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Structure-Aware Thread Throttling for Energy-Efficient Graph Processing on Shared-Memory Systems
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DOI：10.1016/j.future.2025.108297.png)
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Abstract
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Graph processing on shared-memory systems fully utilizes memory bandwidth and avoids communication overhead, yet it is not as energy-efficient as expected. Since memory bandwidth becomes a major bottleneck, using more cores does not always lead to better performance. To address this limitation, we propose two predictive thread-throttling models that infer the optimal number of threads from graph characteristics such as sparsity and skewness, aiming to reduce energy consumption with minimal performance loss.The weighted model is implemented on four representative frameworks, including GreGraphMat, GrePolymer, GreGrazelle, and GreLigra, and evaluated on two CPU architectures, Intel Xeon Gold 6230R and Loongson 3A6000. Experimental results show up to beyound 30% improvement in Energy-Delay Product (EDP) on Intel and consistent 15.8% reduction with 1.16 × speedup on Loongson. These results confirm that the proposed models achieve robust energy efficiency, strong scalability, and cross-architecture generality in shared-memory graph processing.
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