Hands-on tutorial {#Hands_on}

In this tutorial, you will build a PDI-enabled application step-by-step from a PDI-free base. You will end-up building the C version of the \ref PDI_example "example provided with PDI" for the the \ref trace_plugin "Trace", \ref Decl_HDF5_plugin "Decl'HDF5", and \ref pycall_plugin "Pycall" plugins. Additional examples are available for the other plugins.

Setup

\attention To run this hands-on tutorial, you first need to \ref Installation "install PDI" and setup your environment.

PDI installation

\ref Installation "PDI installation" is documented in a \ref Installation "dedicated page".

Hands-on tutorial setup

Once %PDI is installed, you can proceed with getting the sources for the hands-on tutorial from github:

git clone https://github.com/pdidev/tutorial.git
cd tutorial

Compilation

Before compilation, configure the tutorial by detecting all dependencies:

pdirun cmake .

\attention If you installed PDI in a standard path, the pdirun prefix is never required.

Once you have correctly modified each exercise according to instructions, you can compile it by running:

pdirun make ex?

Where ? is the number of the exercise.

Execution

You can run each exercise with the following command:

pdirun mpirun -n 4 ./ex?

Where ? is the number of the exercise and 4 represents the number of MPI processes to use.

To store the logs for later comparison, you can use the following command (for example for ex2.):

pdirun mpirun -n 1 ./ex2 | tee ex2.result.log

Now you're ready to work, good luck!

PDI-free code

Ex1. Getting started

Ex1. implements a simple heat equation solver using an explicit forward finite difference scheme parallelized with MPI. The code uses a block domain decomposition where each process holds a 2D block of data.

Locally, each process holds its local block of data with one additional element on each side for ghost zones.

In the following exercises however, %PDI will only be used to decouple I/O operations. There is no need to fully dive in the core of the solver described in the \ref heat_algorithm "PDI example algorithm" and implemented in the iter and exchange functions.

The specification tree in the .yml files and the main function are the locations where all the I/O-related aspects will be handled and the only ones you will actually need to fully understand or modify.

• Examine the source code, compile it and run it. There is no input/output operations in the code yet, so you can not see any result.

This example gets its configuration from a file in the \ref YAML "YAML format": ex1.yml file. If you're not familiar with YAML, please have a look at our quick \ref YAML "YAML format documentation" to understand it. The example uses the paraconf library to read this file.

• Play with and understand the code parameters in ex1.yml.

• Set values in ex1.yml to be able to run the code with 4 MPI processes.

mpirun -np 4 ./ex1

PDI core & trace plugin

Ex2. Now with some PDI

Ex2. is the same code as that of ex1. with %PDI calls added in main function. In our YAML file (ex2.yml), a new sub-tree has been added under the pdi key. This sub-tree is the %PDI specification tree passed to %PDI at initialization. Here, the %PDI \ref trace_plugin "Trace plugin"(trace) is used to trace %PDI calls.

• Examine the source code, compile it and run it.

• Add the required ::PDI_share and ::PDI_reclaim calls to have some PDI activities. You can then use the \ref trace_plugin "Trace plugin" (trace) plugin to observe these activities on the standard output.

\attention Notice that some share/reclaim pairs come one after the other while others are interlaced. Is one better than the other? If you do not know the answer to this question, just wait until Ex5. :)

Decl'HDF5 plugin

Ex3. HDF5 through PDI

In this exercise, the code is the same as in ex2. No need to touch the C code here, modification of the YAML file (ex3.yml) should be enough.

• Examine the YAML file, compile the code and run it.

The \ref Decl_HDF5_plugin "Decl'HDF5 plugin" (decl_hdf5) is added in the specification tree. In its configuration, the dsize variable is written.

• Write the psize and pcoord variables in addition to dsize .

To achieve this result, you will need to fill 2 sections in the YAML file.

1. The metadata (or data) section to indicate to %PDI the \ref datatype_node type of the fields that are exposed.

2. The decl_hdf5 section for the configuration of the \ref Decl_HDF5_plugin "Decl'HDF5 plugin".

\warning If you relaunch the executable, remember to delete your old ex3.h5 file before, otherwise the data will not be changed.

\warning Since we write to the same location independently of the MPI rank, you have two options: either you run the exercise with one MPI rank, or change the file name to have one output file per rank (this is what we chose in the solution).

Ex4. Writing some real data

In this exercise each MPI process will write its local 2D array block contained in the main_field variable to a separate HDF5 file. Once again, this can be done by modifying the YAML file only, no nee to touch the C file.

• Examine the YAML file, compile the code and run it.

Look at the number of blocks, you will have to use the correct number of MPI ranks to run the example.

Notice that in the YAML file, a list was used in the decl_hdf5 section with multiple write blocks instead of a single one as before in order to write to multiple files.

Also notice that this example now runs in parallel with two processes. Therefore it uses "$-expressions" to specify the file names and ensure we do not write to the same file from distinct ranks.

Unlike the other fields manipulated until now, the type of main_field is not fully known, its size is dynamic. By moving other fields in the metadata section, you can reference them from "$-expressions" in the configuration file.

• Use a $-expression to specify the size of main_field.

