@c -*-texinfo-*-

@c This file is part of the StarPU Handbook.
@c Copyright (C) 2009--2011  Universit@'e de Bordeaux 1
@c Copyright (C) 2010, 2011, 2012, 2013  Centre National de la Recherche Scientifique
@c Copyright (C) 2011 Institut National de Recherche en Informatique et Automatique
@c See the file starpu.texi for copying conditions.

The integration of MPI transfers within task parallelism is done in a
very natural way by the means of asynchronous interactions between the
application and StarPU.  This is implemented in a separate libstarpumpi library
which basically provides "StarPU" equivalents of @code{MPI_*} functions, where
@code{void *} buffers are replaced with @code{starpu_data_handle_t}s, and all
GPU-RAM-NIC transfers are handled efficiently by StarPU-MPI.  The user has to
use the usual @code{mpirun} command of the MPI implementation to start StarPU on
the different MPI nodes.

An MPI Insert Task function provides an even more seamless transition to a
distributed application, by automatically issuing all required data transfers
according to the task graph and an application-provided distribution.

@menu
* Simple Example::
* Point to point communication::
* Exchanging User Defined Data Interface::
* MPI Insert Task Utility::
* MPI Collective Operations::
@end menu

@node Simple Example
@section Simple Example

The flags required to compile or link against the MPI layer are then
accessible with the following commands:

@example
$ pkg-config --cflags starpumpi-1.0  # options for the compiler
$ pkg-config --libs starpumpi-1.0    # options for the linker
@end example

Also pass the @code{--static} option if the application is to be linked statically.

@cartouche
@smallexample
void increment_token(void)
@{
    struct starpu_task *task = starpu_task_create();

    task->cl = &increment_cl;
    task->handles[0] = token_handle;

    starpu_task_submit(task);
@}
@end smallexample
@end cartouche

@cartouche
@smallexample
int main(int argc, char **argv)
@{
    int rank, size;

    starpu_init(NULL);
    starpu_mpi_initialize_extended(&rank, &size);

    starpu_vector_data_register(&token_handle, 0, (uintptr_t)&token, 1, sizeof(unsigned));

    unsigned nloops = NITER;
    unsigned loop;

    unsigned last_loop = nloops - 1;
    unsigned last_rank = size - 1;
@end smallexample
@end cartouche

@cartouche
@smallexample
    for (loop = 0; loop < nloops; loop++) @{
        int tag = loop*size + rank;

        if (loop == 0 && rank == 0)
        @{
            token = 0;
            fprintf(stdout, "Start with token value %d\n", token);
        @}
        else
        @{
            starpu_mpi_irecv_detached(token_handle, (rank+size-1)%size, tag,
                    MPI_COMM_WORLD, NULL, NULL);
        @}

        increment_token();

        if (loop == last_loop && rank == last_rank)
        @{
            starpu_data_acquire(token_handle, STARPU_R);
            fprintf(stdout, "Finished: token value %d\n", token);
            starpu_data_release(token_handle);
        @}
        else
        @{
            starpu_mpi_isend_detached(token_handle, (rank+1)%size, tag+1,
                    MPI_COMM_WORLD, NULL, NULL);
        @}
    @}

    starpu_task_wait_for_all();
@end smallexample
@end cartouche

@cartouche
@smallexample
    starpu_mpi_shutdown();
    starpu_shutdown();

    if (rank == last_rank)
    @{
        fprintf(stderr, "[%d] token = %d == %d * %d ?\n", rank, token, nloops, size);
        STARPU_ASSERT(token == nloops*size);
    @}
@end smallexample
@end cartouche

@node Point to point communication
@section Point to point communication

The standard point to point communications of MPI have been
implemented. The semantic is similar to the MPI one, but adapted to
the DSM provided by StarPU. A MPI request will only be submitted when
the data is available in the main memory of the node submitting the
request.

There is two types of asynchronous communications: the classic
asynchronous communications and the detached communications. The
classic asynchronous communications (@code{starpu_mpi_isend} and
@code{starpu_mpi_irecv}) need to be followed by a call to
@code{starpu_mpi_wait} or to @code{starpu_mpi_test} to wait for or to
test the completion of the communication. Waiting for or testing the
completion of detached communications is not possible, this is done
internally by StarPU-MPI, on completion, the resources are
automatically released. This mechanism is similar to the pthread
detach state attribute which determines whether a thread will be
created in a joinable or a detached state.

For any communication, the call of the function will result in the
creation of a StarPU-MPI request, the function
@code{starpu_data_acquire_cb} is then called to asynchronously request
StarPU to fetch the data in main memory; when the data is available in
main memory, a StarPU-MPI function is called to put the new request in
the list of the ready requests.

