Fast Equi-Join Algorithms on GPUs

Abstract

Processing relational joins on modern GPUs has attracted much attention in the past few years. With the rapid development on the hardware and software environment in the GPU world, the existing GPU join algorithms designed for earlier architecture cannot make the most out of latest GPU products. In this paper, we report new design and implementation of join algorithms with high performance under today's GPGPU environment. This is a key component of our scientific database engine named G-SDMS. In particular, we overhaul the popular radix hash join and redesign sort-merge join algorithms on GPUs by applying a series of novel techniques to utilize the hardware capacity of latest Nvidia GPU architecture and new features of the CUDA programming framework. Our algorithms take advantage of revised hardware arrangement, larger register file and shared memory, native atomic operation, dynamic parallelism, and CUDA Streams. Experiments show that our new hash join algorithm is 2.0 to 14.6 times as efficient as existing GPU implementation, while the new sort-merge join achieves a speedup of 4.0X to 4.9X. Compared to the best CPU sort-merge join and hash join known to date, our optimized code achieves up to 10.5X and 5.5X speedup. Moreover, we extend our design to scenarios where large data tables cannot fit in the GPU memory.