Multi-dimensional Range Joins on HBM-enabled FPGAs

Range join is an essential analysis in various big data applications such as genomic variant annotations, spatiotemporal databases and EDA design rule checking, often requiring searches on billion-scale records. However, its performance on multi-threaded CPUs/GPUs have been bottlenecked by both memory-access bandwidth and instruction/data dependencies. Furthermore, the limited intra-task parallelism in state-of-the-art renders SIMD platforms severely under-utilized. In this work, we present an efficient hardware-software co-design for multi-dimensional range joins on clusters of HBM-enabled FPGAs. Our highly-scalable processing system achieves up-to 2.6x/6.9x/7.1x speedup/energy improvements/memory access reductions compared to state-of-the-art CPU solution for one-dimensional range joins, while being easily extensible to larger data dimensionality.