research-article

DZiG: sparsity-aware incremental processing of streaming graphs

Authors:

Mugilan Mariappan,

Keval VoraAuthors Info & Claims

EuroSys '21: Proceedings of the Sixteenth European Conference on Computer Systems

Pages 83 - 98

https://doi.org/10.1145/3447786.3456230

Published: 21 April 2021 Publication History

Abstract

State-of-the-art streaming graph processing systems that provide Bulk Synchronous Parallel (BSP) guarantees remain oblivious to the computation sparsity present in iterative graph algorithms, which severely limits their performance. In this paper we propose DZiG, a high-performance streaming graph processing system that retains efficiency in presence of sparse computations while still guaranteeing BSP semantics. At the heart of DZiG is: (1) a sparsity-aware incremental processing technique that expresses computations in a recursive manner to be able to safely identify and prune updates (hence retaining sparsity); (2) a simple change-driven programming model that naturally exposes sparsity in iterative computations; and, (3) an adaptive processing model that automatically changes the incremental computation strategy to limit its overheads when computations become very sparse. DZiG outperforms state-of-the-art streaming graph processing systems, and pushes the boundary of dependency-driven processing for streaming graphs to over 10 million simultaneous mutations, which is orders of magnitude higher compared to the state-of-the-art systems.

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EuroSys '21: Proceedings of the Sixteenth European Conference on Computer Systems

April 2021

631 pages

ISBN:9781450383349

DOI:10.1145/3447786

General Chairs:
Antonio Barbalace
The University of Edinburgh
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Pramod Bhatotia
Technical University of Munich
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Cornell University
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Imperial College London

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https://doi.org/10.14778/3681954.3681979
Yin XZhao ZGupta R(2024)IncBoost: Scaling Incremental Graph Processing for Edge Deletions and Weight UpdatesProceedings of the 2024 ACM Symposium on Cloud Computing10.1145/3698038.3698524(915-932)Online publication date: 20-Nov-2024
https://dl.acm.org/doi/10.1145/3698038.3698524
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