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<!-- TABLE OF CONTENTS -->
<details>
<summary>Table of Contents</summary>
<ol>
<li>
<a href="#about-the-project">About The Project</a>
</li>
<li>
<a href="#getting-started">Getting Started</a>
<ul>
<li><a href="#installation">Installation</a></li>
<li><a href="#usage">Usage</a></li>
<li><a href="#example">Example</a></li>
</ul>
</li>
<li><a href="#benchmark">Benchmark</a></li>
<li><a href="#roadmap">Roadmap</a></li>
<li><a href="#contributing">Contributing</a></li>
<li><a href="#license">License</a></li>
<li><a href="#reference">Reference</a></li>
<li><a href="#contact">Contact</a></li>
</ol>
</details>
<!-- ABOUT THE PROJECT -->
## About The Project
`libstreamvbyte` is a `C++` implementation of [StreamVByte](#reference), with `Python` bindings using `pybind11`.
[StreamVByte](#reference) is an integer compression technique that use SIMD instructions (vectorization) to improve performance. The library is optimized with `SSSE3` intrinsics, which are supported by most `x86_64` processors. It uses `sse2neon` to translate `SSSE3` intrinsics to `NEON` intrinsics for `ARM` processors. The library can also be used with other 32-bit architectures, although it will fall back to scalar implementations in those cases.
With `libstreamvbyte`, you can quickly and efficiently compress integer sequences, reducing the amount of storage space and network bandwidth required. The library is easy to use and integrates seamlessly with `Python` via `pybind11` bindings. Whether you're working with large datasets or building a distributed computing system, `libstreamvbyte` can help you improve performance and reduce the resources needed to handle your data.
Currently supports `Python 3.8+` on Windows, Linux (`manylinux_2_17`, `musllinux_1_1`) and macOS (`universal2`).
<p align="right">(<a href="#top">back to top</a>)</p>
<!-- GETTING STARTED -->
## Getting Started
### Installation
#### For `Python`
Install from `PyPI` using `pip`.
```bash
pip install libstreamvbyte
```
Or install from `.whl` file.
```bash
pip install "path/to/your/downloaded/whl"
```
To find appropriate `.whl` file, please visit [releases](https://github.com/wst24365888/libstreamvbyte/releases).
#### For `C++`
You must have `CMake` installed on your system.
```bash
# clone the repo
git clone https://github.com/wst24365888/libstreamvbyte
cd libstreamvbyte
# build and install
cmake .
make
sudo make install
```
### Usage
#### For `Python`
Import `libstreamvbyte` first.
```python
import libstreamvbyte as svb
```
And here are the APIs.
```python
# Encode an array of unsigned integers into a byte array.
encode(in_uint32: numpy.ndarray[numpy.uint32]) -> numpy.ndarray[numpy.uint8]
# Decode a byte array into an array of unsigned integers.
decode(in_uint8: numpy.ndarray[numpy.uint8], size: int) -> numpy.ndarray[numpy.uint32]
# Encode an array of signed integers into an array of unsigned integers.
zigzag_encode(in_int32: numpy.ndarray[numpy.int32]) -> numpy.ndarray[numpy.uint32]
# Decode an array of unsigned integers into an array of signed integers.
zigzag_decode(in_uint32: numpy.ndarray[numpy.uint32]) -> numpy.ndarray[numpy.int32]
# Check if the current wheel is a vectorized version.
is_vectorized_version() -> bool
```
#### For `C++`
Include `streamvbyte.h` first.
```cpp
#include "streamvbyte.h"
```
For the APIs, please refer to [include/streamvbyte.h](https://github.com/wst24365888/libstreamvbyte/blob/main/include/streamvbyte.h).
