Titlebook: Euro-Par 2024: Parallel Processing; 30th European Confer Jesus Carretero,Sameer Shende,Martin Schreiber Conference proceedings 2024 The Edi

繁重

Ascendancy

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

上下連貫

樸素

ADAGE

充氣球

清楚

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

orthodox

https://doi.org/10.1007/978-0-8176-8200-2 major programming languages (e.g., Arc in Rust, shared_ptr and atomic 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

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-