PowerLyra 论文
2015引用 310
Graph Theory and AlgorithmsAdvanced Graph Neural NetworksParallel Computing and Optimization Techniques
摘要
Natural graphs with skewed distribution raise unique challenges to graph computation and partitioning. Existing graph-parallel systems usually use a "one size fits all" design that uniformly processes all vertices, which either suffer from notable load imbalance and high contention for high-degree vertices (e.g., Pregel and GraphLab), or incur high communication cost and memory consumption even for low-degree vertices (e.g., PowerGraph and GraphX).