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New depth-first layouts boost $k^2$-tree compression and performance

By PulseAugur Editorial · [1 sources] ·

Researchers have developed new depth-first representations for $k^2$-trees to improve their efficiency and compression, addressing the poor cache performance of traditional level-wise layouts. The proposed methods, including EDF-1, BP, CEDF, and CBP, utilize depth-first layouts and a linear-time compression technique for identical subtrees. Experimental results on web graphs, Wikidata, and synthetic data show that these new representations are competitive, with CEDF offering the best compression and EDF-1/CEDF consistently reducing peak memory usage, leading to improved performance in matrix operations. AI

RANK_REASON Academic paper detailing new data structure representations and experimental evaluation. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New depth-first layouts boost $k^2$-tree compression and performance

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Academic paper detailing new data structure representations and experimental evaluation. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Francesco Tosoni ·

    Extended Depth-First Representations of $k^2$-trees

    In this paper, we study static, computation-friendly, lossless compression formats for graphs, focusing on memory locality and operational efficiency of $k^2$-trees. We observe that their traditional level-wise layouts suffer from poor cache performance due to weak locality, espe…

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