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In the distributed memory model, the usual approach is to partition the vertex set <math>V</math> of the graph into <math>p</math> sets <math>V_0, \dots, V_{p-1}</math>. Here, <math>p</math> is the amount of available processing elements (PE). The vertex set partitions are then distributed to the PEs with matching index, additionally to the corresponding edges. Every PE has its own subgraph representation, where edges with an endpoint in another partition require special attention. For standard communication interfaces like MPI, the ID of the PE owning the other endpoint has to be identifiable. During computation in a distributed graph algorithms, passing information along these edges implies communication.

In the distributed memory model, the usual approach is to partition the vertex set <math>V</math> of the graph into <math>p</math> sets <math>V_0, \dots, V_{p-1}</math>. Here, <math>p</math> is the amount of available processing elements (PE). The vertex set partitions are then distributed to the PEs with matching index, additionally to the corresponding edges. Every PE has its own subgraph representation, where edges with an endpoint in another partition require special attention. For standard communication interfaces like MPI, the ID of the PE owning the other endpoint has to be identifiable. During computation in a distributed graph algorithms, passing information along these edges implies communication.

在分布式存储模型中，常用的方法是将图的顶点集合<math>V</math> 分解为<math>P</math> 集合 <math> V₀,…,v_{{ p-1}}</math> 。这里，<math>p</math> 是可用处理元素(PE)的数量。然后，顶点集合分区被分配到具有匹配索引的 PE 中，并附加到相应的边上。每个 PE 都有自己的子图表示法，其中带有另一个分区中端点的边需要特别注意。对于像 MPI 这样的标准通信接口，拥有其他端点的 PE 的 ID 必须是可识别的。在分布式图算法的计算过程中，沿着这些边传递信息意味着通信。

在分布式存储模型中，常用的方法是将图的顶点集合<math>V</math> 分解为<math>P</math> 集合 <math> V₀,…,V_{{ p-1}}</math> 。这里，<math>p</math> 是可用处理元素(PE)的数量。然后，顶点集合分区被分配到具有匹配索引的 PE 中，并附加到相应的边上。每个 PE 都有自己的子图表示法，其中带有另一个分区中端点的边需要特别注意。对于像 MPI 这样的标准通信接口，拥有其他端点的 PE 的 ID 必须是可识别的。在分布式图算法的计算过程中，沿着这些边传递信息意味着通信。