Hardware-Conscious Sliding Window Aggregation on GPUs

Stream Processing Engines (SPEs) have recently begun utilizing heterogeneous coprocessors (e.g., GPUs) to meet the velocity requirements of modern real-time applications. The massive parallelism and high memory bandwidth of GPUs can significantly increase processing throughput in data-intensive streaming scenarios, such as windowed aggregations. However, previous research only focused on the overall architecture of hybrid CPU-GPU streaming systems and the need for efficient in-GPU window operators was overshadowed by the limited interconnect bandwidth. With aggregation taking up a significant portion of streaming workloads, in this work, we analyze and optimize the performance of sliding window aggregates over GPUs. Current implementations under-utilize the hardware, and for a range of query parameters they cannot even saturate the bandwidth of the interconnect. To optimize execution, we first evaluate the fundamental building blocks of streaming aggregation for GPUs and identify the performance bottlenecks. Then, we build Slider: an adaptive algorithm that selects the most appropriate primitives and kernel configurations based on the query parameters. Our evaluation shows that Slider outperforms previous approaches by 3×-1250×, and saturates both the interconnect and the memory bandwidth for a wide range of examined input workloads.