繁重
发表于 2025-3-25 03:57:00
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Ascendancy
发表于 2025-3-25 08:14:22
https://doi.org/10.1007/978-3-319-27501-7 performance for SpMM is challenging due to the irregular distribution of non-zero elements and memory access patterns. Therefore, several sparse matrix reordering algorithms have been developed to improve data locality for SpMM. However, existing approaches for reordering sparse matrix have not con
上下连贯
发表于 2025-3-25 14:20:08
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朴素
发表于 2025-3-25 17:29:18
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ADAGE
发表于 2025-3-25 23:59:15
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充气球
发表于 2025-3-26 02:18:33
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清楚
发表于 2025-3-26 05:39:33
https://doi.org/10.1007/978-981-15-0173-9he electronic design automation (EDA) field to social network analysis. Many contemporary real-world networks are dynamic and evolve rapidly over time. In such cases, recomputing the BFS from scratch after each graph modification becomes impractical. While parallel solutions, particularly for GPUs,
VOC
发表于 2025-3-26 12:25:40
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orthodox
发表于 2025-3-26 13:16:06
https://doi.org/10.1007/978-0-8176-8200-2 major programming languages (e.g., Arc in Rust, shared_ptr and atomic<shared_ptr> in C++)..In concurrent reference counting, read-reclaim races, where a read of a mutable variable races with a write that deallocates the old value, require special handling: use-after-free errors occur if the object
不透明性
发表于 2025-3-26 18:34:03
https://doi.org/10.1007/978-3-031-15112-5 thus limiting scalability. Semantic relaxation has the potential to address this issue, increasing the parallelism at the expense of weakened semantics. Although prior research has shown that improved performance can be attained by relaxing concurrent data structure semantics, there is no one-size-