The StarPU-MPI progression thread regularly polls this list of ready
requests. For each new ready request, the appropriate function is
called to post the corresponding MPI call. For example, calling
@code{starpu_mpi_isend} will result in posting @code{MPI_Isend}. If
the request is marked as detached, the request will be put in the list
of detached requests.

The StarPU-MPI progression thread also polls the list of detached
requests. For each detached request, it regularly tests the completion
of the MPI request by calling @code{MPI_Test}. On completion, the data
handle is released, and if a callback was defined, it is called.

@ref{Communication} gives the list of all the point to point
communications defined in StarPU-MPI.

@node Exchanging User Defined Data Interface
@section Exchanging User Defined Data Interface

New data interfaces defined as explained in @ref{Defining a New Data
Interface} can also be used within StarPU-MPI and exchanged between
nodes. Two functions needs to be defined through
the type @code{struct starpu_data_interface_ops} (@pxref{Defining
Interface}). The pack function takes a handle and returns a
contiguous memory buffer along with its size where data to be conveyed to another node
should be copied. The reversed operation is implemented in the unpack
function which takes a contiguous memory buffer and recreates the data
handle.

@cartouche
@smallexample
static int complex_pack_data(starpu_data_handle_t handle, unsigned node, void **ptr, ssize_t *count)
@{
  STARPU_ASSERT(starpu_data_test_if_allocated_on_node(handle, node));

  struct starpu_complex_interface *complex_interface =
    (struct starpu_complex_interface *) starpu_data_get_interface_on_node(handle, node);

  *count = complex_get_size(handle);
  *ptr = malloc(*count);
  memcpy(*ptr, complex_interface->real, complex_interface->nx*sizeof(double));
  memcpy(*ptr+complex_interface->nx*sizeof(double), complex_interface->imaginary,
         complex_interface->nx*sizeof(double));

  return 0;
@}
@end smallexample
@end cartouche

@cartouche
@smallexample
static int complex_unpack_data(starpu_data_handle_t handle, unsigned node, void *ptr, size_t count)
@{
  STARPU_ASSERT(starpu_data_test_if_allocated_on_node(handle, node));

  struct starpu_complex_interface *complex_interface =
    (struct starpu_complex_interface *)	starpu_data_get_interface_on_node(handle, node);

  memcpy(complex_interface->real, ptr, complex_interface->nx*sizeof(double));
  memcpy(complex_interface->imaginary, ptr+complex_interface->nx*sizeof(double),
         complex_interface->nx*sizeof(double));

  return 0;
@}
@end smallexample
@end cartouche

@cartouche
@smallexample
static struct starpu_data_interface_ops interface_complex_ops =
@{
  ...
  .pack_data = complex_pack_data,
  .unpack_data = complex_unpack_data
@};
@end smallexample
@end cartouche

@node MPI Insert Task Utility
@section MPI Insert Task Utility

To save the programmer from having to explicit all communications, StarPU
provides an "MPI Insert Task Utility". The principe is that the application
decides a distribution of the data over the MPI nodes by allocating it and
notifying StarPU of that decision, i.e. tell StarPU which MPI node "owns"
which data. It also decides, for each handle, an MPI tag which will be used to
exchange the content of the handle. All MPI nodes then process the whole task
graph, and StarPU automatically determines which node actually execute which
task, and trigger the required MPI transfers.

The list of functions are described in @ref{MPI Insert Task}.

Here an stencil example showing how to use @code{starpu_mpi_insert_task}. One
first needs to define a distribution function which specifies the
locality of the data. Note that that distribution information needs to
be given to StarPU by calling @code{starpu_data_set_rank}. A MPI tag
should also be defined for each data handle by calling
@code{starpu_data_set_tag}.

@cartouche
@smallexample
/* Returns the MPI node number where data is */
int my_distrib(int x, int y, int nb_nodes) @{
  /* Block distrib */
  return ((int)(x / sqrt(nb_nodes) + (y / sqrt(nb_nodes)) * sqrt(nb_nodes))) % nb_nodes;

  // /* Other examples useful for other kinds of computations */
  // /* / distrib */
  // return (x+y) % nb_nodes;

  // /* Block cyclic distrib */
  // unsigned side = sqrt(nb_nodes);
  // return x % side + (y % side) * size;
@}
@end smallexample
@end cartouche

Now the data can be registered within StarPU. Data which are not
owned but will be needed for computations can be registered through
the lazy allocation mechanism, i.e. with a @code{home_node} set to -1.
StarPU will automatically allocate the memory when it is used for the
first time.

