HyperLogLog Sketch Acceleration on FPGA
Amit B. Kulkarni, Monica Chiosa, Thomas B. Preußer, Kaan Kara, David Sidler, Gustavo Alonso · 2020
Data sketches are a set of widely used approximated data summarizing techniques. Their fundamental property is sub-linear memory complexity on the input cardinality, an important aspect when processing streams or data sets with a vast base domain such as URLs, IP addresses, user IDs, etc. Among the many data sketches available, HyperLogLog has become the reference for cardinality counting i.e., how many distinct data items there are in a data set. Although it does not count every data item but provides probabilistic guarantees on the result thereby reducing its memory footprint, and the result is often used to analyze data streams. In this paper, we explore how to implement HyperLogLog on an FPGA to benefit from the parallelism available and the ability to process data streams coming from high-speed networks. Our multi-pipelined high-cardinality HyperLogLog implementation delivers 1.8x higher throughput than the best-optimized multi-thread HyperLogLog running on a dual-socket Intel Xeon E5-2630 v3 system with a total of 16 cores and 32 hyper-threads.