Jul 2026· 2026 8th International Conference on Electronics and Communication, Network and Computer Technology (ECNCT)· pp. 501-507· 0 citations· 16 references
Abstract
Two independent families of parallel algorithms exist for hypergraph k-core decomposition—the HK codebase (OpenMP, vertex-centric) and HyperCD (ParlayLib, edge-centric with frontier scheduling). Yet work that systematically compares them at the level of individual optimization flags is surprisingly scarce. We constructed 10 new HK variants by toggling 7 compile-time preprocessor flags, benchmarked them alongside 5 HyperCD variants on 7 real-world datasets (489 timed runs at 32 threads), and probed which optimizations actually drive the performance gap. The first seven constructed variants improve on the official baseline by 21% through per-dataset routing, with three cross-pollinated variants adding another 7%. The strongest HK variants sit at an Amdahl’s Law ceiling—their parallel efficiency is lower than the naive baseline. A scalability analysis across 2–32 threads confirms that HK scales with thread count (3.1×–6.2×) while HyperCD does not (∼1.0×). Adding HyperCD through a size-based routing rule yields a combined 1.46× cumulative speedup, from 27.05 s down to 18.63 s, with all results verified byte-for-byte against golden baselines.
Cohesive subgraph mining is a fundamental task in graph data analytics. We re-visit the problem of listing all minimal $k$-cores, where a $k$-core is a subgraph in which every vertex has degree at least $k$, and minimality requires that no proper subset remains a $k$-core. Existing methods are computationally prohibiti...
Yukai Sun, Kaiqiang Yu, Shengxin Liu et al.· IEEE International Conferenc...· 0 citations
CP-McSplitDAL is introduced, a cooperative parallel framework that extends McSplit-DAL with portfolio-style multi-heuristic search on shared-memory machines and achieves lower regret in time to optimality, improves solution quality under time limits, and better exploits multi-core hardware than non-cooperative or purel...
Lorenzo Cardone, Stefano Quer· International Conference on...· 0 citations
Parallel performance depends not only on programming language and runtime design, but also on how the dominant execution bottleneck changes as parallelism increases. We present a controlled cross-language study of Rust, Julia, Haskell, and Python using Merge Sort, Closest Pair of Points, and Numerical Sum in a multicor...
Muhammad Hassam Aslam Khan, Daniel Stapleton, Medha Kulkarni et al.· Software· 0 citations
This work introduces serial and parallel algorithms for multi-core CPUs, as well as the first GPU-based algorithm for multi-core CPUs, and introduces a grid structure the authors call FC-Grid, which is exploited to distribute work among threads.
Cheng Huang, Davide Mottin, Ira Assent· Proceedings of the VLDB Endo...· 0 citations
Deep hypergraph learning is evaluated almost entirely through leaderboards that rank methods by mean accuracy over a few random seeds, usually without significance testing. Is there a best hypergraph neural network, or does the apparent ordering reflect seed noise? We independently recomputed the node-classification tr...
V. Tynchenko, S. Kurashkin, Alexey S. Borodulin et al.· Machine Learning and Knowled...· 0 citations
This paper shows how to uplift wco join algorithms so as to incorporate such filtering natively, improving efficiency and demonstrates the superiority of this approach by extending the Ring -- a compact index that provides wco resolution of BGPs within almost no extra space on top of the graph -- so as to handle proper...
