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The parallelization of graph problems faces significant challenges: Data-driven computations, unstructured problems, poor locality and high data access to computation ratio. The graph representation used for parallel architectures plays a significant role in facing those challenges. Poorly chosen representations may unnecessarily drive up the communication cost of the algorithm, which will decrease its scalability. In the following, shared and distributed memory architectures are considered.

The parallelization of graph problems faces significant challenges: Data-driven computations, unstructured problems, poor locality and high data access to computation ratio. The graph representation used for parallel architectures plays a significant role in facing those challenges. Poorly chosen representations may unnecessarily drive up the communication cost of the algorithm, which will decrease its scalability. In the following, shared and distributed memory architectures are considered.

图问题的并行化面临着重大的挑战: 数据驱动的计算、非结构化问题、局部性差和计算数据访问率高。用于并行架构的图表示在面对这些挑战时扮演着重要的角色。选择的表示方式不当可能会增加不必要的算法通信代价，从而降低算法的可扩展性。在下面，我们将考虑共享和分布式的内存架构。

图问题的并行化面临着重大的挑战: 数据驱动的计算、非结构化问题、局部性差和计算数据访问率高。用于并行架构的图表示在面对这些挑战时扮演着重要的角色。选择的表示方式不当可能会增加不必要的算法连接代价，从而降低算法的可扩展性。在下面，我们将考虑共享和分布式的内存架构。