://github.com/powturbo/TurboPFor

* Oroch is a C++ library that offers a usable API (MIT license) https://github.com/ademakov/Oroch

* CSharpFastPFOR: A C#  integer compression library  https://github.com/Genbox/CSharpFastPFOR

* JavaFastPFOR: A java integer compression library https://github.com/lemire/JavaFastPFOR

* Encoding: Integer Compression Libraries for Go https://github.com/zhenjl/encoding

* FrameOfReference is a C++ library dedicated to frame-of-reference (FOR) compression: https://github.com/lemire/FrameOfReference



Licensing

------------------------



Apache License, Version 2.0



As far as the authors know, this work is patent-free.



Requirements

------------------------



A CPU (AMD or Intel) with support for SSE2 (Pentium 4 or better) is required

while a CPU with SSE 4.1* (Penryn  [2007] processors or better) is recommended. 



A recent GCC (4.7 or better), Clang, Intel or Visual C++ compiler.



A processor support AVX (Intel or AMD).



Tested on Linux, MacOS and Windows. It should be portable to other platforms.



*- The default makefile might assume AVX support, but AVX is not required. For GCC

compilers, you might need the -msse2 flag, but you will not need the -mavx flag.



For advanced benchmarking, please see



advancedbenchmarking/README.md



where there is additional information as well as links to real data sets.



Acknowledgement

------------------------



Thanks to Kelly Sommers for useful feedback.



This work was supported by NSERC grant number 26143.