### Example
#### For `Python`
```python
import libstreamvbyte as svb
N = 2**20 + 2
# type(original_data) == np.ndarray
# original_data.dtype == np.int32
original_data = np.random.randint(-2**31, 2**31, N, dtype=np.int32)
# type(compressed_bytes) == np.ndarray
# compressed_bytes.dtype == np.uint8
compressed_bytes = svb.encode(svb.zigzag_encode(original_data))
# type(recovered_data) == np.ndarray
# recovered_data.dtype == np.int32
recovered_data = svb.zigzag_decode(svb.decode(compressed_bytes, N))
```
#### For `C++`
```cpp
#include "streamvbyte.h"
int main() {
std::size_t N = (1 << 20) + 2;
std::vector<int32_t> original_data(N);
for (std::size_t i = 0; i < N; ++i) {
original_data[i] = rand() - rand();
}
std::vector<uint8_t> compressed_bytes = streamvbyte::encode(streamvbyte::zigzag_encode(original_data));
std::vector<int32_t> recovered_data = streamvbyte::zigzag_decode(streamvbyte::decode(compressed_bytes, N));
return 0;
}
```
Compile it with linking to `libstreamvbyte`.
```bash
g++ -o example example.cpp -lstreamvbyte
```
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<!-- BENCHMARK -->
## Benchmark
```bash
OS: Linux 5.15.79.1-microsoft-standard-WSL2 x86_64
CPU: AMD Ryzen 5 3600 6-Core Processor (12) @ 3.600GHz
Run on (12 X 3593.26 MHz CPU s)
CPU Caches:
L1 Data 32 KiB (x6)
L1 Instruction 32 KiB (x6)
L2 Unified 512 KiB (x6)
L3 Unified 16384 KiB (x1)
Load Average: 0.81, 0.85, 0.69
-----------------------------------------------------------------------------------
Benchmark Time CPU Iterations Throughput
-----------------------------------------------------------------------------------
BM_memcpy/4096 149 ns 149 ns 4688531 13.7122G/s
BM_memcpy/8192 548 ns 548 ns 1275803 7.46783G/s
BM_memcpy/16384 1139 ns 1138 ns 640835 7.19553G/s
BM_memcpy/32768 2185 ns 2185 ns 320840 7.49932G/s
BM_memcpy/65536 4921 ns 4921 ns 142703 6.65895G/s
BM_memcpy/131072 10968 ns 10968 ns 63502 5.97511G/s
BM_memcpy/262144 22465 ns 22465 ns 31134 5.83457G/s
BM_memcpy/524288 45101 ns 45100 ns 15541 5.81245G/s
BM_memcpy/1048576 91131 ns 91131 ns 7639 5.75314G/s
BM_streamvbyte_encode/4096 1222 ns 1222 ns 580855 1.67556G/s
BM_streamvbyte_encode/8192 2470 ns 2467 ns 282349 1.66064G/s
BM_streamvbyte_encode/16384 4945 ns 4945 ns 139671 1.65662G/s
BM_streamvbyte_encode/32768 9990 ns 9989 ns 70497 1.64017G/s
BM_streamvbyte_encode/65536 19853 ns 19853 ns 30963 1.65051G/s
BM_streamvbyte_encode/131072 39933 ns 39932 ns 17401 1.64118G/s
BM_streamvbyte_encode/262144 80563 ns 80562 ns 8193 1.62697G/s
BM_streamvbyte_encode/524288 160716 ns 160716 ns 4284 1.6311G/s
BM_streamvbyte_encode/1048576 319253 ns 319253 ns 1942 1.64223G/s
BM_streamvbyte_decode/4096 691 ns 691 ns 1040462 2.96191G/s
BM_streamvbyte_decode/8192 1341 ns 1341 ns 516979 3.05539G/s
BM_streamvbyte_decode/16384 2683 ns 2683 ns 261208 3.05359G/s
BM_streamvbyte_decode/32768 5348 ns 5348 ns 130319 3.06353G/s
BM_streamvbyte_decode/65536 10817 ns 10817 ns 64427 3.02936G/s
BM_streamvbyte_decode/131072 23207 ns 23207 ns 31546 2.824G/s
BM_streamvbyte_decode/262144 45746 ns 45746 ns 11291 2.86519G/s
