使用设置type=MPI的doParallel包与直接使用doMPI的差异是什么？

Great question! Let’s break down the key distinctions between these two approaches to MPI-based parallelism with foreach, using your provided code examples as context:

1. Underlying Implementation & Dependencies #

• doParallel(type='MPI'): This is a wrapper that leverages the snow package’s MPI cluster implementation under the hood. When you call makeCluster(..., type='MPI'), you’re actually creating a snow MPI cluster, and doParallel acts as an adapter to connect foreach to that cluster. This means you’re indirectly relying on both the snow and MPI libraries.
• doMPI: This is a purpose-built package designed specifically to integrate MPI directly with foreach, no snow middleman involved. It’s built from the ground up for MPI, so it has a tighter, more native integration with MPI’s core features.

2. Cluster Initialization Behavior #

Looking at your code snippets highlights a clear difference here:

• doParallel: You use makeCluster(mpi.universe.size(), type='MPI'). The mpi.universe.size() function retrieves the total number of MPI processes allocated by your MPI runtime (like OpenMPI). Important note: this count includes the master process, so you’ll end up with one fewer worker process than the universe size unless you adjust the number manually.
• doMPI: The startMPIcluster(count=2) call explicitly defines how many worker processes you want. The master process runs separately by default, so the count parameter directly maps to the number of workers. doMPI also offers more granular control over MPI initialization, like specifying custom communicators or rank assignments.

3. Flexibility vs. MPI-Specific Features #

• doParallel: It’s a general-purpose adapter that works with multiple cluster types (MPI, PSOCK, FORK). This makes it easy to switch between parallel backends without rewriting much foreach code. However, this generality means it doesn’t expose all of MPI’s advanced features.
• doMPI: Optimized exclusively for MPI, it supports advanced MPI functionality like collective communication operations, fine-grained error handling for MPI processes, and integration with MPI’s native process management tools. If you need to leverage MPI-specific capabilities beyond basic parallel loops, doMPI is the better fit.

4. Resource Cleanup #

• doParallel: To shut down the cluster, you use stopCluster(cl) — the same method used for snow clusters. This handles stopping worker processes but may require additional steps to fully terminate MPI runtime resources in some cases.
• doMPI: Proper cleanup requires closeCluster(cl) followed by mpi.quit(). This aligns with MPI’s native termination workflow, ensuring all MPI processes (including the master) are properly shut down, reducing the risk of leftover orphaned processes.

5. Performance #

• In simple scenarios like your Sys.sleep() example, the performance difference may be negligible. But for computationally intensive tasks with significant inter-process data transfer, doMPI can offer better efficiency. It avoids the abstraction layer of snow, allowing direct use of MPI’s native communication protocols which are optimized for cluster environments.
• doParallel’s MPI mode inherits any overhead from the snow layer, which can add up in large-scale clusters with many processes.

内容的提问来源于stack exchange，提问作者correocont