A Structure-Aware Storage Optimization for Out-of-Core Concurrent Graph Processing

Liao, Xiaofei; Zhao, Jin; He, Bingsheng; He, Ligang; Jin, Hai; Gu, Lin

doi:10.1109/tc.2021.3098976

IEEE Trans. Comput.

2022

DOI: 10.1109/tc.2021.3098976

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A Structure-Aware Storage Optimization for Out-of-Core Concurrent Graph Processing

Xiaofei Liao

Jin Zhao

Bingsheng He

et al.

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Cited by 4 publications

(1 citation statement)

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“…GraphM [101] is an effective storage system which can be easily embedded into the existing graph computing sys-tem and make full use of the data access similarity of concurrent graph computing tasks, allowing the graph structure data to be regularly flowed into memory/cache and shared by concurrent graph computing tasks, improving the throughput of concurrent graph computing tasks by reducing data access and storage overhead. Subsequently, GraphSO [118] adopts a fine-grained graph data management mechanism and uses an adaptive data repartitioning strategy and a structure-aware graph data caching mechanism at runtime to further reduce redundant I/O for concurrent graph computing tasks overhead and improve system throughput.…”

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confidence: 99%

Software Systems Implementation and Domain-Specific Architectures towards Graph Analytics

Jin

Zhao

et al. 2022

Intell Comput

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Graph analytics, which mainly includes graph processing, graph mining, and graph learning, has become increasingly important in several domains, including social network analysis, bioinformatics, and machine learning. However, graph analytics applications suffer from poor locality, limited bandwidth, and low parallelism owing to the irregular sparse structure, explosive growth, and dependencies of graph data. To address those challenges, several programming models, execution modes, and messaging strategies are proposed to improve the utilization of traditional hardware and performance. In recent years, novel computing and memory devices have emerged, e.g., HMCs, HBM, and ReRAM, providing massive bandwidth and parallelism resources, making it possible to address bottlenecks in graph applications. To facilitate understanding of the graph analytics domain, our study summarizes and categorizes current software systems implementation and domain-specific architectures. Finally, we discuss the future challenges of graph analytics.

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confidence: 99%

Software Systems Implementation and Domain-Specific Architectures towards Graph Analytics

Jin

Zhao

et al. 2022

Intell Comput

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A disk I/O optimized system for concurrent graph processing jobs

Xu,

Wang,

Jiang

et al. 2024

Front. Comput. Sci.

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LargeGraph

Peng

Liao

Jin

et al. 2021

ACM Trans. Archit. Code Optim.

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Many out-of-GPU-memory systems are recently designed to support iterative processing of large-scale graphs. However, these systems still suffer from long time to converge because of inefficient propagation of active vertices’ new states along graph paths. To efficiently support out-of-GPU-memory graph processing, this work designs a system LargeGraph . Different from existing out-of-GPU-memory systems, LargeGraph proposes a dependency-aware data-driven execution approach , which can significantly accelerate active vertices’ state propagations along graph paths with low data access cost and also high parallelism. Specifically, according to the dependencies between the vertices, it only loads and processes the graph data associated with dependency chains originated from active vertices for smaller access cost. Because most active vertices frequently use a small evolving set of paths for their new states’ propagation because of power-law property, this small set of paths are dynamically identified and maintained and efficiently handled on the GPU to accelerate most propagations for faster convergence, whereas the remaining graph data are handled over the CPU. For out-of-GPU-memory graph processing, LargeGraph outperforms four cutting-edge systems: Totem (5.19–11.62×), Graphie (3.02–9.41×), Garaph (2.75–8.36×), and Subway (2.45–4.15×).

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A Structure-Aware Storage Optimization for Out-of-Core Concurrent Graph Processing

Cited by 4 publications

References 31 publications

Software Systems Implementation and Domain-Specific Architectures towards Graph Analytics

Software Systems Implementation and Domain-Specific Architectures towards Graph Analytics

A disk I/O optimized system for concurrent graph processing jobs

LargeGraph

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