smpirun: make sure that <cluster is on its own line when computing the hostfile autom...

[simgrid.git] / doc / doxygen / module-smpi.doc

/** 
@defgroup SMPI_API      SMPI: Simulate real MPI applications
@brief Programming environment for the simulation of MPI applications

@tableofcontents

SMPI enables the study of MPI application by emulating them on top of
the SimGrid simulator. This is particularly interesting to study
existing MPI applications within the comfort of the simulator. The
SMPI reference article is available at
https://hal.inria.fr/hal-01415484. You should also read the 
<a href="http://simgrid.org/tutorials/simgrid-smpi-101.pdf">SMPI
introductory slides</a>.

Our goal is to enable the study of **unmodified MPI applications**.
Some constructs and features are still missing, but we can probably
add them on demand.  If you already used MPI before, SMPI should sound
very familiar to you: Use smpicc instead of mpicc, and smpirun instead
of mpirun. The main difference is that smpirun takes a virtual
platform as extra parameter (see @ref platform).

If you are new to MPI, you should first take our online [SMPI
CourseWare](https://simgrid.github.io/SMPI_CourseWare/). It consists
in several projects that progressively introduce the MPI concepts. It
proposes to use SimGrid and SMPI to run the experiments, but the
learning objectives are centered on MPI itself. 

For **further scalability**, you may modify your code to speed up your
studies or save memory space.  Maximal **simulation accuracy**
requires some specific care from you.

 - @ref SMPI_use
   - @ref SMPI_use_compile
   - @ref SMPI_use_exec
   - @ref SMPI_use_debug
   - @ref SMPI_use_colls
     - @ref SMPI_use_colls_algos
     - @ref SMPI_use_colls_tracing
 - @ref SMPI_what
   - @ref SMPI_what_coverage
   - @ref SMPI_what_globals
 - @ref SMPI_adapting
   - @ref SMPI_adapting_size
   - @ref SMPI_adapting_speed
 - @ref SMPI_accuracy
 - @ref SMPI_troubleshooting
   - @ref SMPI_trouble_configure_refuses_smpicc
   - @ref SMPI_trouble_configure_dont_find_smpicc
   - @ref SMPI_trouble_useconds_t


@section SMPI_use Using SMPI

@subsection SMPI_use_compile Compiling your code

If your application is in C, then simply use <tt>smpicc</tt> as a
compiler just like you use mpicc with other MPI implementations. This
script still calls your default compiler (gcc, clang, ...) and adds
the right compilation flags along the way. If your application is in
C++, Fortran 77 or Fortran 90, use respectively <tt>smpicxx</tt>,
<tt>smpiff</tt> or <tt>smpif90</tt>.

@subsection SMPI_use_exec Executing your code on the simulator

Use the <tt>smpirun</tt> script as follows for that:

@verbatim
smpirun -hostfile my_hostfile.txt -platform my_platform.xml ./program -blah
@endverbatim

 - <tt>my_hostfile.txt</tt> is a classical MPI hostfile (that is, this
   file lists the machines on which the processes must be dispatched, one
   per line)
 - <tt>my_platform.xml</tt> is a classical SimGrid platform file. Of
   course, the hosts of the hostfile must exist in the provided
   platform.
 - <tt>./program</tt> is the MPI program to simulate, that you
   compiled with <tt>smpicc</tt>
 - <tt>-blah</tt> is a command-line parameter passed to this program.

<tt>smpirun</tt> accepts other parameters, such as <tt>-np</tt> if you
don't want to use all the hosts defined in the hostfile, <tt>-map</tt>
to display on which host each rank gets mapped of <tt>-trace</tt> to
activate the tracing during the simulation. You can get the full list
by running

@verbatim
smpirun -help
@endverbatim

@subsection SMPI_use_debug Debugging your code on top of SMPI

If you want to explore the automatic platform and deployment files
that are generated by @c smpirun, add @c -keep-temps to the command
line.

