| 91 | | The `MUSCLE_Barrier` set of functions ease the integration of MUSCLE with MPI. Most MPI functions (including barrier, broadcast and gather) use a polling mechanism when they wait for communication to happen. This will use all the available CPU power but will somewhat reduce the latency of the operation. However, with MUSCLE, often other submodels than the MPI submodel should do some computing, and while the MPI operation waits this will slow down other submodels immensely. Therefore, MUSCLE has its own barrier operation, which has a higher latency than MPI_Barrier, but will not use any CPU resources. Since only rank 0 of the process ever receives data from MUSCLE, and a receive must wait for another submodel to send the message, that is a good point for calling a barrier. If multiple receives follow each other, barrier only needs to be called after the last one. |
| | 91 | The `MUSCLE_Barrier` set of functions ease the integration of MUSCLE with MPI. Most MPI functions (including barrier, broadcast and gather) use a polling mechanism when they wait for communication to happen. This will use all the available CPU power but will somewhat reduce the latency of the operation. However, with MUSCLE, often other submodels than the MPI submodel should do some computing, and while the MPI operation waits this will slow down other submodels immensely. Therefore, MUSCLE has its own barrier operation, which has a higher latency than MPI_Barrier, but will not use any CPU resources. Since only rank 0 of the process ever receives data from MUSCLE, and a receive must wait for another submodel to send the message, that is a good point for calling a barrier. If multiple receives follow each other, barrier only needs to be called after the last one. Note that only ranks other than 0 are stopped by the barrier, rank 0 will not stop. |
