LZSS GPU is a Lempel-Ziv-Storer-Szymansk compression algorithm optimized for GPUs.

How to Cite LZSS

Stein, C.; Griebler, D.; Danelutto, M.; Fernandes, L. G. Stream Parallelism on the LZSS Data Compression Application for Multi-Cores with GPUs. 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), pp. 247-251, IEEE, Pavia, Italy, 2019. [PDF]

@inproceedings{larcc:stream_parallelism_lzss_gpu:PDP:19,
    author={Charles Michael Stein and Dalvan Griebler and Marco Danelutto and Luiz Gustavo Fernandes},
    title={{Stream Parallelism on the LZSS Data Compression Application for Multi-Cores with GPUs}},
    booktitle={27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)},
    pages={247-251},
    publisher={IEEE},
    address={Pavia, Italy},
    month={February},
    year={2019},
    doi={10.1109/EMPDP.2019.8671624},
    url={https://ieeexplore.ieee.org/document/8671624},
}

lzss-gpu-stream-parallelism

This repository has the parallel and sequential implementations for the Lempel-Ziv-Storer-Szymanski (LZSS) data compression applications. We introduced stream parallelism for CPU (using SPar) and GPU (using CUDA and OpenCL).

Compiling

To compile the project, you need to have the following prerequisites installed.

• CUDA
• SPar in your home (https://github.com/dalvangriebler/SPar)

Once you have all the prerequisites installed, run the following command inside src folder:

make

This will generate the app runnable file. PS: You wont be able to run it on on Windows, because SPar at the time we are publishing it can only be ran on linux.

Using Lzss on GPU

Compression

    ./src/app -c -i <input_file_name> -o <output_file_name>

By default, this command will use sequential version of compression, you can change the mode to use GPU by passing the -p argument

    ./src/app -c -i <input_file_name> -o <output_file_name> -p <cuda|opencl|cpu_original|cpu_sequential>

To use multiple GPU, pass in the argument -g, indicating the GPU ids separated by comma

    ./src/app -c -i <input_file_name> -o <output_file_name> -p cuda -g 0,1

For benchmarking purposes, you can pass the -m argument to run the test in memory

Decompression

The decompression is only available on a serial implementation, thus you only need to provide the input file path and the output path by using this command:

    ./src/app -d -i <input_file_name> -o <output_file_name>

Help

    ./src/app --help

Downloading datasets

The datasets used in our work can be downloaded by executing this command:

cd data
./download_dataset.sh