You can also run your simulation within valgrind or gdb using the
following commands. Once in GDB, each MPI ranks will be represented as
a regular thread, and you can explore the state of each of them as
usual.
@verbatim
smpirun -wrapper valgrind ...other args...
smpirun -wrapper "gdb --args" --cfg=contexts/factory:thread ...other args...
@endverbatim

@subsection SMPI_use_colls Simulating collective operations

MPI collective operations are crucial to the performance of MPI
applications and must be carefully optimized according to many
parameters. Every existing implementation provides several algorithms
for each collective operation, and selects by default the best suited
one, depending on the sizes sent, the number of nodes, the
communicator, or the communication library being used.  These
decisions are based on empirical results and theoretical complexity
estimation, and are very different between MPI implementations. In
most cases, the users can also manually tune the algorithm used for
each collective operation.

SMPI can simulate the behavior of several MPI implementations:
OpenMPI, MPICH,
<a href="http://star-mpi.sourceforge.net/">STAR-MPI</a>, and
MVAPICH2. For that, it provides 115 collective algorithms and several
selector algorithms, that were collected directly in the source code
of the targeted MPI implementations.

You can switch the automatic selector through the
\c smpi/coll-selector configuration item. Possible values:

 - <b>ompi</b>: default selection logic of OpenMPI (version 3.1.2)
 - <b>mpich</b>: default selection logic of MPICH (version 3.3b)
 - <b>mvapich2</b>: selection logic of MVAPICH2 (version 1.9) tuned
   on the Stampede cluster   
 - <b>impi</b>: preliminary version of an Intel MPI selector (version
   4.1.3, also tuned for the Stampede cluster). Due the closed source
   nature of Intel MPI, some of the algorithms described in the
   documentation are not available, and are replaced by mvapich ones.   
 - <b>default</b>: legacy algorithms used in the earlier days of
   SimGrid. Do not use for serious perform performance studies.


@subsubsection SMPI_use_colls_algos Available algorithms

You can also pick the algorithm used for each collective with the
corresponding configuration item. For example, to use the pairwise
alltoall algorithm, one should add \c --cfg=smpi/alltoall:pair to the
line. This will override the selector (if any) for this algorithm.
It means that the selected algorithm will be used 

Warning: Some collective may require specific conditions to be
executed correctly (for instance having a communicator with a power of
two number of nodes only), which are currently not enforced by
Simgrid. Some crashes can be expected while trying these algorithms
with unusual sizes/parameters

#### MPI_Alltoall

Most of these are best described in <a href="http://www.cs.arizona.edu/~dkl/research/papers/ics06.pdf">STAR-MPI</a>

 - default: naive one, by default
 - ompi: use openmpi selector for the alltoall operations
 - mpich: use mpich selector for the alltoall operations
 - mvapich2: use mvapich2 selector for the alltoall operations
 - impi: use intel mpi selector for the alltoall operations
 - automatic (experimental): use an automatic self-benchmarking algorithm 
 - bruck: Described by Bruck et.al. in <a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=642949">this paper</a>
 - 2dmesh: organizes the nodes as a two dimensional mesh, and perform allgather 
   along the dimensions
 - 3dmesh: adds a third dimension to the previous algorithm
 - rdb: recursive doubling: extends the mesh to a nth dimension, each one 
   containing two nodes
 - pair: pairwise exchange, only works for power of 2 procs, size-1 steps,
   each process sends and receives from the same process at each step
 - pair_light_barrier: same, with small barriers between steps to avoid
   contention
 - pair_mpi_barrier: same, with MPI_Barrier used
 - pair_one_barrier: only one barrier at the beginning
 - ring: size-1 steps, at each step a process send to process (n+i)%size, and receives from (n-i)%size
 - ring_light_barrier: same, with small barriers between some phases to avoid contention
 - ring_mpi_barrier: same, with MPI_Barrier used
 - ring_one_barrier: only one barrier at the beginning
 - basic_linear: posts all receives and all sends,
starts the communications, and waits for all communication to finish
 - mvapich2_scatter_dest: isend/irecv with scattered destinations, posting only a few messages at the same time

#### MPI_Alltoallv

 - default: naive one, by default
 - ompi: use openmpi selector for the alltoallv operations
 - mpich: use mpich selector for the alltoallv operations
 - mvapich2: use mvapich2 selector for the alltoallv operations
 - impi: use intel mpi selector for the alltoallv operations
 - automatic (experimental): use an automatic self-benchmarking algorithm 
 - bruck: same as alltoall
 - pair: same as alltoall
 - pair_light_barrier: same as alltoall
 - pair_mpi_barrier: same as alltoall
 - pair_one_barrier: same as alltoall
 - ring: same as alltoall
 - ring_light_barrier: same as alltoall
 - ring_mpi_barrier: same as alltoall
 - ring_one_barrier: same as alltoall
 - ompi_basic_linear: same as alltoall

