In this example, you are switching from Intel to NVIDIA:Ĭheyenne uses SUSE Enterprise Linux, while the Casper data analysis and visualization (DAV) nodes run CentOS. The different operating systems provide different versions of some standard libraries, which may be incompatible with each othe r. Should you prefer to use a compiler other than the Intel default compiler, use module swap to make the change. Compiling multi-GPU MPI-CUDA code on Casper.nvcc – NVIDIA CUDA compiler (Using nvcc requires a C compiler to be present in the background nvc, icc, or gcc, for example.).To compile CUDA code to run on the Casper data analysis and visualization nodes, use the appropriate NVIDIA compiler command: PGI users should migrate to NVIDIA when possible. The PGI compiler has become the NVIDIA HPC (nvhpc) compiler and all future versions will be released as such. Include these flags for best performance when you use the Intel compiler: To get the man page for a compiler, log in to the system where you intend to use it, load the module, then execute man for the compiler. This will avoid job failures that can result from missing mpi launchers and library routines. Also, when you run the applications, be sure you have loaded the sam e module/version environment in which you created the applications. (If your script already includes one of the following generic MPI commands, there is no need to change it: mpif90, mpif77, ftn mpicc, cc mpiCC and CC.)Īlso consider using the compiler' s diagnostic flags to identify potential problems.Īny libraries you build to support an application should be built with the same compiler, compiler version, and compatible flags that were used to compile the other parts of the application, including the main executable(s). After loading the compiler module that you want to use, identify and run the appropriate compilation wrapper command from the table below.
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