One can note an optimization here (the @code{else if} test): we only register
data which will be needed by the tasks that we will execute.

@cartouche
@smallexample
    unsigned matrix[X][Y];
    starpu_data_handle_t data_handles[X][Y];

    for(x = 0; x < X; x++) @{
        for (y = 0; y < Y; y++) @{
            int mpi_rank = my_distrib(x, y, size);
             if (mpi_rank == my_rank)
                /* Owning data */
                starpu_variable_data_register(&data_handles[x][y], 0,
                                              (uintptr_t)&(matrix[x][y]), sizeof(unsigned));
            else if (my_rank == my_distrib(x+1, y, size) || my_rank == my_distrib(x-1, y, size)
                  || my_rank == my_distrib(x, y+1, size) || my_rank == my_distrib(x, y-1, size))
                /* I don't own that index, but will need it for my computations */
                starpu_variable_data_register(&data_handles[x][y], -1,
                                              (uintptr_t)NULL, sizeof(unsigned));
            else
                /* I know it's useless to allocate anything for this */
                data_handles[x][y] = NULL;
            if (data_handles[x][y]) @{
                starpu_data_set_rank(data_handles[x][y], mpi_rank);
                starpu_data_set_tag(data_handles[x][y], x*X+y);
            @}
        @}
    @}
@end smallexample
@end cartouche

Now @code{starpu_mpi_insert_task()} can be called for the different
steps of the application.

@cartouche
@smallexample
    for(loop=0 ; loop<niter; loop++)
        for (x = 1; x < X-1; x++)
            for (y = 1; y < Y-1; y++)
                starpu_mpi_insert_task(MPI_COMM_WORLD, &stencil5_cl,
                                       STARPU_RW, data_handles[x][y],
                                       STARPU_R, data_handles[x-1][y],
                                       STARPU_R, data_handles[x+1][y],
                                       STARPU_R, data_handles[x][y-1],
                                       STARPU_R, data_handles[x][y+1],
                                       0);
    starpu_task_wait_for_all();
@end smallexample
@end cartouche

I.e. all MPI nodes process the whole task graph, but as mentioned above, for
each task, only the MPI node which owns the data being written to (here,
@code{data_handles[x][y]}) will actually run the task. The other MPI nodes will
automatically send the required data.

This can be a concern with a growing number of nodes. To avoid this, the
application can prune the task for loops according to the data distribution,
so as to only submit tasks on nodes which have to care about them (either to
execute them, or to send the required data).

@node MPI Collective Operations
@section MPI Collective Operations

The functions are described in @ref{Collective Operations}.

@cartouche
@smallexample
if (rank == root)
@{
    /* Allocate the vector */
    vector = malloc(nblocks * sizeof(float *));
    for(x=0 ; x<nblocks ; x++)
    @{
        starpu_malloc((void **)&vector[x], block_size*sizeof(float));
    @}
@}

/* Allocate data handles and register data to StarPU */
data_handles = malloc(nblocks*sizeof(starpu_data_handle_t *));
for(x = 0; x < nblocks ;  x++)
@{
    int mpi_rank = my_distrib(x, nodes);
    if (rank == root) @{
        starpu_vector_data_register(&data_handles[x], 0, (uintptr_t)vector[x],
                                    blocks_size, sizeof(float));
    @}
    else if ((mpi_rank == rank) || ((rank == mpi_rank+1 || rank == mpi_rank-1))) @{
        /* I own that index, or i will need it for my computations */
        starpu_vector_data_register(&data_handles[x], -1, (uintptr_t)NULL,
                                   block_size, sizeof(float));
    @}
    else @{
        /* I know it's useless to allocate anything for this */
        data_handles[x] = NULL;
    @}
    if (data_handles[x]) @{
        starpu_data_set_rank(data_handles[x], mpi_rank);
        starpu_data_set_tag(data_handles[x], x*nblocks+y);
    @}
@}

/* Scatter the matrix among the nodes */
starpu_mpi_scatter_detached(data_handles, nblocks, root, MPI_COMM_WORLD);

/* Calculation */
for(x = 0; x < nblocks ;  x++) @{
    if (data_handles[x]) @{
        int owner = starpu_data_get_rank(data_handles[x]);
        if (owner == rank) @{
            starpu_insert_task(&cl, STARPU_RW, data_handles[x], 0);
        @}
    @}
@}

/* Gather the matrix on main node */
starpu_mpi_gather_detached(data_handles, nblocks, 0, MPI_COMM_WORLD);
@end smallexample
@end cartouche