#include <boost/mpi.hpp>
#include <iostream>
#include <string>
#include <boost/serialization/string.hpp>
namespace mpi = boost::mpi;

int main()
{
  mpi::environment env;
  mpi::communicator world;

  if (world.rank() == 0) {
    world.send(1, 0, std::string("Hello"));
    std::string msg;
    world.recv(1, 1, msg);
    std::cout << msg << "!" << std::endl;
  } else {
    std::string msg;
    world.recv(0, 0, msg);
    std::cout << msg << ", ";
    std::cout.flush();
    world.send(0, 1, std::string("world"));
  }

  return 0;
}

The default MPI communication operations--send and recv--may have to wait until the entire transmission is completed before they can return. Sometimes this blocking behavior has a negative impact on performance, because the sender could be performing useful computation while it is waiting for the transmission to occur. More important, however, are the cases where several communication operations must occur simultaneously, e.g., a process will both send and receive at the same time.

Let's revisit our "Hello, world!" program from the previous section. The core of this program transmits two messages:

if (world.rank() == 0) {
  world.send(1, 0, std::string("Hello"));
  std::string msg;
  world.recv(1, 1, msg);
  std::cout << msg << "!" << std::endl;
} else {
  std::string msg;
  world.recv(0, 0, msg);
  std::cout << msg << ", ";
  std::cout.flush();
  world.send(0, 1, std::string("world"));
}

The first process passes a message to the second process, then prepares to receive a message. The second process does the send and receive in the opposite order. However, this sequence of events is just that--a sequence--meaning that there is essentially no parallelism. We can use non-blocking communication to ensure that the two messages are transmitted simultaneously (hello_world_nonblocking.cpp):

#include <boost/mpi.hpp>
#include <iostream>
#include <string>
#include <boost/serialization/string.hpp>
namespace mpi = boost::mpi;

int main()
{
  mpi::environment env;
  mpi::communicator world;

  if (world.rank() == 0) {
    mpi::request reqs[2];
    std::string msg, out_msg = "Hello";
    reqs[0] = world.isend(1, 0, out_msg);
    reqs[1] = world.irecv(1, 1, msg);
    mpi::wait_all(reqs, reqs + 2);
    std::cout << msg << "!" << std::endl;
  } else {
    mpi::request reqs[2];
    std::string msg, out_msg = "world";
    reqs[0] = world.isend(0, 1, out_msg);
    reqs[1] = world.irecv(0, 0, msg);
    mpi::wait_all(reqs, reqs + 2);
    std::cout << msg << ", ";
  }

  return 0;
}

We have replaced calls to the communicator::send and communicator::recv members with similar calls to their non-blocking counterparts, communicator::isend and communicator::irecv. The prefix i indicates that the operations return immediately with a mpi::request object, which allows one to query the status of a communication request (see the test method) or wait until it has completed (see the wait method). Multiple requests can be completed at the same time with the wait_all operation.

Important note: The MPI standard requires users to keep the request handle for a non-blocking communication, and to call the "wait" operation (or successfully test for completion) to complete the send or receive. Unlike most C MPI implementations, which allow the user to discard the request for a non-blocking send, Boost.MPI requires the user to call "wait" or "test", since the request object might contain temporary buffers that have to be kept until the send is completed. Moreover, the MPI standard does not guarantee that the receive makes any progress before a call to "wait" or "test", although most implementations of the C MPI do allow receives to progress before the call to "wait" or "test". Boost.MPI, on the other hand, generally requires "test" or "wait" calls to make progress.

If you run this program multiple times, you may see some strange results: namely, some runs will produce:

Hello, world!

while others will produce:

world!
Hello,

or even some garbled version of the letters in "Hello" and "world". This indicates that there is some parallelism in the program, because after both messages are (simultaneously) transmitted, both processes will concurrent execute their print statements. For both performance and correctness, non-blocking communication operations are critical to many parallel applications using MPI.

The inclusion of boost/serialization/string.hpp in the previous examples is very important: it makes values of type std::string serializable, so that they can be be transmitted using Boost.MPI. In general, built-in C++ types (ints, floats, characters, etc.) can be transmitted over MPI directly, while user-defined and library-defined types will need to first be serialized (packed) into a format that is amenable to transmission. Boost.MPI relies on the Boost.Serialization library to serialize and deserialize data types.

For types defined by the standard library (such as std::string or std::vector) and some types in Boost (such as boost::variant), the Boost.Serialization library already contains all of the required serialization code. In these cases, you need only include the appropriate header from the boost/serialization directory.

