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- W4381886095 abstract "Social network graphs follow a power-law distribution, enabling us to exploit the GPU’s hierarchical execution model for efficient computation through a degree-aware kernel computation approach. In this approach, nodes are mapped to different levels of parallelism on the GPU, depending upon their in-degree. To take advantage of this execution model, the nodes must be arranged in decreasing order of in-degree. However, doing so distorts the community structure (a property responsible for providing memory locality during computation), impacting the degree-aware kernel’s performance gain. To balance ordering by in-degree and community structure, we propose DRBS, a graph reordering algorithm that sorts the graph nodes while preserving enough community structure to enhance cache efficiency." @default.
- W4381886095 created "2023-06-25" @default.
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- W4381886095 creator A5052590511 @default.
- W4381886095 date "2023-04-01" @default.
- W4381886095 modified "2023-10-16" @default.
- W4381886095 title "Degree-Aware Kernel Mapping for Graph Processing on GPUs" @default.
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- W4381886095 doi "https://doi.org/10.1109/ispass57527.2023.00039" @default.
- W4381886095 hasPublicationYear "2023" @default.
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