#### MPI_Gather

 - default: naive one, by default
 - ompi: use openmpi selector for the gather operations
 - mpich: use mpich selector for the gather operations
 - mvapich2: use mvapich2 selector for the gather operations
 - impi: use intel mpi selector for the gather operations
 - automatic (experimental): use an automatic self-benchmarking algorithm 
which will iterate over all implemented versions and output the best
 - ompi_basic_linear: basic linear algorithm from openmpi, each process sends to the root
 - ompi_binomial: binomial tree algorithm
 - ompi_linear_sync: same as basic linear, but with a synchronization at the
 beginning and message cut into two segments.
 - mvapich2_two_level: SMP-aware version from MVAPICH. Gather first intra-node (defaults to mpich's gather), and then exchange with only one process/node. Use mvapich2 selector to change these to tuned algorithms for Stampede cluster.

#### MPI_Barrier

 - default: naive one, by default
 - ompi: use openmpi selector for the barrier operations
 - mpich: use mpich selector for the barrier operations
 - mvapich2: use mvapich2 selector for the barrier operations
 - impi: use intel mpi selector for the barrier operations
 - automatic (experimental): use an automatic self-benchmarking algorithm 
 - ompi_basic_linear: all processes send to root
 - ompi_two_procs: special case for two processes
 - ompi_bruck: nsteps = sqrt(size), at each step, exchange data with rank-2^k and rank+2^k
 - ompi_recursivedoubling: recursive doubling algorithm
 - ompi_tree: recursive doubling type algorithm, with tree structure
 - ompi_doublering: double ring algorithm
 - mvapich2_pair: pairwise algorithm
 - mpich_smp: barrier intra-node, then inter-node

#### MPI_Scatter

 - default: naive one, by default
 - ompi: use openmpi selector for the scatter operations
 - mpich: use mpich selector for the scatter operations
 - mvapich2: use mvapich2 selector for the scatter operations
 - impi: use intel mpi selector for the scatter operations
 - automatic (experimental): use an automatic self-benchmarking algorithm 
 - ompi_basic_linear: basic linear scatter 
 - ompi_binomial: binomial tree scatter
 - mvapich2_two_level_direct: SMP aware algorithm, with an intra-node stage (default set to mpich selector), and then a basic linear inter node stage. Use mvapich2 selector to change these to tuned algorithms for Stampede cluster. 
 - mvapich2_two_level_binomial: SMP aware algorithm, with an intra-node stage (default set to mpich selector), and then a binomial phase. Use mvapich2 selector to change these to tuned algorithms for Stampede cluster.

#### MPI_Reduce

 - default: naive one, by default
 - ompi: use openmpi selector for the reduce operations
 - mpich: use mpich selector for the reduce operations
 - mvapich2: use mvapich2 selector for the reduce operations
 - impi: use intel mpi selector for the reduce operations
 - automatic (experimental): use an automatic self-benchmarking algorithm 
 - arrival_pattern_aware: root exchanges with the first process to arrive
 - binomial: uses a binomial tree
 - flat_tree: uses a flat tree
 - NTSL: Non-topology-specific pipelined linear-bcast function 
   0->1, 1->2 ,2->3, ....., ->last node: in a pipeline fashion, with segments
 of 8192 bytes
 - scatter_gather: scatter then gather
 - ompi_chain: openmpi reduce algorithms are built on the same basis, but the
 topology is generated differently for each flavor
chain = chain with spacing of size/2, and segment size of 64KB 
 - ompi_pipeline: same with pipeline (chain with spacing of 1), segment size 
depends on the communicator size and the message size
 - ompi_binary: same with binary tree, segment size of 32KB
 - ompi_in_order_binary: same with binary tree, enforcing order on the 
operations
 - ompi_binomial: same with binomial algo (redundant with default binomial 
one in most cases)
 - ompi_basic_linear: basic algorithm, each process sends to root
 - mvapich2_knomial: k-nomial algorithm. Default factor is 4 (mvapich2 selector adapts it through tuning)
 - mvapich2_two_level: SMP-aware reduce, with default set to mpich both for intra and inter communicators. Use mvapich2 selector to change these to tuned algorithms for Stampede cluster.
 - rab: <a href="https://fs.hlrs.de/projects/par/mpi//myreduce.html">Rabenseifner</a>'s reduce algorithm 