For types that do not already have a serialization header, you will first need to implement serialization code before the types can be transmitted using Boost.MPI. Consider a simple class gps_position that contains members degrees, minutes, and seconds. This class is made serializable by making it a friend of boost::serialization::access and introducing the templated serialize() function, as follows:

class gps_position
{
private:
    friend class boost::serialization::access;

    template<class Archive>
    void serialize(Archive & ar, const unsigned int version)
    {
        ar & degrees;
        ar & minutes;
        ar & seconds;
    }

    int degrees;
    int minutes;
    float seconds;
public:
    gps_position(){};
    gps_position(int d, int m, float s) :
        degrees(d), minutes(m), seconds(s)
    {}
};

Complete information about making types serializable is beyond the scope of this tutorial. For more information, please see the Boost.Serialization library tutorial from which the above example was extracted. One important side benefit of making types serializable for Boost.MPI is that they become serializable for any other usage, such as storing the objects to disk and manipulated them in XML.

Some serializable types, like gps_position above, have a fixed amount of data stored at fixed offsets and are fully defined by the values of their data member (most POD with no pointers are a good example). When this is the case, Boost.MPI can optimize their serialization and transmission by avoiding extraneous copy operations. To enable this optimization, users must specialize the type trait is_mpi_datatype, e.g.:

namespace boost { namespace mpi {
  template <>
  struct is_mpi_datatype<gps_position> : mpl::true_ { };
} }

For non-template types we have defined a macro to simplify declaring a type as an MPI datatype

BOOST_IS_MPI_DATATYPE(gps_position)

For composite traits, the specialization of is_mpi_datatype may depend on is_mpi_datatype itself. For instance, a boost::array object is fixed only when the type of the parameter it stores is fixed:

namespace boost { namespace mpi {
  template <typename T, std::size_t N>
  struct is_mpi_datatype<array<T, N> >
    : public is_mpi_datatype<T> { };
} }

The redundant copy elimination optimization can only be applied when the shape of the data type is completely fixed. Variable-length types (e.g., strings, linked lists) and types that store pointers cannot use the optimization, but Boost.MPI will be unable to detect this error at compile time. Attempting to perform this optimization when it is not correct will likely result in segmentation faults and other strange program behavior.

Boost.MPI can transmit any user-defined data type from one process to another. Built-in types can be transmitted without any extra effort; library-defined types require the inclusion of a serialization header; and user-defined types will require the addition of serialization code. Fixed data types can be optimized for transmission using the is_mpi_datatype type trait.

The broadcast algorithm is by far the simplest collective operation. It broadcasts a value from a single process to all other processes within a communicator. For instance, the following program broadcasts "Hello, World!" from process 0 to every other process. (hello_world_broadcast.cpp)

#include <boost/mpi.hpp>
#include <iostream>
#include <string>
#include <boost/serialization/string.hpp>
namespace mpi = boost::mpi;

int main()
{
  mpi::environment env;
  mpi::communicator world;

  std::string value;
  if (world.rank() == 0) {
    value = "Hello, World!";
  }

  broadcast(world, value, 0);

  std::cout << "Process #" << world.rank() << " says " << value
            << std::endl;
  return 0;
}

Running this program with seven processes will produce a result such as:

Process #0 says Hello, World!
Process #2 says Hello, World!
Process #1 says Hello, World!
Process #4 says Hello, World!
Process #3 says Hello, World!
Process #5 says Hello, World!
Process #6 says Hello, World!

The gather collective gathers the values produced by every process in a communicator into a vector of values on the "root" process (specified by an argument to gather). The /i/th element in the vector will correspond to the value gathered from the /i/th process. For instance, in the following program each process computes its own random number. All of these random numbers are gathered at process 0 (the "root" in this case), which prints out the values that correspond to each processor. (random_gather.cpp)

#include <boost/mpi.hpp>
#include <iostream>
#include <vector>
#include <cstdlib>
namespace mpi = boost::mpi;

int main()
{
  mpi::environment env;
  mpi::communicator world;

  std::srand(time(0) + world.rank());
  int my_number = std::rand();
  if (world.rank() == 0) {
    std::vector<int> all_numbers;
    gather(world, my_number, all_numbers, 0);
    for (int proc = 0; proc < world.size(); ++proc)
      std::cout << "Process #" << proc << " thought of "
                << all_numbers[proc] << std::endl;
  } else {
    gather(world, my_number, 0);
  }

  return 0;
}

Executing this program with seven processes will result in output such as the following. Although the random values will change from one run to the next, the order of the processes in the output will remain the same because only process 0 writes to std::cout.

