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Improving Streaming Graph Processing Performance using Input Knowledge

Authors:

Alaa R. Alameldeen,

Zeshan Chishti,

Yuan XieAuthors Info & Claims

MICRO '21: MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture

Pages 1036 - 1050

https://doi.org/10.1145/3466752.3480096

Published: 17 October 2021 Publication History

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Abstract

Streaming graphs are ubiquitous in today’s big data era. Prior work has improved the performance of streaming graph workloads without taking input characteristics into account. In this work, we demonstrate that input knowledge-driven software and hardware co-design is critical to optimize the performance of streaming graph processing. To improve graph update efficiency, we first characterize the performance trade-offs of input-oblivious batch reordering. Guided by our findings, we propose input-aware batch reordering to adaptively reorder input batches based on their degree distributions. To complement adaptive batch reordering, we propose updating graphs dynamically, based on their input characteristics, either in software (via update search coalescing) or in hardware (via acceleration support). To improve graph computation efficiency, we present input-aware work aggregation which adaptively modulates the computation granularity based on inter-batch locality characteristics. Evaluated across 260 workloads, our input-aware techniques provide on average 4.55 × and 2.6 × improvement in graph update performance for different input types (on top of eliminating the performance degradation from input-oblivious batch reordering). The graph compute performance is improved by 1.26 × (up to 2.7 ×).

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Cited By

He MLiu FDo S(2024)Heterogeneous Hyperthreading2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)10.1109/IPDPSW63119.2024.00018(68-78)Online publication date: 27-May-2024
https://doi.org/10.1109/IPDPSW63119.2024.00018
Ren JXu DYang SZhao JLi ZNavasca CWang CXu HLi D(2024)Enabling Large Dynamic Neural Network Training with Learning-based Memory Management2024 IEEE International Symposium on High-Performance Computer Architecture (HPCA)10.1109/HPCA57654.2024.00066(788-802)Online publication date: 2-Mar-2024
https://doi.org/10.1109/HPCA57654.2024.00066
Yu HZhang YZhao JLiao YHuang ZHe DGu LJin HLiao XLiu HHe BYue J(2023)RACE: An Efficient Redundancy-aware Accelerator for Dynamic Graph Neural NetworkACM Transactions on Architecture and Code Optimization10.1145/361768520:4(1-26)Online publication date: 14-Dec-2023
https://dl.acm.org/doi/10.1145/3617685
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cover image ACM Conferences

MICRO '21: MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture

October 2021

1322 pages

ISBN:9781450385572

DOI:10.1145/3466752

Copyright © 2021 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 17 October 2021

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Cited By

He MLiu FDo S(2024)Heterogeneous Hyperthreading2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)10.1109/IPDPSW63119.2024.00018(68-78)Online publication date: 27-May-2024
https://doi.org/10.1109/IPDPSW63119.2024.00018
Ren JXu DYang SZhao JLi ZNavasca CWang CXu HLi D(2024)Enabling Large Dynamic Neural Network Training with Learning-based Memory Management2024 IEEE International Symposium on High-Performance Computer Architecture (HPCA)10.1109/HPCA57654.2024.00066(788-802)Online publication date: 2-Mar-2024
https://doi.org/10.1109/HPCA57654.2024.00066
Yu HZhang YZhao JLiao YHuang ZHe DGu LJin HLiao XLiu HHe BYue J(2023)RACE: An Efficient Redundancy-aware Accelerator for Dynamic Graph Neural NetworkACM Transactions on Architecture and Code Optimization10.1145/361768520:4(1-26)Online publication date: 14-Dec-2023
https://dl.acm.org/doi/10.1145/3617685
Yu SGong SZhang YYu WYin QTian CTao QYan YYu GZhou J(2023)Layph: Making Change Propagation Constraint in Incremental Graph Processing by Layering Graph2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00212(2766-2779)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00212
Jiang ZMao FGuo YLiu XLiu HLiao XJin HZhang W(2023)ACGraph: Accelerating Streaming Graph Processing via Dependence Hierarchy2023 60th ACM/IEEE Design Automation Conference (DAC)10.1109/DAC56929.2023.10247904(1-6)Online publication date: 9-Jul-2023
https://doi.org/10.1109/DAC56929.2023.10247904
Huang YZheng LYao PWang QLiu HLiao XJin HXue J(2022)ReaDy: A ReRAM-Based Processing-in-Memory Accelerator for Dynamic Graph Convolutional NetworksIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2022.319915241:11(3567-3578)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1109/TCAD.2022.3199152

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