BM_streamvbyte_decode/524288 88660 ns 88660 ns 7947 2.95673G/s
BM_streamvbyte_decode/1048576 178497 ns 178497 ns 3907 2.93724G/s
BM_zigzag_encode/4096 810 ns 810 ns 854076 2.52829G/s
BM_zigzag_encode/8192 1611 ns 1608 ns 433154 2.548G/s
BM_zigzag_encode/16384 3174 ns 3174 ns 219165 2.58084G/s
BM_zigzag_encode/32768 6457 ns 6457 ns 108415 2.53754G/s
BM_zigzag_encode/65536 12582 ns 12582 ns 54747 2.60432G/s
BM_zigzag_encode/131072 25243 ns 25243 ns 27802 2.59617G/s
BM_zigzag_encode/262144 50278 ns 50278 ns 13952 2.60693G/s
BM_zigzag_encode/524288 100563 ns 100562 ns 6932 2.60678G/s
BM_zigzag_encode/1048576 211846 ns 211845 ns 3222 2.47487G/s
BM_zigzag_decode/4096 675 ns 675 ns 1041044 3.03263G/s
BM_zigzag_decode/8192 1342 ns 1342 ns 523553 3.05196G/s
BM_zigzag_decode/16384 2643 ns 2643 ns 265497 3.09905G/s
BM_zigzag_decode/32768 5383 ns 5383 ns 130976 3.04377G/s
BM_zigzag_decode/65536 11474 ns 11474 ns 60817 2.85588G/s
BM_zigzag_decode/131072 21777 ns 21777 ns 32345 3.00944G/s
BM_zigzag_decode/262144 43477 ns 43478 ns 14387 3.0147G/s
BM_zigzag_decode/524288 86120 ns 86120 ns 8145 3.04393G/s
BM_zigzag_decode/1048576 173095 ns 173093 ns 4028 3.02894G/s
```
> The unit of `Throughput` is `GB/s` (Giga Bytes per second).
### Build Benchmarks from Source
```bash
cmake . \
-DCMAKE_BUILD_TYPE=Release \
-DBUILD_SHARED_LIBS=OFF \
-DBUILD_PYBIND11=OFF \
-DPRINT_BENCHMARK=OFF \
-DBUILD_TESTS=ON \
-DBUILD_BENCHMARKS=ON
make libstreamvbyte_benchmarks
./libstreamvbyte_benchmarks --benchmark_counters_tabular=true
```
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<!-- ROADMAP -->
## Roadmap
- [x] Zigzag encoding/decoding.
- [x] Support ARM processors with `NEON` intrinsics.
- [ ] Differential coding (delta encoding/decoding).
See the [open issues](https://github.com/wst24365888/libstreamvbyte/issues)
for a full list of proposed features (and known issues).
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<!-- CONTRIBUTING -->
## Contributing
Contributions are what make the open source community such an amazing place to
learn, inspire, and create. Any contributions you make are **greatly
appreciated**.
If you have a suggestion that would make this better, please fork the repo and
create a pull request. You can also simply open an issue with the tag
"enhancement". Don't forget to give the project a star! Thanks again!
1. Fork the Project
2. Create your Feature Branch (`git checkout -b feat/amazing-feature`)
3. Commit your Changes with
[Conventional Commits](https://www.conventionalcommits.org/en/v1.0.0/)
4. Push to the Branch (`git push origin feat/amazing-feature`)
5. Open a Pull Request
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<!-- LICENSE -->
## License
Distributed under the MIT License. See
[LICENSE](https://github.com/wst24365888/libstreamvbyte/blob/main/LICENSE)
for more information.
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<!-- REFERENCE -->
## Reference
- Daniel Lemire, Nathan Kurz, Christoph Rupp, [Stream VByte: Faster Byte-Oriented Integer Compression](https://arxiv.org/abs/1709.08990), Information Processing Letters 130, 2018.
