HyperLogLog Sketch Acceleration on FPGA

TL;DR

This paper presents an FPGA implementation of HyperLogLog that significantly accelerates cardinality estimation in high-speed data streams, achieving 1.8 times higher throughput than a high-end CPU system.

Contribution

The paper introduces a parallel FPGA-based HyperLogLog implementation that outperforms traditional CPU-based solutions in processing high-speed data streams.

Findings

Achieved 1.8x higher throughput than CPU implementation

Demonstrated effective FPGA parallelism for data sketching

Enhanced real-time data stream analysis capabilities

Abstract

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 (URLs, IP addresses, user IDs, etc.). Among the many data sketches available, HyperLogLog has become the reference for cardinality counting (how many distinct data items there are in a data set). Although it does not count every data item (to reduce memory consumption), it provides probabilistic guarantees on the result, and it is, thus, 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 an optimized HyperLogLog running on a dual-socket Intel Xeon E5-2630 v3 system with a total of 16 cores and 32 hyper-threads.