#### MPI_Allreduce

 - default: naive one, by default
 - ompi: use openmpi selector for the allreduce operations
 - mpich: use mpich selector for the allreduce operations
 - mvapich2: use mvapich2 selector for the allreduce operations
 - impi: use intel mpi selector for the allreduce operations
 - automatic (experimental): use an automatic self-benchmarking algorithm 
 - lr: logical ring reduce-scatter then logical ring allgather
 - rab1: variations of the  <a href="https://fs.hlrs.de/projects/par/mpi//myreduce.html">Rabenseifner</a> algorithm: reduce_scatter then allgather
 - rab2: variations of the  <a href="https://fs.hlrs.de/projects/par/mpi//myreduce.html">Rabenseifner</a> algorithm: alltoall then allgather
 - rab_rsag: variation of the  <a href="https://fs.hlrs.de/projects/par/mpi//myreduce.html">Rabenseifner</a> algorithm: recursive doubling 
reduce_scatter then recursive doubling allgather 
 - rdb: recursive doubling
 - smp_binomial: binomial tree with smp: binomial intra 
SMP reduce, inter reduce, inter broadcast then intra broadcast
 - smp_binomial_pipeline: same with segment size = 4096 bytes
 - smp_rdb: intra: binomial allreduce, inter: Recursive 
doubling allreduce, intra: binomial broadcast
 - smp_rsag: intra: binomial allreduce, inter: reduce-scatter, 
inter:allgather, intra: binomial broadcast
 - smp_rsag_lr: intra: binomial allreduce, inter: logical ring 
reduce-scatter, logical ring inter:allgather, intra: binomial broadcast
 - smp_rsag_rab: intra: binomial allreduce, inter: rab
reduce-scatter, rab inter:allgather, intra: binomial broadcast
 - redbcast: reduce then broadcast, using default or tuned algorithms if specified
 - ompi_ring_segmented: ring algorithm used by OpenMPI
 - mvapich2_rs: rdb for small messages, reduce-scatter then allgather else
 - mvapich2_two_level: SMP-aware algorithm, with mpich as intra algoritm, and rdb as inter (Change this behavior by using mvapich2 selector to use tuned values)
 - rab: default <a href="https://fs.hlrs.de/projects/par/mpi//myreduce.html">Rabenseifner</a> implementation

#### MPI_Reduce_scatter

 - default: naive one, by default
 - ompi: use openmpi selector for the reduce_scatter operations
 - mpich: use mpich selector for the reduce_scatter operations
 - mvapich2: use mvapich2 selector for the reduce_scatter operations
 - impi: use intel mpi selector for the reduce_scatter operations
 - automatic (experimental): use an automatic self-benchmarking algorithm 
 - ompi_basic_recursivehalving: recursive halving version from OpenMPI
 - ompi_ring: ring version from OpenMPI
 - mpich_pair: pairwise exchange version from MPICH
 - mpich_rdb: recursive doubling version from MPICH
 - mpich_noncomm: only works for power of 2 procs, recursive doubling for noncommutative ops


