![]() Romans 2:1 Therefore you have no excuse, O man, every one of you who judges. You Are Part of the Problem, Not the Solution Someone needs to do something to stop all the evil/unrighteousness/unjustice.Something needs to be done to fix our problems and pains.There is an urgent global problem that is only getting worse.Something has gone terribly wrong (social media makes this amplified).If you think something is wrong or you want extra examples, please let me know in the comments.4 Undeniable Facts We are All Feeling in our Conscience (Rom. However, it is not possible to fit everything into one post. There are many things that can be done with Pstream. In order to avoid deadlocks, make sure that the number of OPstream is equal to the number of IPstream. You can consider these as MPI_Send and MPI_Recv. As you can see above, to send or receive data you have to create OPstream or IPstream objects, respectively. In the end, each process prints its data to the console. Receiving processes hold their data in another variable called recvData. In that case, process 0 communicates with process 9, process 1 communicates with process 8, etc. Here is the code snippet that solved my problem: #include "PstreamReduceOps.H"ĭata = Pstream::myProcNo() Īssume the number of processes used in this program is 10. ![]() The numbers are arbitrary but explain my problem very well. How can you gather the list from all processors like 8 (0 0 1 1 2 2 3 3)? The first two numbers of the list are calculated on processor 0, the second two numbers of the list are calculated on processor 1, etc. You have 8 cells of a computational domain and split the domain into 4 partitions. One of the problems I faced during this summer was this problem: You can check out Pstream.H file for the rest. ![]() There are many other methods that Pstream class provides. For that, we can use Pstream class’ gather method: labelList data(10, Pstream::myProcNo() + 1) įor the full list, please checkout ops.H file. We can use this class for the inter-processor communications stream.įor instance, gathering information from other processes is a common task. OpenFOAM provides a wrapper class called Pstream. Pstream Class and Collective CommunicationĪs you can see from the above example, we did not use MPI routines in our program. If someone tries to run it in serial, it will complain and throw an error. I also added a check to make sure the program is run only in parallel. Now, the final step is to print the message to the console: Pout If you want to disable this behavior, add this line above the line where you include setRootCase.H file. If you do not want your program to search existing processor directories, you can disable this behavior by: argList::noCheckProcessorDirectories() The reason why we add this line inside the main function is that this header file is constructed from the arguments ( int argc, char* argv) in the main function.īy default, our program searches for processor directories and if it does not find any directory like processor2, it throws an error saying that it could not find this directory. Therefore, we have to include setRootCase.H file inside the main function to set up the basic command-line arguments to provide the program. When we run our program, we have to provide an extra argument to let the compiler know that this run is not a serial run. You can look at other OpenFOAM programs’ Make directory to have an idea of how to compile your program. The details of compiling the program are not given here. Let’s write a basic hello world program and run it in parallel. Knowledge of how to compile a program in OpenFOAM using wmake OpenMPI (for installation you can refer to this article) The compiled version of OpenFOAM-dev (v2106 is used in this blog but unless it is too old, you can use any version) Therefore, please use these examples at your own risk. I am neither expert in parallel programming nor OpenFOAM. Most of the information presented here is derived from my experience during the Summer of HPC project which is developing a parallel pre-processing utility in OpenFOAM. ![]() So, that is why I decided to write this blog. This kind of blog could have helped me so much. It was difficult for me to learn parallel programming in OpenFOAM at the beginning of the SoHPC. OpenFOAM has native support for parallel programming with MPI but writing parallel programs in OpenFOAM is not the same as writing parallel programs with MPI. For computation-heavy problems, parallel programming can dramatically reduce the computation time. However, some problems can take too much time to execute. It is also used to solve HPC problems related to fluid dynamics. OpenFOAM is a widely used CFD toolbox in academia and industry.
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