High-Throughput Subset Matching on Commodity GPU-Based Systems

Abstract

Large-scale information processing often relies on subset matching for data classification and routing. Examples are publish/subscribe and stream processing systems, database systems, social media, and information-centric networking. For instance, an advanced Twitter-like messaging service where users might follow specific publishers as well as specific topics encoded as tag sets must join a stream of published messages with the users and their preferred tag sets so that the user tag set is a subset of the message tags. Subset matching is an old but also notoriously difficult problem. We present TagMatch, a system that solves this problem by taking advantage of a hybrid CPU/GPU stream processing architecture. TagMatch targets large-scale applications with thousands of matching operations per seconds against hundreds of millions of tag sets. We evaluate TagMatch on an advanced message streaming application, with very positive results both in absolute terms and in comparison with existing systems. As a notable example, our experiments demonstrate that TagMatch running on a single, commodity machine with two GPUs can easily sustain the traffic throughput of Twitter even augmented with expressive tag-based selection.