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<!-- CONTACT -->
## Contact
### Author
- HSING-HAN, WU (Xyphuz)
- Mail me: xyphuzwu@gmail.com
- About me: <https://www.xyphuz.com>
- GitHub: <https://github.com/wst24365888>
### Project Link
- <https://github.com/wst24365888/libstreamvbyte>
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Raw data
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"maintainer": "HSING-HAN WU (Xyphuz)",
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"maintainer_email": "xyphuzwu@gmail.com",
"keywords": "streamvbyte,libstreamvbyte,vbyte,integer,compression,SIMD,vectorization,SSSE3,NEON,SSE2NEON,Python,C++,CMake,pybind11",
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"platform": null,
"description": "<div id=\"top\"></div>\n\n<!-- PROJECT SHIELDS -->\n\n[<div align=\"center\"> ![Contributors][contributors-shield]][contributors-url]\n[![Forks][forks-shield]][forks-url]\n[![Stargazers][stars-shield]][stars-url]\n[![MIT License][license-shield]][license-url]\n[![Issues][issues-shield]][issues-url]\n[![Issues Closed][issues-closed-shield]][issues-closed-url]\n[![Python Version][python-version-shield]][python-version-url]\n[![Week Download][download-shield]</div>][download-url]\n\n<br />\n\n<!-- PROJECT BANNER -->\n\n\n\n<br />\n<div align=\"center\">\n<p align=\"center\">\n <a href=\"https://github.com/wst24365888/libstreamvbyte#usage\"><strong>Explore Usage \u00bb</strong></a>\n <br />\n <br />\n <a href=\"https://github.com/wst24365888/libstreamvbyte/issues\">Report Bug</a>\n \u00b7\n <a href=\"https://github.com/wst24365888/libstreamvbyte/issues\">Request Feature</a>\n </p>\n</div>\n\n<!-- TABLE OF CONTENTS -->\n\n<details>\n <summary>Table of Contents</summary>\n <ol>\n <li>\n <a href=\"#about-the-project\">About The Project</a>\n </li>\n <li>\n <a href=\"#getting-started\">Getting Started</a>\n <ul>\n <li><a href=\"#installation\">Installation</a></li>\n <li><a href=\"#usage\">Usage</a></li>\n <li><a href=\"#example\">Example</a></li>\n </ul>\n </li>\n <li><a href=\"#benchmark\">Benchmark</a></li>\n <li><a href=\"#roadmap\">Roadmap</a></li>\n <li><a href=\"#contributing\">Contributing</a></li>\n <li><a href=\"#license\">License</a></li>\n <li><a href=\"#reference\">Reference</a></li>\n <li><a href=\"#contact\">Contact</a></li>\n </ol>\n</details>\n\n<!-- ABOUT THE PROJECT -->\n\n## About The Project\n\n`libstreamvbyte` is a `C++` implementation of [StreamVByte](#reference), with `Python` bindings using `pybind11`. \n\n[StreamVByte](#reference) is an integer compression technique that use SIMD instructions (vectorization) to improve performance. The library is optimized with `SSSE3` intrinsics, which are supported by most `x86_64` processors. It uses `sse2neon` to translate `SSSE3` intrinsics to `NEON` intrinsics for `ARM` processors. The library can also be used with other 32-bit architectures, although it will fall back to scalar implementations in those cases.\n\nWith `libstreamvbyte`, you can quickly and efficiently compress integer sequences, reducing the amount of storage space and network bandwidth required. The library is easy to use and integrates seamlessly with `Python` via `pybind11` bindings. Whether you're working with large datasets or building a distributed computing system, `libstreamvbyte` can help you improve performance and reduce the resources needed to handle your data.