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 Tag- Match 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.
|Titolo:||High-throughput subset matching on commodity GPU-based systems|
|Data di pubblicazione:||2017|
|Appare nelle tipologie:||04.1 Contributo in Atti di convegno|