Accelerating multi-way joins on the GPU

Graphic processing units (GPUs) have been employed as hardware accelerators for online analytics. However, multi-way joins, which are common in analytic workloads, are inefficient on GPUs. Therefore, we propose to accelerate two representative multi-way join algorithms on the GPU: a multi-way hash join (MHJ) and the worst-case optimal Leapfrog Triejoin (LFTJ). Specifically, we design a warp-based parallelization strategy to reduce thread divergence and to facilitate coalesced memory access in parallel searches in a table. We further enhance our implementations with a set of GPU-friendly optimizations, including dynamic workload sharing among threads and elimination of the result counting phase. Additionally, we enable out-of-core multi-way joins with software pipelining. Our experiments show that our optimized MHJ and LFTJ outperform the state-of-the-art GPU algorithms by a factor of up to 67 on an NVIDIA V100 GPU.