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Enabling Tile-Based Direct Query on Adaptively Compressed Data With GPU Acceleration
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DOI：10.1109/TPDS.2025.3639485.png)
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Abstract
En 中文
The explosive growth of data poses significant challenges for GPU-based databases, which must balance limited memory capacity with the need for high-speed query execution. Compression has become an essential technique for optimizing memory utilization and reducing data movement. However, its benefits have been limited to the necessary data decompression. Querying compressed data conventionally requires decompression, which causes the query process to be significantly slower than a direct query on uncompressed data. To address this problem, this article presents a novel GPU-accelerated tile-based direct query framework that successfully eliminates the limitation, significantly enhancing query performance. By employing direct query strategies, the framework minimizes data movement and maximizes memory bandwidth utilization. It incorporates tile-based hardware-conscious execution strategies for direct query, including memory management and control flow coordination, to improve execution efficiency. Additionally, adaptive data-driven compression formats are paired with tailored SQL operators to enable efficient support for diverse queries. Our experiments, conducted using the Star Schema Benchmark, show an average improvement of 3.5× compared to the state-of-the-art tile-based decompression scheme, while maintaining the space-saving advantages of compression. Notably, our solution consistently outperforms existing direct execution schemes for compressed data across all query types.
Keywords:
Data compaction and compression
parallel databases
query processing
memory control and access
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