In GPU-accelerated data analytics, the overhead of data transfer from CPU to GPU becomes a performance bottleneck when the data scales beyond GPU memory capacity due to the limited PCIe bandwidth. Data compression has come to rescue for reducing the amount of data transfer while taking advantage of the powerful GPU computation for decompression. To optimize the end-to-end query performance, however, the workflow of data compression, transfer, and decompression must be holistically designed based on the compression strategies and hardware characteristics to balance the I/O latency and computational overhead. In this work, we present ZipFlow, a compiler-based framework for optimizing compressed data transfer in GPU-accelerated data analytics. ZipFlow classifies compression algorithms into three distinct patterns based on their inherent parallelism. For each pattern, ZipFlow employs generalized scheduling strategies to effectively exploit the computational power of GPUs across diverse architectures. Building on these patterns, ZipFlow delivers flexible, high-performance, and holistic optimization, which substantially advances end-to-end data transfer capabilities. We evaluate the effectiveness of ZipFlow on industry-standard benchmark, TPC-H. Overall, ZipFlow achieves an average improvement of 2.08 times over the state-of-the-art GPU compression library (nvCOMP) and 3.14 times speedup against CPU-based query processing engines (e.g., DuckDB).

翻译：在GPU加速的数据分析中，当数据规模超过GPU内存容量时，由于PCIe带宽有限，从CPU到GPU的数据传输开销成为性能瓶颈。数据压缩技术应运而生，它通过减少数据传输量，同时利用强大的GPU计算能力进行解压来缓解这一问题。然而，为了优化端到端的查询性能，数据压缩、传输和解压的工作流程必须基于压缩策略和硬件特性进行整体设计，以平衡I/O延迟和计算开销。本文提出ZipFlow，一个基于编译器的框架，用于优化GPU加速数据分析中的压缩数据传输。ZipFlow根据压缩算法固有的并行性，将其分为三种不同的模式。针对每种模式，ZipFlow采用通用的调度策略，以有效利用不同架构下GPU的计算能力。基于这些模式，ZipFlow提供了灵活、高性能且整体化的优化方案，显著提升了端到端的数据传输能力。我们在行业标准基准测试TPC-H上评估了ZipFlow的有效性。总体而言，相比最先进的GPU压缩库（nvCOMP），ZipFlow平均实现了2.08倍的性能提升；与基于CPU的查询处理引擎（例如DuckDB）相比，则实现了3.14倍的加速。