+Lab 0: Hello World
+------------------
+
+It is time to simulate your first MPI program. Use the simplistic
+example `roundtrip.c
+<https://framagit.org/simgrid/simgrid-template-smpi/raw/master/roundtrip.c?inline=false>`_
+that comes with the template.
+
+.. literalinclude:: /tuto_smpi/roundtrip.c
+ :language: c
+
+Compiling and Executing
+.......................
+
+Compiling the program is straightforward (double check your
+:ref:`SimGrid installation <install>` if you get an error message):
+
+
+.. code-block:: shell
+
+ $ smpicc -O3 roundtrip.c -o roundtrip
+
+
+Once compiled, you can simulate the execution of this program on 16
+nodes from the ``cluster_crossbar.xml`` platform as follows:
+
+.. code-block:: shell
+
+ $ smpirun -np 16 -platform cluster_crossbar.xml -hostfile cluster_hostfile ./roundtrip
+
+- The ``-np 16`` option, just like in regular MPI, specifies the
+ number of MPI processes to use.
+- The ``-hostfile cluster_hostfile`` option, just like in regular
+ MPI, specifies the host file. If you omit this option, ``smpirun``
+ will deploy the application on the first machines of your platform.
+- The ``-platform cluster_crossbar.xml`` option, **which doesn't exist
+ in regular MPI**, specifies the platform configuration to be
+ simulated.
+- At the end of the line, one finds the executable name and
+ command-line arguments (if any -- roundtrip does not expect any arguments).
+
+Feel free to tweak the content of the XML platform file and the
+prorgam to see the effect on the simulated execution time. Note that
+the simulation accounts for realistic network protocol effects and MPI
+implementation effects. As a result, you may see "unexpected behavior"
+like in the real world (e.g., sending a message 1 byte larger may lead
+to significant higher execution time).
+
+Lab 1: Visualizing LU
+---------------------
+
+We will now simulate a larger application: the LU benchmark of the NAS
+suite. The version provided in the code template was modified to
+compile with SMPI instead of the regular MPI. Compare the difference
+between the original ``config/make.def.template`` and the
+``config/make.def`` that was adapted to SMPI. We use ``smpiff`` and
+``smpicc`` as compilers, and don't pass any additional library.
+
+Now compile and execute the LU benchmark, class A (i.e., for small
+data size) with 4 nodes.
+
+.. code-block:: shell
+
+ $ make lu NPROCS=4 CLASS=A
+ (compilation logs)
+ $ smpirun -np 4 -platform ../cluster_backbone.xml bin/lu.A.4
+ (execution logs)
+
+To get a better understanding of what is going on, activate the
+vizualization tracing, and convert the produced trace for later
+use:
+
+.. code-block:: shell
+
+ smpirun -np 4 -platform ../cluster_backbone.xml -trace --cfg=tracing/filename:lu.A.4.trace bin/lu.A.4
+ pj_dump --ignore-incomplete-links lu.A.4.trace | grep State > lu.A.4.state.csv
+
+You can then produce a Gantt Chart with the following R chunk. You can
+either copy/paste it in a R session, or `turn it into a Rscript executable
+<https://swcarpentry.github.io/r-novice-inflammation/05-cmdline/>`_ to
+run it again and again.
+
+.. code-block:: R
+
+ library(ggplot2)
+
+ # Read the data
+ df_state = read.csv("lu.A.4.state.csv", header=F, strip.white=T)
+ names(df_state) = c("Type", "Rank", "Container", "Start", "End", "Duration", "Level", "State");
+ df_state = df_state[!(names(df_state) %in% c("Type","Container","Level"))]
+ df_state$Rank = as.numeric(gsub("rank-","",df_state$Rank))
+
+ # Draw the Gantt Chart
+ gc = ggplot(data=df_state) + geom_rect(aes(xmin=Start, xmax=End, ymin=Rank, ymax=Rank+1,fill=State))
+
+ # Produce the output
+ plot(gc)
+ dev.off()
+
+This produces a file called ``Rplots.pdf`` with the following
+content. You can find more examples of visualization in the `SimGrid
+documentation <http://simgrid.gforge.inria.fr/contrib/R_visualization.html>`_.
+
+.. image:: /tuto_smpi/img/lu.A.4.png
+ :align: center
+
+Lab 2: Tracing and Replay of LU
+-------------------------------
+
+Now compile and execute the LU benchmark, class A, with 32 nodes.
+
+.. code-block:: shell
+
+ $ make lu NPROCS=32 CLASS=A
+
+This takes several minutes to to simulate, because all code from all
+processes has to be really executed, and everything is serialized.
+
+SMPI provides several methods to speed things up. One of them is to
+capture a time independent trace of the running application, and
+replay it on a different platform with the same amount of nodes. The
+replay is much faster than live simulation, as the computations are
+skipped (the application must be network-dependent for this to work).
+
+You can even generate the trace during as live simulation, as follows:
+
+.. code-block:: shell
+
+ $ smpirun -trace-ti --cfg=tracing/filename:LU.A.32 -np 32 -platform ../cluster_backbone.xml bin/lu.A.32
+
+The produced trace is composed of a file ``LU.A.32`` and a folder
+``LU.A.32_files``. To replay this with SMPI, you need to first compile
+the provided ``smpi_replay.cpp`` file, that comes from
+`simgrid/examples/smpi/replay
+<https://framagit.org/simgrid/simgrid/tree/master/examples/smpi/replay>`_.
+
+.. code-block:: shell
+
+ $ smpicxx ../replay.cpp -O3 -o ../smpi_replay
+
+Afterward, you can replay your trace in SMPI as follows:
+
+ $ smpirun -np 32 -platform ../cluster_torus.xml -ext smpi_replay ../smpi_replay LU.A.32
+
+All the outputs are gone, as the application is not really simulated
+here. Its trace is simply replayed. But if you visualize the live
+simulation and the replay, you will see that the behavior is
+unchanged. The simulation does not run much faster on this very
+example, but this becomes very interesting when your application
+is computationally hungry.
+
+.. todo:: smpi_replay should be installed by SimGrid, and smpirun interface could be simplified here.
