在哪些情况下，MPI比MapReduce更适合? [英] What are some scenarios for which MPI is a better fit than MapReduce?

问题描述

据我了解，MPI使我可以更好地控制群集中不同节点之间如何通信.

As far as I understand, MPI gives me much more control over how exactly different nodes in the cluster will communicate.

在MapReduce/Hadoop中，每个节点进行一些计算，与其他节点交换数据，然后整理其结果分区.看起来很简单，但是由于您可以迭代该过程，因此即使像K-means或PageRank之类的算法也非常适合该模型.在具有调度局部性的分布式文件系统上，性能显然不错.相比之下，MPI使我可以明确控制节点之间如何相互发送消息.

In MapReduce/Hadoop, each node does some computation, exchanges data with other nodes, and then collates its partition of results. Seems simple, but since you can iterate the process, even algorithms like K-means or PageRank fit the model quite well. On a distributed file system with locality of scheduling, the performance is apparently good. In comparison, MPI gives me explicit control over how nodes send messages to each other.

谁能描述一个集群编程场景，其中更通用的MPI模型比更简单的MapReduce模型具有明显优势?

Can anyone describe a cluster programming scenario where the more general MPI model is an obvious advantage over the simpler MapReduce model?

推荐答案

几乎所有科学代码-有限差分，有限元素等.哪种类型导致循环答案，即任何不容易映射的分布式程序使用更通用的MPI模型可以更好地实现MapReduce.不确定这对您有多大帮助，我在发布答案后会立即投票否决.

Almost any scientific code -- finite differences, finite elements, etc. Which kind of leads to the circular answer, that any distributed program which doesn't easily map to MapReduce would be better implemented with a more general MPI model. Not sure that's much help to you, I'll downvote this answer right after I post it.

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