OpenCL implementation of a cascaded decompression algorithm for querying of big table data

Luyang Sun; Xiawei Yue; Zheng Zhang; Bo Zhang

doi:10.1117/12.2674741

2 May 2023 OpenCL implementation of a cascaded decompression algorithm for querying of big table data

Luyang Sun, Xiawei Yue, Zheng Zhang, Bo Zhang

Proceedings Volume 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023); 126421C (2023) https://doi.org/10.1117/12.2674741
Event: Second International Conference on Electronic Information Engineering, Big Data and Computer Technology (EIBDCT 2023), 2023, Xishuangbanna, China

Abstract

Some applications require fast queries on big tables whose rows are as large as one billion. If we store table data without any compression on disks, the operation of loading the table data into host memory itself costs quite a long time. To speed up queries, we resort to data compression. Table data is first compressed before being saved to disks. In the query stage, compressed data is loaded into host memory, decompressed and then accessed. To speed up decompressing, we make use of the massively parallel capability of GPU devices. In order to make full use of the GPU computing resources, GPU kernels should avoid divergent execution as much as possible, and should make efficient use of the GPU local memory. Guided by these criteria, we have designed a GPU decompression algorithm. Its basic idea is to decompose the decompression task into a few sequential basic operations, and then accomplish each basic operation parallel by using GPU threads. Experiments showed that the average throughput rate of the decompression algorithm implemented with OpenCL could reach to 13.12 GB/s when using AMD RX 6600. The reduced loading time and decompression time significantly improved the query speed in the query stage.

Citation Download Citation

Luyang Sun, Xiawei Yue, Zheng Zhang, and Bo Zhang "OpenCL implementation of a cascaded decompression algorithm for querying of big table data", Proc. SPIE 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023), 126421C (2 May 2023); https://doi.org/10.1117/12.2674741

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