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Traffic in parallel

August 8, 2014 at 5:00 am PST

In my last entry, I gave a vehicles-driving-in-a-city analogy for network traffic.

Let’s tie that analogy back to HPC and MPI.

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Still more traffic

August 2, 2014 at 5:00 am PST

I periodically write about network traffic, and how general / datacenter network traffic analysis is related to MPI / HPC.

In my last entry, I mentioned how network traffic has many characteristics in common with distributed computing. Routing decisions, for example, are made independently at each network switch.

Consider if you were looking down at a city from above. Look at all the cars driving around the city streets. It’s chaos: each car/truck/bus/etc. makes its own routing decisions. Each one is a different size. Each one potentially goes in a different direction. Each one continually merges and splits from other traffic.

Yet somehow it all works.

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Traffic (redux)

July 28, 2014 at 10:44 am PST

I’ve written about network traffic before (see this post and this post). It’s the subject of endless blog posts, help forums, and instructional guides across the internet.

In a High Performance Computing (HPC) context, there are some fascinating aspects about network traffic that are fairly different than other types of network traffic.
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Networks for MPI

May 24, 2014 at 7:14 am PST

It seems like we’ve gotten a rash of “how do I setup my new cluster for MPI?” questions on the Open MPI mailing list recently.

I take this as a Very Good Thing, actually — it means more and more people are tinkering with and discovering the power of parallel computing, HPC, and MPI.

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First public tools for the MPI_T interface in MPI-3.0

May 20, 2014 at 5:00 am PST

Today’s guest post is written by Tanzima Islam, Post Doctoral Researcher at Lawrence Livermore Laboratory, and Kathryn Mohror and Martin Schulz, Computer Scientists at Lawrence Livermore Laboratory.

MPI_T logoThe latest version of the MPI Standard, MPI 3.0, includes a new interface for tools: the MPI Tools Information Interface, or “MPI_T”.

MPI_T complements the existing MPI profiling interface, PMPI, and offers access to both internal performance information as well as runtime settings. It is based on the concept of typed variables that can be queried, read, and set through the MPI_T API.

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