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Massively Parallel Computation via Remote Memory Access

机译:通过远程内存访问大规模并行计算

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We introduce the Adaptive Massively Parallel Computation (AMPC) model, which is an extension of the Massively Parallel Computation (MPC) model. At a high level, the AMPC model strengthens the MPC model by storing all messages sent within a round in a distributed data store. In the following round, all machines are provided with random read access to the data store, subject to the same constraints on the total amount of communication as in the MPC model. Our model is inspired by the previous empirical studies of distributed graph algorithms [8, 30] using MapReduce and a distributed hash table service [17]. This extension allows us to give new graph algorithms with much lower round complexities compared to the best-known solutions in the MPC model. In particular, in the AMPC model we show how to solve maximal independent set in O(1) rounds and connectivity/minimum spanning tree in O(loglog_(m/n) n) rounds both using O(n~δ) space per machine for constant δ < 1. In the same memory regime for MPC, the best-known algorithms for these problems require poly log n rounds. Our results imply that the 2-Cycle conjecture, which is widely believed to hold in the MPC model, does not hold in the AMPC model.
机译:我们介绍了自适应大规模并行计算(AMPC)模型,其是大规模并行计算(MPC)模型的扩展。在高级别,AMPC模型通过存储分布式数据存储中的圆形内发送的所有消息来增强MPC模型。在下一轮一轮中,所有计算机都提供对数据存储的随机读访问,而在MPC模型中的总通信量相同的约束。我们的模型是通过MapReduce和分布式哈希表服务的分布式图算法[8,30]的先前实证研究的启发。与MPC模型中的最佳已知的解决方案相比,此扩展允许我们提供具有远低级复杂性的新图形算法。特别是,在AMPC模型中,我们展示了如何在O(loglog_(m / n)n)中使用O(n〜Δ)空间的O(1)圆形和连接/最小生成树中的最大独立集合使用每台机器的o(n〜Δ)空间对于恒定Δ<1.在MPC的相同内存状态下,这些问题的最佳已知算法需要多个日志N轮。我们的结果意味着,两轮循环猜想,广泛认为在MPC模型中持有,在AMPC模型中不会持有。

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