Unlike the other fields manipulated until now, main_field is exposed multiple times along execution. In order not to overwrite it every time it is exposed, you write one file per rank and per iteration. Or, you can write only one iteration data using the when: &ii=1 clause

Ex5. Introducing events

In ex4., two variables were written to ex4-data*.h5, but the file was opened and closed for each and every write. Since Decl'HDF5 only sees the data appear one after the other, it does not keep the file open. Since ii and main_field are shared in an interlaced way, they are both available to %PDI at the same time and could be written without opening the file twice. You have to use events for that, you will modify both the C and YAML file.

• Examine the YAML file and source code.

• In the C file, trigger a %PDI event named loop when both ii and main_field are shared. With the \ref trace_plugin "Trace plugin", check that the event is indeed triggered at the expected time.

• Use the on_event mechanism to trigger the write of ii and main_field. This mechanism can be combined with a when directive, in that case the write is only executed when both mechanisms agree.

• Also notice the extended syntax that make it possible to write data to a dataset whose name differs from the %PDI variable name. Use this mechanism to write main_field at iterations 1 and 2, in two distinct groups iter1 and iter2.

Ex6. Simplifying the code

As you can notice, the %PDI code is quite redundant. In this exercise, you will use ::PDI_expose and ::PDI_multi_expose to simplify the code while keeping the exact same behaviour. For once, there is no need to modify the YAML file here, you only need to modify the C file in this exercise.

• Examine the source code, compile it and run it.

There are lots of matched ::PDI_share/::PDI_reclaim in the code.

• Replace these by ::PDI_expose that is the exact equivalent of a ::PDI_share followed by a matching ::PDI_reclaim.

This replacement is not possible for interlaced ::PDI_share/::PDI_reclaim with events in the middle. This case is however handled by ::PDI_multi_expose call that exposes all data, then triggers an event and finally does all the reclaim in reverse order.

• Replace the remaining ::PDI_share/::PDI_reclaim by ::PDI_exposes and ::PDI_multi_exposes and ensure that your code keeps the exact same behaviour as in previous exercise.

Ex7. Writing a selection

In this exercise, you will only write a selection of the 2D array in memory excluding ghosts to the HDF5 file. Once again, you only need to modify the YAML file in this exercise, no need to touch the C file.

• Examine the YAML file and compile the code.

As you can notice, now the dataset is independently described in the file.

• Restrict the selection to the non-ghost part of the array in memory (excluding one element on each side).

You can achieve this by using the memory_selection directive that specifies the selection of data from memory to write.

Ex8. Selecting on the dataset size

In this exercise, you will once again change the YAML file to handle a selection in the dataset in addition to the selection in memory from the previous exercise. In this exercise, you don't want to have one output file per iteration. You will write the 2D array from the previous exercise as a slice of 3D dataset including a dimension for time. Once again, you only need to modify the YAML file in this exercise, no need to touch the C file.

• Examine the YAML file and compile the code.

Notice how the dataset is now extended with an additional dimension for three time-steps.

• Write the 2D selection from main_field at iterations 1 to 3 inclusive into slices at coordinate 0 to 2 of first dimension of the 3D dataset.

You can achieve this by using the dataset_selection directive that specifies the selection where to write in the file dataset.

parallel Decl'HDF5

Ex9. Going parallel

Running the code from the previous exercises in parallel should already work and yield one file per process containing the local data block. In this exercise you will write one single file with parallel HDF5 whose content should be independent from the number of processes used. Once again, you only need to modify the YAML file in this exercise, no need to touch the C file.

• Examine the YAML file and compile the code.

The mpi plugin was loaded to make sharing MPI communicators possible.

• Uncomment the communicator directive of the \ref Decl_HDF5_plugin "Decl'HDF5 plugin" to switch to parallel I/O and change the file name so that all processes access the same file.

• Set the size of the dataset to take the global (parallel) array size into account. You will need to multiply the local size by the number of processes in each dimension (use psize).

• Ensure the dataset selection of each process does not overlap with the others. You will need to make a selection in the dataset that depends on the global coordinate of the local data block (use pcoord).

Pycall

Ex10. Post-processing the data in python

In this exercise, you will once again modify the YAML file only and use python to post-process the data in situ before writing it to HDF5. Here, you will write the square root of the raw data to HDF5 instead of the data itself.

• Examine the YAML file and compile the code.

Notice that the Decl'HDF5 configuration was simplified, no memory selection is applied, the when condition disappeared. The dataset name is however explicitly specified now because it does not match the %PDI variable name anymore, you will instead write a new variable exposed from python.

The pycall section has been added to load the \ref pycall_plugin "Pycall plugin". It executes the provided code when the "loop" event is triggered. The with section specifies the variables (parameters) to pass to Python as a set of "$-expressions". The provided code again exposes its result to %PDI and multiple blocks can be chained this way.

• Add the missing parameter to the with block to let the Python code process the data exposed in main_field.

• Modify the Decl'HDF5 configuration to write the new data exposed from Python.

\attention In a more realistic setup, one would typically not write much code in the YAML file directly, but would instead call functions specified in a .py file on the side.

What next ?

In this tutorial, you used the C API of %PDI and from YAML, you used the \ref trace_plugin "Trace", \ref Decl_HDF5_plugin "Decl'HDF5", and \ref pycall_plugin "Pycall" plugins.

If you want to try PDI from another language (Fortran, python, ...) or if you want to experiment with other \ref Plugins "PDI plugins", have a look at the examples provided with PDI.