Tile-based Lightweight Integer Compression in GPU

Anil Shanbhag, Bobbi W. Yogatama, Xiangyao Yu, Samuel Madden
Massachusetts Institute of Technology
Proceedings of the 2022 International Conference on Management of Data (SIGMOD ’22), 2022


   title={Tile-based lightweight integer compression in GPU},

   author={Shanbhag, Anil and Yogatama, Bobbi W and Yu, Xiangyao and Madden, Samuel},

   booktitle={Proceedings of the 2022 International Conference on Management of Data},




GPUs are increasingly used for high-performance and interactive data analytics workloads due to their capability to accelerate computation using massive parallelism. A key constraint of GPU-based data analytics today is the limited memory capacity in GPU devices. Data compression is a powerful technique that can mitigate the capacity limitation in two ways: (1) fitting more data into GPU memory and (2) speeding up data transfer between CPU and GPU. However, compression schemes for GPU today are still limited in compression ratio and/or decompression speed. We identify two limiting factors of existing approaches. First, existing decompression solutions require multiple passes of scanning the global memory to decode layers of compression schemes, incurring significant memory traffic and hurting performance. We present the tile-based decompression model to decompress encoded data in a single pass over global memory and inline with query execution. Second, we develop an e"cient implementation of bit-packing-based compression schemes and their optimization techniques in the context of GPU. Our evaluation shows that our schemes can achieve similar compression rates to the best state-of-the-art compression schemes in GPU (i.e., nvCOMP) while being 2.2x and 2.6x faster in decompression speed and query running time.
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