边并行图编码器嵌入
Edge-Parallel Graph Encoder Embedding
AI总结：
提出边并行图编码器嵌入算法，在Ligra引擎中并行化GEE，利用无锁原子指令避免数据竞争，在18亿条边的图上实现500倍加速。
摘要：
用于图嵌入的新算法已降低了寻找低维表示的渐近复杂度。独热图编码器嵌入（GEE）对边进行单次线性遍历，并产生渐近收敛于谱嵌入的嵌入。该方法在扩展性和性能上的优势一直受到解释型语言中串行实现的限制。我们将GEE重构为Ligra图引擎中的并行程序，该程序对图的边映射函数，并使用无锁原子指令防止数据竞争。在具有18亿条边的图上，这比原始实现实现了500倍的加速，比即时编译版本实现了17倍的加速。
英文摘要：
New algorithms for embedding graphs have reduced the asymptotic complexity of finding low-dimensional representations. One-Hot Graph Encoder Embedding (GEE) uses a single, linear pass over edges and produces an embedding that converges asymptotically to the spectral embedding. The scaling and performance benefits of this approach have been limited by a serial implementation in an interpreted language. We refactor GEE into a parallel program in the Ligra graph engine that maps functions over the edges of the graph and uses lock-free atomic instrutions to prevent data races. On a graph with 1.8B edges, this results in a 500 times speedup over the original implementation and a 17 times speedup over a just-in-time compiled version.
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