main(int argc,char **argv) { int myid,numprocs; int namelen; char processor_name[MPI_MAX_PROCESSOR_NAME]; MPI_Init(&argc,&argv); MPI_Comm_rank(MPI_COMM_WORLD,&myid); MPI_Comm_size(MPI_COMM_WORLD,&numprocs); MPI_Get_processor_name(processor_name,&namelen); printf("Hello World!Proc...
要评估的通信器。 指定MPI_COMM_WORLD常量以检索可用进程的总数。 size[out] 返回时,指示通信器组中的进程数。 返回值 返回成功时MPI_SUCCESS。 否则,返回值为错误代码。 在Fortran 中,返回值存储在IERROR参数中。 Fortran FORTRAN MPI_COMM_SIZE(COMM,SIZE,IERROR)INTEGERCOMM,SIZE, IERROR ...
MPI_Comm_size() 是MPI 中用于获取通信器中进程个数的函数,其函数原型如下: int MPI_Comm_size(MPI_Comm comm, int *size) 其中,comm 参数是一个 MPI 通信器,size 参数是一个指向整型变量的指针,用于存储通信器中进程的个数。函数返回一个整型值,表示函数的执行结果。如果函数成功执行,返回值为 MPI_...
https://stackoverflow.com/questions/29264640/mpiexec-and-python-mpi4py-gives-rank-0-and-size-1 也就是说mpi4py的编译时的mpi环境与运行时的mpi环境不同,于是才会出现这样的问题。 通过python查询当前mpi4py的编译环境: import mpi4py mpi4py.get_config() 发现这个MPI编译环境和运行环境确实不同。
Subject: Re: about MPI_Comm_size(PETSC_COMM_WORLD,&size) On 10/25/06,? Yixun Liu? <yxliu at fudan.edu.cn> wrote: Hi, ? My computer is a standare dual processors PC of Dell corp., but after calling ? ierr = PI_Comm_size(PETSC_COMM_WORLD,&size), I find the size is one....
> --- Original Message --- > *From:* Matthew Knepley <knepley at gmail.com> > *To:* petsc-users at mcs.anl.gov > *Sent:* Thursday, October 26, 2006 9:47 AM > *Subject:* Re: about MPI_Comm_size(PETSC_COMM_WORLD,&size) > > ...
HYPRE_Int num_procs, my_id;/* Initialize MPI */hypre_MPI_Init(&argc, &argv);hypre_MPI_Comm_size(hypre_MPI_COMM_WORLD,&num_procs); hypre_MPI_Comm_rank(hypre_MPI_COMM_WORLD,&my_id); row_starts =NULL; col_starts =NULL;if(my_id ==0) ...
Hello world: rank 0 of 1 running on node4...so while the job is farmed out to the 4 nodes, each node thinks that it's the master, and needs to do the propagation etc. Have I missed something embarassingly obvious here? Any help would really be great!Thanks Translate 0 Kudos ...
https://stackoverflow.com/questions/29264640/mpiexec-and-python-mpi4py-gives-rank-0-and-size-1 也就是说mpi4py的编译时的mpi环境与运行时的mpi环境不同,于是才会出现这样的问题。 通过python查询当前mpi4py的编译环境: import mpi4py mpi4py.get_config() 发现这个MPI编译环境和运行环境确实不同。
Hello world: rank 0 of 1 running on node4...so while the job is farmed out to the 4 nodes, each node thinks that it's the master, and needs to do the propagation etc. Have I missed something embarassingly obvious here? Any help would really be great!Thanks Translate...