\n\nCurrently supports `Python 3.8+` on Windows, Linux (`manylinux_2_17`, `musllinux_1_1`) and macOS (`universal2`).\n\n<p align=\"right\">(<a href=\"#top\">back to top</a>)</p>\n\n<!-- GETTING STARTED -->\n\n## Getting Started\n\n### Installation\n\n#### For `Python`\n\nInstall from `PyPI` using `pip`.\n\n```bash\npip install libstreamvbyte\n```\n\nOr install from `.whl` file.\n\n```bash\npip install \"path/to/your/downloaded/whl\"\n```\n\nTo find appropriate `.whl` file, please visit [releases](https://github.com/wst24365888/libstreamvbyte/releases).\n\n#### For `C++`\n\nYou must have `CMake` installed on your system.\n\n```bash\n# clone the repo\ngit clone https://github.com/wst24365888/libstreamvbyte\ncd libstreamvbyte\n\n# build and install\ncmake .\nmake\nsudo make install\n```\n\n### Usage\n\n#### For `Python`\n\nImport `libstreamvbyte` first.\n\n```python\nimport libstreamvbyte as svb\n```\n\nAnd here are the APIs.\n\n```python\n# Encode an array of unsigned integers into a byte array.\nencode(in_uint32: numpy.ndarray[numpy.uint32]) -> numpy.ndarray[numpy.uint8]\n\n# Decode a byte array into an array of unsigned integers.\ndecode(in_uint8: numpy.ndarray[numpy.uint8], size: int) -> numpy.ndarray[numpy.uint32]\n\n# Encode an array of signed integers into an array of unsigned integers.\nzigzag_encode(in_int32: numpy.ndarray[numpy.int32]) -> numpy.ndarray[numpy.uint32]\n\n# Decode an array of unsigned integers into an array of signed integers.\nzigzag_decode(in_uint32: numpy.ndarray[numpy.uint32]) -> numpy.ndarray[numpy.int32]\n\n# Check if the current wheel is a vectorized version.\nis_vectorized_version() -> bool\n```\n\n#### For `C++`\n\nInclude `streamvbyte.h` first.\n\n```cpp\n#include \"streamvbyte.h\"\n```\n\nFor the APIs, please refer to [include/streamvbyte.h](https://github.com/wst24365888/libstreamvbyte/blob/main/include/streamvbyte.h).\n\n### Example\n\n#### For `Python`\n\n```python\nimport libstreamvbyte as svb\n\nN = 2**20 + 2\n\n# type(original_data) == np.ndarray\n# original_data.dtype == np.int32\noriginal_data = np.random.randint(-2**31, 2**31, N, dtype=np.int32)\n\n# type(compressed_bytes) == np.ndarray\n# compressed_bytes.dtype == np.uint8\ncompressed_bytes = svb.encode(svb.zigzag_encode(original_data))\n\n# type(recovered_data) == np.ndarray\n# recovered_data.dtype == np.int32\nrecovered_data = svb.zigzag_decode(svb.decode(compressed_bytes, N))\n```\n\n#### For `C++`\n\n```cpp\n#include \"streamvbyte.h\"\n\nint main() {\n std::size_t N = (1 << 20) + 2;\n\n std::vector<int32_t> original_data(N);\n for (std::size_t i = 0; i < N; ++i) {\n original_data[i] = rand() - rand();\n }\n\n std::vector<uint8_t> compressed_bytes = streamvbyte::encode(streamvbyte::zigzag_encode(original_data));\n std::vector<int32_t> recovered_data = streamvbyte::zigzag_decode(streamvbyte::decode(compressed_bytes, N));\n\n return 0;\n}\n```\n\nCompile it with linking to `libstreamvbyte`.\n\n```bash\ng++ -o example example.cpp -lstreamvbyte\n```\n\n<p align=\"right\">(<a href=\"#top\">back to top</a>)</p>\n\n<!