#### MPI_Allgather

 - default: naive one, by default
 - ompi: use openmpi selector for the allgather operations
 - mpich: use mpich selector for the allgather operations
 - mvapich2: use mvapich2 selector for the allgather operations
 - impi: use intel mpi selector for the allgather operations
 - automatic (experimental): use an automatic self-benchmarking algorithm 
 - 2dmesh: see alltoall
 - 3dmesh: see alltoall
 - bruck: Described by Bruck et.al. in <a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=642949">
Efficient algorithms for all-to-all communications in multiport message-passing systems</a> 
 - GB: Gather - Broadcast (uses tuned version if specified)
 - loosely_lr: Logical Ring with grouping by core (hardcoded, default 
processes/node: 4)
 - NTSLR: Non Topology Specific Logical Ring
 - NTSLR_NB: Non Topology Specific Logical Ring, Non Blocking operations
 - pair: see alltoall
 - rdb: see alltoall
 - rhv: only power of 2 number of processes
 - ring: see alltoall
 - SMP_NTS: gather to root of each SMP, then every root of each SMP node 
post INTER-SMP Sendrecv, then do INTRA-SMP Bcast for each receiving message, 
using logical ring algorithm (hardcoded, default processes/SMP: 8)
 - smp_simple: gather to root of each SMP, then every root of each SMP node 
post INTER-SMP Sendrecv, then do INTRA-SMP Bcast for each receiving message, 
using simple algorithm (hardcoded, default processes/SMP: 8)
 - spreading_simple: from node i, order of communications is i -> i + 1, i ->
 i + 2, ..., i -> (i + p -1) % P
 - ompi_neighborexchange: Neighbor Exchange algorithm for allgather. 
Described by Chen et.al. in  <a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=1592302">Performance Evaluation of Allgather Algorithms on Terascale Linux Cluster with Fast Ethernet</a>
 - mvapich2_smp: SMP aware algorithm, performing intra-node gather, inter-node allgather with one process/node, and bcast intra-node


#### MPI_Allgatherv

 - default: naive one, by default
 - ompi: use openmpi selector for the allgatherv operations
 - mpich: use mpich selector for the allgatherv operations
 - mvapich2: use mvapich2 selector for the allgatherv operations
 - impi: use intel mpi selector for the allgatherv operations
 - automatic (experimental): use an automatic self-benchmarking algorithm 
 - GB: Gatherv - Broadcast (uses tuned version if specified, but only for 
Bcast, gatherv is not tuned)
 - pair: see alltoall
 - ring: see alltoall
 - ompi_neighborexchange: see allgather
 - ompi_bruck: see allgather
 - mpich_rdb: recursive doubling algorithm from MPICH
 - mpich_ring: ring algorithm from MPICh - performs differently from the  one from STAR-MPI

#### MPI_Bcast

 - default: naive one, by default
 - ompi: use openmpi selector for the bcast operations
 - mpich: use mpich selector for the bcast operations
 - mvapich2: use mvapich2 selector for the bcast operations
 - impi: use intel mpi selector for the bcast operations
 - automatic (experimental): use an automatic self-benchmarking algorithm 
 - arrival_pattern_aware: root exchanges with the first process to arrive
 - arrival_pattern_aware_wait: same with slight variation
 - binomial_tree: binomial tree exchange
 - flattree: flat tree exchange
 - flattree_pipeline: flat tree exchange, message split into 8192 bytes pieces
 - NTSB: Non-topology-specific pipelined binary tree with 8192 bytes pieces
 - NTSL: Non-topology-specific pipelined linear with 8192 bytes pieces
 - NTSL_Isend: Non-topology-specific pipelined linear with 8192 bytes pieces, asynchronous communications
 - scatter_LR_allgather: scatter followed by logical ring allgather
 - scatter_rdb_allgather: scatter followed by recursive doubling allgather
 - arrival_scatter: arrival pattern aware scatter-allgather
 - SMP_binary: binary tree algorithm with 8 cores/SMP
 - SMP_binomial: binomial tree algorithm with 8 cores/SMP
 - SMP_linear: linear algorithm with 8 cores/SMP
 - ompi_split_bintree: binary tree algorithm from OpenMPI, with message split in 8192 bytes pieces
 - ompi_pipeline: pipeline algorithm from OpenMPI, with message split in 128KB pieces
 - mvapich2_inter_node: Inter node default mvapich worker 
 - mvapich2_intra_node: Intra node default mvapich worker
 - mvapich2_knomial_intra_node:  k-nomial intra node default mvapich worker. default factor is 4.

#### Automatic evaluation 

(Warning: This is still very experimental)

An automatic version is available for each collective (or even as a selector). This specific 
version will loop over all other implemented algorithm for this particular collective, and apply 
them while benchmarking the time taken for each process. It will then output the quickest for 
each process, and the global quickest. This is still unstable, and a few algorithms which need 
specific number of nodes may crash.

#### Adding an algorithm

To add a new algorithm, one should check in the src/smpi/colls folder how other algorithms 
are coded. Using plain MPI code inside Simgrid can't be done, so algorithms have to be 
changed to use smpi version of the calls instead (MPI_Send will become smpi_mpi_send). Some functions may have different signatures than their MPI counterpart, please check the other algorithms or contact us using <a href="http://lists.gforge.inria.fr/mailman/listinfo/simgrid-devel">SimGrid developers mailing list</a>.

