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GSST

Parallel string decompression at 191 GB/s on GPU

Journal Article (2025)
Author(s)

Robin Vonk (Student TU Delft)

Joost Hoozemans (Voltron Data)

Z. Al-Ars (TU Delft - Computer Engineering)

Research Group
Computer Engineering
DOI related publication
https://doi.org/10.1145/3759441.3759450
More Info
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Publication Year
2025
Language
English
Research Group
Computer Engineering
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository as part of the Taverne amendment. More information about this copyright law amendment can be found at https://www.openaccess.nl. Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. @en
Issue number
1
Volume number
59
Pages (from-to)
55-61
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Abstract

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.

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