-- BENCHMARK -->\n\n## Benchmark\n\n```bash\nOS: Linux 5.15.79.1-microsoft-standard-WSL2 x86_64\nCPU: AMD Ryzen 5 3600 6-Core Processor (12) @ 3.600GHz\n\nRun on (12 X 3593.26 MHz CPU s)\nCPU Caches:\n L1 Data 32 KiB (x6)\n L1 Instruction 32 KiB (x6)\n L2 Unified 512 KiB (x6)\n L3 Unified 16384 KiB (x1)\nLoad Average: 0.81, 0.85, 0.69\n-----------------------------------------------------------------------------------\nBenchmark Time CPU Iterations Throughput\n-----------------------------------------------------------------------------------\nBM_memcpy/4096 149 ns 149 ns 4688531 13.7122G/s\nBM_memcpy/8192 548 ns 548 ns 1275803 7.46783G/s\nBM_memcpy/16384 1139 ns 1138 ns 640835 7.19553G/s\nBM_memcpy/32768 2185 ns 2185 ns 320840 7.49932G/s\nBM_memcpy/65536 4921 ns 4921 ns 142703 6.65895G/s\nBM_memcpy/131072 10968 ns 10968 ns 63502 5.97511G/s\nBM_memcpy/262144 22465 ns 22465 ns 31134 5.83457G/s\nBM_memcpy/524288 45101 ns 45100 ns 15541 5.81245G/s\nBM_memcpy/1048576 91131 ns 91131 ns 7639 5.75314G/s\nBM_streamvbyte_encode/4096 1222 ns 1222 ns 580855 1.67556G/s\nBM_streamvbyte_encode/8192 2470 ns 2467 ns 282349 1.66064G/s\nBM_streamvbyte_encode/16384 4945 ns 4945 ns 139671 1.65662G/s\nBM_streamvbyte_encode/32768 9990 ns 9989 ns 70497 1.64017G/s\nBM_streamvbyte_encode/65536 19853 ns 19853 ns 30963 1.65051G/s\nBM_streamvbyte_encode/131072 39933 ns 39932 ns 17401 1.64118G/s\nBM_streamvbyte_encode/262144 80563 ns 80562 ns 8193 1.62697G/s\nBM_streamvbyte_encode/524288 160716 ns 160716 ns 4284 1.6311G/s\nBM_streamvbyte_encode/1048576 319253 ns 319253 ns 1942 1.64223G/s\nBM_streamvbyte_decode/4096 691 ns 691 ns 1040462 2.96191G/s\nBM_streamvbyte_decode/8192 1341 ns 1341 ns 516979 3.05539G/s\nBM_streamvbyte_decode/16384 2683 ns 2683 ns 261208 3.05359G/s\nBM_streamvbyte_decode/32768 5348 ns 5348 ns 130319 3.06353G/s\nBM_streamvbyte_decode/65536 10817 ns 10817 ns 64427 3.02936G/s\nBM_streamvbyte_decode/131072 23207 ns 23207 ns 31546 2.824G/s\nBM_streamvbyte_decode/262144 45746 ns 45746 ns 11291 2.86519G/s\nBM_streamvbyte_decode/524288 88660 ns 88660 ns 7947 2.95673G/s\nBM_streamvbyte_decode/1048576 178497 ns 178497 ns 3907 2.93724G/s\nBM_zigzag_encode/4096 810 ns 810 ns 854076 2.52829G/s\nBM_zigzag_encode/8192 1611 ns 1608 ns 433154 2.548G/s\nBM_zigzag_encode/16384 3174 ns 3174 ns 219165 2.58084G/s\nBM_zigzag_encode/32768 6457 ns 6457 ns 108415 2.53754G/s\nBM_zigzag_encode/65536 12582 ns 12582 ns 54747 2.60432G/s\nBM_zigzag_encode/131072 25243 ns 25243 ns 27802 2.59617G/s\nBM_zigzag_encode/262144 50278 ns 50278 ns 13952 2.60693G/s\nBM_zigzag_encode/524288 100563 ns 100562 ns 6932 2.60678G/s\nBM_zigzag_encode/1048576 211846 ns 211845 ns 3222 2.47487G/s\nBM_zigzag_decode/4096 675 ns 675 ns 1041044 3.03263G/s\nBM_zigzag_decode/8192 1342 ns 1342 ns 523553 3.05196G/s\nBM_zigzag_decode/16384 2643 ns 2643 ns 265497 3.09905G/s\nBM_zigzag_decode/32768 5383 ns 5383 ns 130976 3.04377G/s\nBM_zigzag_decode/65536 11474 ns 11474 ns 60817 2.85588G/s\nBM_zigzag_decode/131072 21777 ns 21777 ns 32345 3.00944G/s\nBM_zigzag_decode/262144 43477 ns 43478 ns 14387 3.0147G/s\nBM_zigzag_decode/524288 86120 ns 86120 ns 8145 3.04393G/s\nBM_zigzag_decode/1048576 173095 ns 173093 ns 4028 3.02894G/s\n```\n\n> The unit of `Throughput` is `GB/s` (Giga Bytes per second).\n\n### Build Benchmarks from Source\n\n```bash\ncmake . \\\n -DCMAKE_BUILD_TYPE=Release \\\n -DBUILD_SHARED_LIBS=OFF \\\n -DBUILD_PYBIND11=OFF \\\n -DPRINT_BENCHMARK=OFF \\\n -DBUILD_TESTS=ON \\\n -DBUILD_BENCHMARKS=ON\nmake libstreamvbyte_benchmarks\n./libstreamvbyte_benchmarks --benchmark_counters_tabular=true\n```\n\n<p align=\"right\">(<a href=\"#top\">back to top</a>)</p>\n\n<!-- ROADMAP -->\n\n## Roadmap\n\n- [x] Zigzag encoding/decoding.\n- [x] Support ARM processors with `NEON` intrinsics.