What are some scenarios for which MPI is a better fit than MapReduce?

Question

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

In MapReduce/Hadoop, each node does some com

Answer 1

When the computation and data that you are using have irregular behaviors that mostly translates to many message-passings between objects, or when you need low level hardware level accesses e.g. RDMA then MPI is better. In some answers that you see in here the latency of tasks or memory consistency model gets mentioned, frameworks like Spark or Actor Models like AKKA have shown that they can compete with MPI. Finally one should consider that MPI has benefit of being for years the main base for development of libraries needed for scientific computations (This are the most important missing parts missing from new frameworks using DAG/MapReduce Models).

All in all, I think the benefits that MapReduce/DAG models are bringing to the table like dynamic resource managers, and fault tolerance computation will make make them feasible for scientific computing groups.