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.

High-throughput subset matching on commodity GPU-based systems

ROGORA, DANIELE;MARGARA, ALESSANDRO;CUGOLA, GIANPAOLO
2017

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 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.
Proceedings of the 12th European Conference on Computer Systems, EuroSys 2017
9781450349383
GPU-based processing; Message selection and dissemination; Subset matching; Computer Networks and Communications; Software; Information Systems; Hardware and Architecture; Control and Systems Engineering
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1028888
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