Example: adding a "pair" version of the Alltoall collective.

 - Implement it in a file called alltoall-pair.c in the src/smpi/colls folder. This file should include colls_private.hpp.

 - The name of the new algorithm function should be smpi_coll_tuned_alltoall_pair, with the same signature as MPI_Alltoall.

 - Once the adaptation to SMPI code is done, add a reference to the file ("src/smpi/colls/alltoall-pair.c") in the SMPI_SRC part of the DefinePackages.cmake file inside buildtools/cmake, to allow the file to be built and distributed.

 - To register the new version of the algorithm, simply add a line to the corresponding macro in src/smpi/colls/cools.h ( add a "COLL_APPLY(action, COLL_ALLTOALL_SIG, pair)" to the COLL_ALLTOALLS macro ). The algorithm should now be compiled and be selected when using --cfg=smpi/alltoall:pair at runtime.

 - To add a test for the algorithm inside Simgrid's test suite, juste add the new algorithm name in the ALLTOALL_COLL list found inside teshsuite/smpi/CMakeLists.txt . When running ctest, a test for the new algorithm should be generated and executed. If it does not pass, please check your code or contact us.

 - Please submit your patch for inclusion in SMPI, for example through a pull request on GitHub or directly per email.

@subsubsection SMPI_use_colls_tracing Tracing of internal communications

By default, the collective operations are traced as a unique operation
because tracing all point-to-point communications composing them could
result in overloaded, hard to interpret traces. If you want to debug
and compare collective algorithms, you should set the
\c tracing/smpi/internals configuration item to 1 instead of 0.

Here are examples of two alltoall collective algorithms runs on 16 nodes, 
the first one with a ring algorithm, the second with a pairwise one:

@htmlonly
<a href="smpi_simgrid_alltoall_ring_16.png" border=0><img src="smpi_simgrid_alltoall_ring_16.png" width="30%" border=0 align="center"></a>
<a href="smpi_simgrid_alltoall_pair_16.png" border=0><img src="smpi_simgrid_alltoall_pair_16.png" width="30%" border=0 align="center"></a>
<br/>
@endhtmlonly

@section SMPI_what What can run within SMPI?

You can run unmodified MPI applications (both C/C++ and Fortran) within
SMPI, provided that you only use MPI calls that we implemented. Global
variables should be handled correctly on Linux systems.

@subsection SMPI_what_coverage MPI coverage of SMPI

Our coverage of the interface is very decent, but still incomplete;
Given the size of the MPI standard, we may well never manage to 
implement absolutely all existing primitives. Currently, we have
almost no support for I/O primitives, but we still pass a very large
amount of the MPICH coverage tests.

The full list of not yet implemented functions is documented in the
file @ref include/smpi/smpi.h, between two lines containing the
<tt>FIXME</tt> marker. If you really miss a feature, please get in
touch with us: we can guide you though the SimGrid code to help you
implementing it, and we'd glad to integrate your contribution to the
main project afterward.

@subsection SMPI_what_globals Privatization of global variables

Concerning the globals, the problem comes from the fact that usually,
MPI processes run as real UNIX processes while they are all folded
into threads of a unique system process in SMPI. Global variables are
usually private to each MPI process while they become shared between
the processes in SMPI.  The problem and some potential solutions are
discussed in this article: "Automatic Handling of Global Variables for
Multi-threaded MPI Programs", available at
http://charm.cs.illinois.edu/newPapers/11-23/paper.pdf (note that this
article does not deal with SMPI but with a competing solution called
AMPI that suffers of the same issue).  This point used to be
problematic in SimGrid, but the problem should now be handled
automatically on Linux.

Older versions of SimGrid came with a script that automatically
privatized the globals through static analysis of the source code. But
our implementation was not robust enough to be used in production, so
it was removed at some point. Currently, SMPI comes with two
privatization mechanisms that you can @ref options_smpi_privatization
"select at runtime". At the time of writing (v3.18), the dlopen
approach is considered to be very fast (it's used by default) while
the mmap approach is considered to be rather slow but very robust.

