High Speed Data Compression Using NVIDIA GPUs

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The nvCOMP library provides fast lossless data compression and decompression using a GPU. It features generic compression interfaces to enable developers to use high-performance GPU compressors in their applications.

Binaries are available under the standard NVIDIA Software License Agreement

nvCOMP Key Features

  • Compression Algorithms
    • Cascaded: Novel high-throughput compressor ideal for analytical or structured/tabular data.
    • LZ4: General-purpose no-entropy byte-level compressor well-suited for a wide range of datasets.
    • Snappy: Similar to LZ4, this byte-level compressor is a popular existing format used for tabular data.
    • GDeflate: Proprietary compressor with entropy encoding and LZ77, high compression ratios on arbitrary data.
    • Deflate: Huffman + LZ77, Provided for compatibility with existing Deflate-compressed datasets.
    • Bitcomp: Proprietary compressor designed for efficient GPU compression in Scientific Computing applications.
    • ANS: Proprietary entropy encoder based on asymmetric numeral systems (ANS).
    • zStandard: Huffman + LZ77 + ANS, popular compression format developed by Meta.
  • Flexible APIs
    • High-level is a good starting point for new users — it’s easy to use and fast. Metadata, chunking and the required scratch space are managed internally by nvCOMP. Using the high-level interface metadata, you can decompress a buffer without knowing how it was compressed. This enables the easiest way to ramp up and use nvCOMP in applications.
    • Low-level is targeting advanced users — metadata and chunking must be managed by the user outside of nvCOMP. Low-level nvCOMP APIs also perform batch compression/decompression of multiple streams. This provides the most flexibility for users with existing compression/decompression pipelines in their application.

nvCOMP Performance

The table below shows GPU compression throughput results in GB/s for a wide range of Ampere architecture GPU products, datasets and compression methods.


For additional information, please see our technical blogs and presentations:

Also feel free to gather your own results by running the benchmarks from the nvCOMP GitHub page.