Parquet is the de facto columnar file format in modern analytical systems, yet its configuration guidelines have largely been shaped by CPU-centric execution models. As GPU-accelerated data processing becomes increasingly prevalent, Parquet files generated with CPU-oriented defaults can severely underutilize GPU parallelism, turning GPU scans into a performance bottleneck. In this work, we systematically study how Parquet configurations affect GPU scan performance. We show that Parquet's poor GPU performance is not inherent to the format itself but rather a consequence of suboptimal configuration choices. By applying GPU-aware configurations, we increase effective read bandwidth up to 125 GB/s without modifying the Parquet specification.

翻译：Parquet是现代分析系统中事实上的列式文件格式，但其配置指南主要受CPU中心执行模型的影响。随着GPU加速数据处理日益普及，采用CPU导向默认设置生成的Parquet文件会严重未充分利用GPU并行性，导致GPU扫描成为性能瓶颈。本研究系统性地探讨了Parquet配置如何影响GPU扫描性能。我们证明Parquet在GPU上的低效表现并非格式本身固有缺陷，而是次优配置选择的结果。通过应用GPU感知配置，我们在不修改Parquet规范的前提下，将有效读取带宽提升至125 GB/s。