With the <b>mmap approach</b>, SMPI duplicates and dynamically switch
the \c .data and \c .bss segments of the ELF process when switching
the MPI ranks. This allows each ranks to have its own copy of the
global variables.  No copy actually occures as this mechanism uses \c
mmap for efficiency. This mechanism is considered to be very robust on
all systems supporting \c mmap (Linux and most BSDs). Its performance
is questionable since each context switch between MPI ranks induces
several syscalls to change the \c mmap that redirects the \c .data and
\c .bss segments to the copies of the new rank. The code will also be
copied several times in memory, inducing a slight increase of memory
occupation.

Another limitation is that SMPI only accounts for global variables
defined in the executable. If the processes use external global
variables from dynamic libraries, they won't be switched
correctly. The easiest way to solve this is to statically link against
the library with these globals. This way, each MPI rank will get its
own copy of these libraries. Of course you should never statically
link against the SimGrid library itself.

With the <b>dlopen approach</b>, SMPI loads several copies of the same
executable in memory as if it were a library, so that the global
variables get naturally duplicated. It first requires the executable
to be compiled as a relocatable binary, which is less common for
programs than for libraries. But most distributions are now compiled
this way for security reason as it allows to randomize the address
space layout. It should thus be safe to compile most (any?) program
this way.  The second trick is that the dynamic linker refuses to link
the exact same file several times, be it a library or a relocatable
executable. It makes perfectly sense in the general case, but we need
to circumvent this rule of thumb in our case. To that extend, the
binary is copied in a temporary file before being re-linked against.
`dlmopen()` cannot be used as it only allows 256 contextes, and as it
would also dupplicate simgrid itself.

This approach greatly speeds up the context switching, down to about
40 CPU cycles with our raw contextes, instead of requesting several
syscalls with the \c mmap approach. Another advantage is that it
permits to run the SMPI contexts in parallel, which is obviously not
possible with the \c mmap approach. It was tricky to implement, but we
are not aware of any flaws, so smpirun activates it by default.

In the future, it may be possible to further reduce the memory and
disk consumption. It seems that we could <a
href="https://lwn.net/Articles/415889/">punch holes</a> in the files
before dl-loading them to remove the code and constants, and mmap
these area onto a unique copy. If done correctly, this would reduce
the disk- and memory- usage to the bare minimum, and would also reduce
the pressure on the CPU instruction cache. See 
<a href="https://github.com/simgrid/simgrid/issues/137">the relevant
bug</a> on github for implementation leads.\n

Also, currently, only the binary is copied and dlopen-ed for each MPI
rank. We could probably extend this to external dependencies, but for
now, any external dependencies must be statically linked into your
application. As usual, simgrid itself shall never be statically linked
in your app. You don't want to give a copy of SimGrid to each MPI rank:
that's ways too much for them to deal with.

@section SMPI_adapting Adapting your MPI code for further scalability

As detailed in the reference article (available at
http://hal.inria.fr/hal-01415484), you may want to adapt your code
to improve the simulation performance. But these tricks may seriously
hinder the result quality (or even prevent the app to run) if used
wrongly. We assume that if you want to simulate an HPC application,
you know what you are doing. Don't prove us wrong!

@subsection SMPI_adapting_size Reducing your memory footprint

If you get short on memory (the whole app is executed on a single node when
simulated), you should have a look at the SMPI_SHARED_MALLOC and
SMPI_SHARED_FREE macros. It allows to share memory areas between processes: The
purpose of these macro is that the same line malloc on each process will point
to the exact same memory area. So if you have a malloc of 2M and you have 16
processes, this macro will change your memory consumption from 2M*16 to 2M
only. Only one block for all processes.

If your program is ok with a block containing garbage value because all
processes write and read to the same place without any kind of coordination,
then this macro can dramatically shrink your memory consumption. For example,
that will be very beneficial to a matrix multiplication code, as all blocks will
be stored on the same area. Of course, the resulting computations will useless,
but you can still study the application behavior this way. 

Naturally, this won't work if your code is data-dependent. For example, a Jacobi
iterative computation depends on the result computed by the code to detect
convergence conditions, so turning them into garbage by sharing the same memory
area between processes does not seem very wise. You cannot use the
SMPI_SHARED_MALLOC macro in this case, sorry.

