MG-Join: A Scalable Join for Massively Parallel Multi-GPU Architectures

The recent scale-up of GPU hardware through the integration of multiple GPUs into a single machine and the introduction of higher bandwidth interconnects like NVLink 2.0 has enabled new opportunities of relational query processing on multiple GPUs. However, due to the unique characteristics of GPUs and the interconnects, existing hash join implementations spend up to 66% of their execution time moving the data between the GPUs and achieve lower than 50% utilization of the newer high bandwidth interconnects. This leads to extremely poor scalablity of hash join performance on multiple GPUs, which can be slower than the performance on a single GPU. In this paper, we propose MG-Join, a scalable partitioned hash join implementation on multiple GPUs of a single machine. In order to effectively improve the bandwidth utilization, we develop a novel multi-hop routing for cross-GPU communication that adaptively chooses the efficient route for each data flow to minimize congestion. Our experiments on the DGX-1 machine show that MG-Join helps significantly reduce the communication overhead and achieves up to 97% utilization of the bisection bandwidth of the interconnects, resulting in significantly better scalability. Overall, MG-Join outperforms the state-of-the-art hash join implementations by up to 2.5x. MG-Join further helps improve the overall performance of TPC-H queries by up to 4.5x over multi-GPU version of an open-source commercial GPU database Omnisci.