Almost any scientific code — finite differences, finite elements, etc. Which kind of leads to the circular answer, that any distributed program which doesn’t easily map to MapReduce would be better implemented with a more general MPI model. Not sure that’s much help to you, I’ll downvote this answer right after I post it.
This assumes you are using C. There are similar concepts in C++ and Fortran. You would just specify MPI_ANY_SOURCE as the source in the MPI_recv(). The status struct contains the actual source of the message. int buf[32]; MPI_Status status; // receive message from any source MPI_recv(buf, 32, MPI_INT, MPI_ANY_SOURCE, MPI_ANY_TAG, MPI_COMM_WORLD, &status); int replybuf[]; // … Read more
mpicc is just a wrapper around certain set of compilers. Most implementations have their mpicc wrappers understand a special option like -showme (Open MPI) or -show (Open MPI, MPICH and derivates) that gives the full list of options that the wrapper passes on to the backend compiler. For example, in Open MPI, wrappers are C++ … Read more
It seems that your implementation is correct so far. The issue is with how you are printing the local array for the first processor. The local array is formed column-wise because of how the file is being read and the data is distributed among the processes. To print the local array for the first processor … Read more
All collective operations in MPI before MPI-3.0 are blocking, which means that it is safe to use all buffers passed to them after they return. In particular, this means that all data was received when one of these functions returns. (However, it does not imply that all data was sent!) So MPI_Barrier is not necessary … Read more
First remove the update-alternatives for openmpi: sudo rm -f /etc/alternatives/mpi* /var/lib/dpkg/alternatives/mpi* Then install openmpi-bin again: sudo apt install open-mpi answer Y when asked for confirmation. In Ubuntu 22.04 (or if the above line doesn’t work) – try this instead: sudo apt install openmpi-bin
I agree with all previous answers, but I think a key point that is not made totally clear is that one reason that MPI might be considered hard and Erlang easy is the match of model to the domain. Erlang is based on a concept of local memory, asynchronous message passing, and shared state solved … Read more
Apparently oversubscribing can be attained using the “–oversubscribe” option with mpirun – did the trick for me with running torque/maui
One of the big features of Hadoop/map-reduce is the fault tolerance. Fault tolerance is not supported in most (any?) current MPI implementations. It is being thought about for future versions of OpenMPI. Sandia labs has a version of map-reduce which uses MPI, but it lacks fault tolerance.
This indicates the linux has delivered a SIGTERM to your process. This is usually at the request of some other process (via kill()) but could also be sent by your process to itself (using raise()). This signal requests an orderly shutdown of your process. If you need a quick cheatsheet of signal numbers, open a … Read more