= Offline Mode: Initializing the parallelization for PDAF = {{{ #!html

Offline Mode: Implementation Guide

  1. Main page
  2. Adaptation of the parallelization
  3. Initialization of PDAF
  4. Implementation of the analysis step
  5. Memory and timing information
}}} [[PageOutline(2-3,Contents of this page)]] == Overview == Like many numerical models, PDAF uses the MPI standard for the parallelization. The offline mode of PDAF can be used without explicit parallelization. In this case, the assimilation program does not need to be compiled with an MPI libraries. It is sufficient to compiler with the dummy implementation of MPI for a single process that is supplied with the source code of PDAF. Even in this case, it is necessary to execute the routine `init_parallel_pdaf` as described below. If the assimilation program in the offline mode is executed as a parallel program one will usually choose the number of processors to be equal to the number of processes used in the model integrations. Then, on can use the same domain decomposition that was used in the model integration. Generally, it is recommended to run the assimilation program as a parallel program, if a large-scale model is used such that the execution of the program on a single processor would take too long or if the program would require too much memory to be run on a single processor. In the offline model not all parallel feature of PDAF are used. As PDAF is not directly attached to the model code, it should be possible to simply use the routine `init_parallel_pdaf` in the form provided with the PDAF package. For completeness, we describe here the communicators required for PDAF as well as the initialization routine. == Three communicators == MPI uses so-called 'communicators' to define sets of parallel processes. As the ensemble integrations are performed externally to the assimilation program in the case of the offline mode, the communicators of the PDAF parallelization are only used internally to PDAF. However, as the communicators need to be initialized, we still describe them here. In order to provide the 2-level parallelism, three communicators need to be initialized that define the processes that are involved in different tasks of the data assimilation system. The required communicators are initialized in the routine `init_parallel_pdaf` and called * `COMM_model` - defines the processes that are involved in the model integrations * `COMM_filter` - defines the processes that perform the filter analysis step * `COMM_couple` - defines the processes that are involved when data are transferred between the model and the filter The parallel region of an MPI parallel program is initialized by calling `MPI_init`. By calling `MPI_init`, the communicator `MPI_COMM_WORLD` is initialized. This communicator is pre-defined by MPI to contain all processes of the MPI-parallel program. In the offline mode, it would be sufficient to conduct all parallel communication using only `MPI_COMM_WORLD`. However, as PDAF uses the three communicators listed above internally, they have to be initialized. In general they will be identical to `MPI_COMM_WORLD` with the exception of `COMM_couple`, which is not used in the offline mode. == Initializing the communicators == The routine `init_parallel_init`, which is supplied with the PDAF package, initializes the necessary communicators for the assimilation program and PDAF. The routine should be added to the model usually directly after the initialization of the parallelization described above. The routine `init_parallel_pdaf` also defines the communicators `COMM_filter` and `COMM_couple` that were described above. The provided routine `init_parallel_init` is a template implementation. For the offline mode, it should not be necessary to modify it! For the offline mode, the variable `n_modeltasks` should always be set to one, because no integrations are performed in the assimilation program. The routine `init_parallel_pdaf` splits the communicator `MPI_COMM_WORLD` and defines the three communicators described above. In addition, the variables `npes_world` and `mype_world` are defined. These can be used in the user-supplied routines to control, for example, which process write information to the screen. The routine defines several more variables that are declared and held in the module `mod_parallel`. It can be useful to use this module with the model code as some of these variables are required when the initialization routine of PDAF (`PDAF_init`) is called. == Arguments of `init_parallel_pdaf` == The routine `init_parallel_pdaf` has two arguments, which are the following: {{{ SUBROUTINE init_parallel_pdaf(dim_ens, screen) }}} * `dim_ens`: An integer defining the ensemble size. This allows to check the consistency of the ensemble size with the number of processes of the program. For the offline mode, one should set this variable to 0. In this case no consistency check for the ensemble size with regard to parallelization is performed. * `screen`: An integer defining whether information output is written to the screen (i.e. standard output). The following choices are available: * 0: quite mode - no information is displayed. * 1: Display standard information about the configuration of the processes (recommended) * 2: Display detailed information for debugging == Compiling and testing the assimilation program == IF one want to check the initialization of the parallelization, one can compile the assimilation program with just the call to `init_parallel_pdaf` as well as calls to `MPI_init` and `MPI_finalize`. One can test the extension by running the compiled assimilation program. If `screen` in the call to `init_parallel_pdaf` is set to 1 in the call to init_parallel_pdaf, the standard output should include lines like {{{ PDAF: Initializing communicators PE configuration: world filter model couple filterPE rank rank task rank task rank T/F ---------------------------------------------------------- 0 0 1 0 1 0 T 1 1 1 1 2 0 T 2 2 1 2 3 0 T 3 3 1 3 4 0 T }}} These lines show the configuration of the communicators. This example was executed using 4 processes and `n_modeltasks=1`.