\n- [ ] Differential coding (delta encoding/decoding).\n\nSee the [open issues](https://github.com/wst24365888/libstreamvbyte/issues)\nfor a full list of proposed features (and known issues).\n\n<p align=\"right\">(<a href=\"#top\">back to top</a>)</p>\n\n<!-- CONTRIBUTING -->\n\n## Contributing\n\nContributions are what make the open source community such an amazing place to\nlearn, inspire, and create. Any contributions you make are **greatly\nappreciated**.\n\nIf you have a suggestion that would make this better, please fork the repo and\ncreate a pull request. You can also simply open an issue with the tag\n\"enhancement\". Don't forget to give the project a star! Thanks again!\n\n1. Fork the Project\n2. Create your Feature Branch (`git checkout -b feat/amazing-feature`)\n3. Commit your Changes with\n [Conventional Commits](https://www.conventionalcommits.org/en/v1.0.0/)\n4. Push to the Branch (`git push origin feat/amazing-feature`)\n5. Open a Pull Request\n\n<p align=\"right\">(<a href=\"#top\">back to top</a>)</p>\n\n<!-- LICENSE -->\n\n## License\n\nDistributed under the MIT License. See\n[LICENSE](https://github.com/wst24365888/libstreamvbyte/blob/main/LICENSE)\nfor more information.\n\n<p align=\"right\">(<a href=\"#top\">back to top</a>)</p>\n\n<!-- REFERENCE -->\n\n## Reference\n\n- Daniel Lemire, Nathan Kurz, Christoph Rupp, [Stream VByte: Faster Byte-Oriented Integer Compression](https://arxiv.org/abs/1709.08990), Information Processing Letters 130, 2018.\n\n<p align=\"right\">(<a href=\"#top\">back to top</a>)</p>\n\n<!-- CONTACT -->\n\n## Contact\n\n### Author\n\n- HSING-HAN, WU (Xyphuz)\n - Mail me: xyphuzwu@gmail.com\n - About me: <https://www.xyphuz.com>\n - GitHub: <https://github.com/wst24365888>\n\n### Project Link\n\n- <https://github.com/wst24365888/libstreamvbyte>\n\n<p align=\"right\">(<a href=\"#top\">back to top</a>)</p>\n\n[contributors-shield]: https://img.shields.io/github/contributors/wst24365888/libstreamvbyte.svg?style=for-the-badge\n[contributors-url]: https://github.com/wst24365888/libstreamvbyte/graphs/contributors\n[forks-shield]: https://img.shields.io/github/forks/wst24365888/libstreamvbyte.svg?style=for-the-badge\n[forks-url]: https://github.com/wst24365888/libstreamvbyte/network/members\n[stars-shield]: https://img.shields.io/github/stars/wst24365888/libstreamvbyte.svg?style=for-the-badge\n[stars-url]: https://github.com/wst24365888/libstreamvbyte/stargazers\n[issues-shield]: https://img.shields.io/github/issues/wst24365888/libstreamvbyte.svg?style=for-the-badge\n[issues-url]: https://github.com/wst24365888/libstreamvbyte/issues\n[issues-closed-shield]: https://img.shields.io/github/issues-closed/wst24365888/libstreamvbyte.svg?style=for-the-badge\n[issues-closed-url]: https://github.com/wst24365888/libstreamvbyte/issues?q=is%3Aissue+is%3Aclosed\n[license-shield]: https://img.shields.io/github/license/wst24365888/libstreamvbyte.svg?style=for-the-badge\n[license-url]: https://github.com/wst24365888/libstreamvbyte/blob/main/LICENSE\n[python-version-shield]: https://img.shields.io/pypi/pyversions/libstreamvbyte?color=A000A0&style=for-the-badge\n[python-version-url]: https://pypi.org/project/libstreamvbyte/\n[download-shield]: https://img.shields.io/pypi/dw/libstreamvbyte?color=A000A0&style=for-the-badge\n[download-url]: https://pypistats.org/packages/libstreamvbyte\n",
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