research-article

High-Throughput Subset Matching on Commodity GPU-Based Systems

Authors:

Daniele Rogora,

Michele Papalini,

Koorosh Khazaei,

Alessandro Margara,

Antonio Carzaniga,

Gianpaolo CugolaAuthors Info & Claims

EuroSys '17: Proceedings of the Twelfth European Conference on Computer Systems

Pages 513 - 526

https://doi.org/10.1145/3064176.3064190

Published: 23 April 2017 Publication History

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.

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Cited By

Fernandez Declara PCampora Perez DGarcia-Blas JVom Bruch DDaniel Garcia JNeufeld N(2019)A Parallel-Computing Algorithm for High-Energy Physics Particle Tracking and Decoding Using GPU ArchitecturesIEEE Access10.1109/ACCESS.2019.29272617(91612-91626)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2927261

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cover image ACM Conferences

EuroSys '17: Proceedings of the Twelfth European Conference on Computer Systems

April 2017

648 pages

ISBN:9781450349383

DOI:10.1145/3064176

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 23 April 2017

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EuroSys '17

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EuroSys '17: Twelfth EuroSys Conference 2017

April 23 - 26, 2017

Belgrade, Serbia

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Overall Acceptance Rate 241 of 1,308 submissions, 18%

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Cited By

Fernandez Declara PCampora Perez DGarcia-Blas JVom Bruch DDaniel Garcia JNeufeld N(2019)A Parallel-Computing Algorithm for High-Energy Physics Particle Tracking and Decoding Using GPU ArchitecturesIEEE Access10.1109/ACCESS.2019.29272617(91612-91626)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2927261

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