Most of the commonly used compression standards make use of some form of the LZ algorithm. Decompressing this type of data is not a good match for the Single-Instruction, Multiple Thread (SIMT) model of computation used by GPUs, resulting in low throughput and poor utilization of the GPU parallel compute capabilities. In this paper, we introduce GSST, a GPU-optimized version of the FSST compression algorithm, which targets string compression. The optimizations proposed in this paper make the algorithm particularly suitable for GPUs, which allows it to achieve a significantly better tradeoff for decompression throughput vs compression ratio as compared to the state of the art. Our results show that the new algorithm pushes the Pareto curve closer towards the ideal region, completely dominating LZ-based compressors in the nvCOMP library (LZ4, Snappy, GDeflate). GSST provides a compression ratio of 2.7 4x and achieves a throughput of 191 GB/s on an A100 GPu.

Most of the commonly used compression standards make use of some form of the LZ algorithm. Decompressing this type of data is not a good match for the Single-Instruction, Multiple Thread (SIMT) model of computation used by GPUs, resulting in low throughput and poor utilization of the GPU parallel compute capabilities. In this paper, we introduce GSST, a GPU-optimized version of the FSST compression algorithm, which targets string compression. The optimizations proposed in this paper make the algorithm particularly suitable for GPUs, which allows it to achieve a significantly better tradeoff for decompression throughput vs compression ratio as compared to the state of the art. Our results show that the new algorithm pushes the Pareto curve closer towards the ideal region, completely dominating LZ-based compressors in the nvCOMP library (LZ4, Snappy, GDeflate). GSST provides a compression ratio of 2.74x and achieves a throughput of 191 GB/s on an A100 GPU.