This feature is demoed by the example file
<tt>examples/smpi/NAS/dt.c</tt>

@subsection SMPI_adapting_speed Toward faster simulations

If your application is too slow, try using SMPI_SAMPLE_LOCAL,
SMPI_SAMPLE_GLOBAL and friends to indicate which computation loops can
be sampled. Some of the loop iterations will be executed to measure
their duration, and this duration will be used for the subsequent
iterations. These samples are done per processor with
SMPI_SAMPLE_LOCAL, and shared between all processors with
SMPI_SAMPLE_GLOBAL. Of course, none of this will work if the execution
time of your loop iteration are not stable.

This feature is demoed by the example file 
<tt>examples/smpi/NAS/ep.c</tt>

@section SMPI_accuracy Ensuring accurate simulations

Out of the box, SimGrid may give you fairly accurate results, but
there is a plenty of factors that could go wrong and make your results
inaccurate or even plainly wrong. Actually, you can only get accurate
results of a nicely built model, including both the system hardware
and your application. Such models are hard to pass over and reuse in
other settings, because elements that are not relevant to an
application (say, the latency of point-to-point communications,
collective operation implementation details or CPU-network
interaction) may be irrelevant to another application. The dream of
the perfect model, encompassing every aspects is only a chimera, as
the only perfect model of the reality is the reality. If you go for
simulation, then you have to ignore some irrelevant aspects of the
reality, but which aspects are irrelevant is actually
application-dependent...

The only way to assess whether your settings provide accurate results
is to double-check these results. If possible, you should first run
the same experiment in simulation and in real life, gathering as much
information as you can. Try to understand the discrepancies in the
results that you observe between both settings (visualization can be
precious for that). Then, try to modify your model (of the platform,
of the collective operations) to reduce the most preeminent differences.

If the discrepancies come from the computing time, try adapting the \c
smpi/host-speed: reduce it if your simulation runs faster than in
reality. If the error come from the communication, then you need to
fiddle with your platform file.

Be inventive in your modeling. Don't be afraid if the names given by
SimGrid does not match the real names: we got very good results by
modeling multicore/GPU machines with a set of separate hosts
interconnected with very fast networks (but don't trust your model
because it has the right names in the right place either).

Finally, you may want to check [this
article](https://hal.inria.fr/hal-00907887) on the classical pitfalls
in modeling distributed systems.

@section SMPI_troubleshooting Troubleshooting with SMPI

@subsection SMPI_trouble_configure_refuses_smpicc ./configure refuses to use smpicc

If your <tt>./configure</tt> reports that the compiler is not
functional or that you are cross-compiling, try to define the
<tt>SMPI_PRETEND_CC</tt> environment variable before running the
configuration.

@verbatim
SMPI_PRETEND_CC=1 ./configure # here come the configure parameters
make
@endverbatim

Indeed, the programs compiled with <tt>smpicc</tt> cannot be executed
without <tt>smpirun</tt> (they are shared libraries, and they do weird
things on startup), while configure wants to test them directly.
With <tt>SMPI_PRETEND_CC</tt> smpicc does not compile as shared,
and the SMPI initialization stops and returns 0 before doing anything
that would fail without <tt>smpirun</tt>.

\warning 

  Make sure that SMPI_PRETEND_CC is only set when calling ./configure,
  not during the actual execution, or any program compiled with smpicc
  will stop before starting.

@subsection SMPI_trouble_configure_dont_find_smpicc ./configure does not pick smpicc as a compiler

In addition to the previous answers, some projects also need to be
explicitely told what compiler to use, as follows:

@verbatim
SMPI_PRETEND_CC=1 ./configure CC=smpicc # here come the other configure parameters
make
@endverbatim

Maybe your configure is using another variable, such as <tt>cc</tt> or
similar. Just check the logs.

@subsection SMPI_trouble_useconds_t  error: unknown type name 'useconds_t'

Try to add <tt>-D_GNU_SOURCE</tt> to your compilation line to get ride
of that error.

The reason is that SMPI provides its own version of <tt>usleep(3)</tt>
to override it and to block in the simulation world, not in the real
one. It needs the <tt>useconds_t</tt> type for that, which is declared
only if you declare <tt>_GNU_SOURCE</tt> before including
<tt>unistd.h</tt>. If your project includes that header file before
SMPI, then you need to ensure that you pass the right configuration
defines as advised above.


*/


/** @example include/smpi/smpi.h */