It has so far been unclear which data-intensive CPU tasks can be accelerated with GPUs, as GPUs are bottlenecked by the slow bus connection to the CPU and the limited size of GPU memories.

In this paper we demonstrate a database workload where co-processing actually helps: accelerating large join pipelines where the join condition is selective, by pushing down a Bloom filter test for early pruning. GPUs are more powerful than CPUs for computing hash functions needed in Bloom filter tests, have a local memory with significantly more random-access bandwidth than the CPU, and since only keys (or extracts thereof) have to be moved to the GPU, data transfers over the bus are relatively small. Our micro-benchmarks show that raw Bloom filter lookups are up to 6x faster on the GPU than on the CPU in case the Bloom filter is larger than the CPU cache.

The next quest is for a database architecture that allows efficient CPU-GPU co-processing. We present a new heterogeneous query processing framework based on fluid co-processing. In fluid co-processing, tasks of different sizes -- that fit the device -- are dynamically co-processed. Early results show that fluid co-processing consistently improves end-to-end CPU performance of early pruning in join queries thanks to the GPU, by factors up to 2-3x.
DaMoN 19: The 15th International Workshop on Data Management on New Hardware
Database Architectures

Gubner, T., Gomes Tomé, D., Lang, H., & Boncz, P. (2019). Fluid co-processing: GPU Bloom-filters for CPU joins. In Proceedings of the 15th International Workshop on Data Management on New Hardware (pp. 9:1–9:10). doi:10.1145/3329785.3329934