One major technical challenge for modern analytical database systems is how to leverage GPU to exploit their massive parallelism and high bandwidth. Yet, existing GPU-driven database engines suffer from inefficiencies caused by frequent host-device interactions and fragmented execution across multiple GPU kernels, limiting their ability to fully utilize GPU's computational and IO capabilities. This paper proposes Data Path Fusion (DPF), a novel GPU-driven data processing architecture that integrates a sequence of data path operations -- including IOs, decompression, and query operations -- into a single GPU kernel. By fusing the data path, DPF reduces host-device communication overheads and enables more efficient utilization of GPU resources for analytical query workloads. DPF seamlessly integrates GPU-friendly optimization techniques, including type-specific compression/decompression, variable-length attribute support, and state-of-the-art GPU-driven IO mechanism, to work in concert, enabling efficient end-to-end query execution directly on GPU. Through extensive experimental evaluation using a prototyped DPF-based GPU-driven database engine (DPFProto) with analytical benchmark workloads, this paper demonstrates that DPF achieves speedups of 2.66 to 6.22 on TPC-H and 3.84 to 16.81 on SSB over the state-of-the-art approach in the representative configuration. Our results show that DPF effectively unlocks the computational and IO potential of modern GPU, providing a promising direction for next-generation analytical database systems.

翻译：现代分析数据库系统面临的主要技术挑战之一是如何利用GPU发挥其大规模并行性和高带宽优势。然而，现有基于GPU的数据库引擎因频繁的主机-设备交互及跨多个GPU内核的碎片化执行而效率低下，限制了其充分利用GPU计算和IO能力。本文提出数据路径融合（DPF）——一种新型基于GPU的数据处理架构，它将包括IO、解压缩和查询操作在内的一系列数据路径操作整合到单个GPU内核中。通过融合数据路径，DPF降低了主机-设备通信开销，并使得GPU资源在分析查询工作负载下得到更高效利用。DPF无缝集成了适用于GPU的优化技术，包括类型特定压缩/解压缩、可变长度属性支持以及最先进的基于GPU的IO机制，使其协同工作，直接在GPU上实现高效的端到端查询执行。通过使用基于DPF的原型GPU数据库引擎（DPFProto）并采用分析基准工作负载进行广泛实验评估，本文证明：在典型配置下，DPF在TPC-H上实现了2.66到6.22倍的加速比，在SSB上实现了3.84到16.81倍的加速比，性能优于现有最先进方法。实验结果表明，DPF有效释放了现代GPU的计算和IO潜力，为下一代分析数据库系统提供了有前景的发展方向。