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MPI newbie: Requirements and installation of an MPI

I often get questions from those who are just starting with MPI; they want to know common things such as:

  • How to install / setup an MPI implementation
  • How to compile their MPI applications
  • How to run their MPI applications
  • How to learn more about MPI

This will be the first blog entry of several that attempts to guide MPI newbies down the parallelization path.

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Short message latency and NUMA effects

I’ve previously written a bunch about the effects of location, Location, LOCATION! on MPI applications.

Here’s another subtle NUMA effect that a well-tuned MPI implementation can hide from you: intelligently distributing traffic between multiple network interfaces.

Yeah, yeah, most MPI implementations have had so-called “multi-rail” support for a long time (i.e., using multiple network interfaces for MPI traffic).  But there’s more to it than that.

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How many network links do you have for MPI traffic?

If you’re a bargain basement HPC user, you might well scoff at the idea of having more than one network interface for your MPI traffic.

“I’ve got (insert your favorite high bandwidth network name here)! That’s plenty to serve all my cores! Why would I need more than that?”

I can think of (at least) three reasons off the top of my head.

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Ultra low latency Ethernet (UCS “usNIC”): questions and answers

I have previously written a few details about our upcoming ultra low latency solution for High Performance Computing (HPC).  Since my last blog post, a few of you sent me emails asking for more technical details about it.

So let’s just put it all out there.

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Open MPI and the MPI-3 MPI_T interface

Open MPI recently revamped its entire run-time parameter system (a.k.a., “MCA parameter system”) as part of its implementation effort for the “MPI_T” interface from MPI-3.

The MPI_T interface is a standardized interface designed for MPI tools, but can be used by regular MPI application programs, too.

Specifically, MPI_T provides programatic access to two types of MPI implementation data:

  1. Control variables: used to control the behavior of an MPI implementation
  2. Performance variables: provide access to internal MPI implementation performance metrics

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