Skanda

Skanda is an LZ+Huffman based compression algorithm, kinda like an spiritual successor to Lizard. Unlike other open-source compressors, which always have a constant decompression speed, Skanda can be fine tuned at runtime to prefer better ratios or a higher decoding throughput, while always maintaining encoding and decoding performance that matches or exceeds the competition. The only other compressor I know that is capable of this is Oodle by RAD Game Tools, but that is closed source.

How to use

Simply add the file Skanda.h to your project and create a .cpp file with the following:

#define SKANDA_IMPLEMENTATION
#include "Skanda.h"

Then simply add the header file anywhere you need.

The API is very simple and straightforward. To compress you might do something like this:

uint8_t* outputBuf = new uint8_t[skanda::compress_bound(inputSize)];
size_t compressedSize = skanda::compress(inputBuf, inputSize, outputBuf);
if (skanda::is_error(compressedSize))
  std::cout << "Error while compressing data";

And to decompress:

size_t err = skanda::decompress(compressedBuf, compressedSize, decompressedBuf, uncompressedSize);
if (skanda::is_error(err))
  std::cout << "Error while decompressing data";

During decoding it is possible to use 2 threads on a single compressed file. You can directly use the ThreadCallback class, which spawns a new thread, or create a custom child class from it that uses a thread pool. For example:

dp::thread_pool threadPool(16);

class MyThreadCallback : public skanda::ThreadCallback {
public:
    std::future<size_t> enqueue(size_t(*func)(void*), void* arg) {
        return threadPool.enqueue(func, arg);
    }
};

int main() {
  //...
  MyThreadCallback threadCallback();
  size_t err = skanda::decompress(compressedBuf, compressedSize, decompressedBuf, uncompressedSize, &threadCallback);
  //...
}

This works by decoding the file like a CPU pipeline. Note that it only provides a boost for relatively large files (512+ KiB) and that were compressed with a decSpeedBias lower than 1 (the lower the bias the bigger the boost).

If you want to keep track of the progress, you can create a child class from ProgressCallbacks, and then pass a pointer of the object to the functions:

class MyProgressCallback : public skanda::ProgressCallback {
  size_t fileSize;
  
public:
  MyProgressCallback(size_t _fileSize) {
    fileSize = _fileSize;
  }
  bool progress(size_t bytes) {
    std::cout << "Current progress: " << bytes << "/" << fileSize << "\n";
    return false;
  }
}

int main() {
  //...
  MyProgressCallback progressCallback(inputSize);
  size_t compressedSize = skanda::compress(input, inputSize, output, level, speedBias, &progressCallback);
  //...
}

Benchmarks

The algorithm was benchmarked on Windows 11, on a Ryzen 6900HX@3.3GHz and compiled with Visual Studio 2022. The file used was produced by tarring the Silesia corpus.