Say if I wanted to parallelise an algorithm. Only certain parts of this algorithm can be parallelised. The other parts are trivial and not worth distributing. Do I do all of these trivial calculations on my master node and then broadcast the data to all remaining nodes OR do I conduct these calculations on ALL processors?

  • $\begingroup$ You seem to be under some misapprehension on how MPI works. There is no "master node". $\endgroup$ Commented Jul 21, 2018 at 15:03
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    $\begingroup$ @VictorEijkhout I don't think that is a misapprehension, but just a terminology problem. It is very common to name the processor with id=0 as a master one. And say, write to stdout only from it, or time things only on it. $\endgroup$
    – Anton Menshov
    Commented Jul 22, 2018 at 17:48

3 Answers 3


If the operation is as trivial as you say, and each node has all the information necessary to carry out the operation, then the communication will be substantially more expensive than recomputing locally on each node. That said, it's a good exercise to write both implementations and compare; experiment is better than the advice of strangers on the internet.

The break-even point when a task becomes non-trivial will depend on how the problem has been parallelized. It's much lower for shared-memory parallelism on the same machine than it is for distributed-memory parallelism across a cluster. For that matter, it probably depends on whether your cluster is using ethernet or infiniband. A handy reference that I often come back to is Latency Numbers Every Programmer Should Know. It's 6 years old at this point but the same principle still applies.

  • $\begingroup$ Your answer would be reasonable for shared memory. This question is tagged "MPI". $\endgroup$ Commented Jul 21, 2018 at 14:50
  • $\begingroup$ I think this answer is actually very reasonable for an MPI, OpenMP and hybrid context... $\endgroup$
    – BlaB
    Commented Aug 6, 2018 at 19:29

An addition to @Daniel Shapero's answer.

It might also be important to know if there are computations that can lead to different results depending on which machine they are launched (or just vary from launch to launch) – and how sensitive is your code to those fluctuations.

These will be even more pronounced if the cluster you are working on is inhomogeneous (nodes can be different).

Possible examples:

  1. Computations that depend on random number generator (with non-fixed seed)
  2. Floating-point calculations. Which might slightly differ from machine-to-machine (if the architecture/OS is different) or even on the same machine (say, OpenMP reduction has been performed).

Now, if this is the case and your future calculation are really sensitive to that, you might consider broadcasting instead of everybody calculating this step individually.

Again, the benchmarking will give you the ultimate answer.


Since you tagged this with "MPI": there is no master node. The stuff that is too trivial should be done redundantly on every process.

Bear in mind that even sending a single byte through an MPI message is the equivalent of (tens of) thousands of operations.

  • $\begingroup$ Of course, that scaling depends on the system infrastructure. If the problem is small enough to be running on a single machine, then it will be a lot closer to a shared memory paradigm, and possibly using shared memory as a method of exchange under the hood. $\endgroup$
    – origimbo
    Commented Jul 21, 2018 at 21:42

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