Process #0 thought of 332199874
Process #1 thought of 20145617
Process #2 thought of 1862420122
Process #3 thought of 480422940
Process #4 thought of 1253380219
Process #5 thought of 949458815
Process #6 thought of 650073868

The gather operation collects values from every process into a vector at one process. If instead the values from every process need to be collected into identical vectors on every process, use the all_gather algorithm, which is semantically equivalent to calling gather followed by a broadcast of the resulting vector.

The scatter collective scatters the values from a vector in the "root" process in a communicator into values in all the processes of the communicator. The /i/th element in the vector will correspond to the value received by the /i/th process. For instance, in the following program, the root process produces a vector of random nomber and send one value to each process that will print it. (random_scatter.cpp)

#include <boost/mpi.hpp>
#include <boost/mpi/collectives.hpp>
#include <iostream>
#include <cstdlib>
#include <vector>

namespace mpi = boost::mpi;

int main(int argc, char* argv[])
{
  mpi::environment env(argc, argv);
  mpi::communicator world;

  std::srand(time(0) + world.rank());
  std::vector<int> all;
  int mine = -1;
  if (world.rank() == 0) {
    all.resize(world.size());
    std::generate(all.begin(), all.end(), std::rand);
  }
  mpi::scatter(world, all, mine, 0);
  for (int r = 0; r < world.size(); ++r) {
    world.barrier();
    if (r == world.rank()) {
      std::cout << "Rank " << r << " got " << mine << '\n';
    }
  }
  return 0;
}

Executing this program with seven processes will result in output such as the following. Although the random values will change from one run to the next, the order of the processes in the output will remain the same because of the barrier.

Rank 0 got 1409381269
Rank 1 got 17045268
Rank 2 got 440120016
Rank 3 got 936998224
Rank 4 got 1827129182
Rank 5 got 1951746047
Rank 6 got 2117359639

The reduce collective summarizes the values from each process into a single value at the user-specified "root" process. The Boost.MPI reduce operation is similar in spirit to the STL accumulate operation, because it takes a sequence of values (one per process) and combines them via a function object. For instance, we can randomly generate values in each process and the compute the minimum value over all processes via a call to reduce (random_min.cpp):

#include <boost/mpi.hpp>
#include <iostream>
#include <cstdlib>
namespace mpi = boost::mpi;

int main()
{
  mpi::environment env;
  mpi::communicator world;

  std::srand(time(0) + world.rank());
  int my_number = std::rand();

  if (world.rank() == 0) {
    int minimum;
    reduce(world, my_number, minimum, mpi::minimum<int>(), 0);
    std::cout << "The minimum value is " << minimum << std::endl;
  } else {
    reduce(world, my_number, mpi::minimum<int>(), 0);
  }

  return 0;
}

The use of mpi::minimum<int> indicates that the minimum value should be computed. mpi::minimum<int> is a binary function object that compares its two parameters via < and returns the smaller value. Any associative binary function or function object will work. For instance, to concatenate strings with reduce one could use the function object std::plus<std::string> (string_cat.cpp):

#include <boost/mpi.hpp>
#include <iostream>
#include <string>
#include <functional>
#include <boost/serialization/string.hpp>
namespace mpi = boost::mpi;

int main()
{
  mpi::environment env;
  mpi::communicator world;

  std::string names[10] = { "zero ", "one ", "two ", "three ",
                            "four ", "five ", "six ", "seven ",
                            "eight ", "nine " };

  std::string result;
  reduce(world,
         world.rank() < 10? names[world.rank()]
                          : std::string("many "),
         result, std::plus<std::string>(), 0);

  if (world.rank() == 0)
    std::cout << "The result is " << result << std::endl;

  return 0;
}

In this example, we compute a string for each process and then perform a reduction that concatenates all of the strings together into one, long string. Executing this program with seven processors yields the following output:

The result is zero one two three four five six

Any kind of binary function objects can be used with reduce. For instance, and there are many such function objects in the C++ standard <functional> header and the Boost.MPI header <boost/mpi/operations.hpp>. Or, you can create your own function object. Function objects used with reduce must be associative, i.e. f(x, f(y, z)) must be equivalent to f(f(x, y), z). If they are also commutative (i..e, f(x, y) == f(y, x)), Boost.MPI can use a more efficient implementation of reduce. To state that a function object is commutative, you will need to specialize the class is_commutative. For instance, we could modify the previous example by telling Boost.MPI that string concatenation is commutative:

namespace boost { namespace mpi {

  template<>
  struct is_commutative<std::plus<std::string>, std::string>
    : mpl::true_ { };

} } // end namespace boost::mpi

By adding this code prior to main(), Boost.MPI will assume that string concatenation is commutative and employ a different parallel algorithm for the reduce operation. Using this algorithm, the program outputs the following when run with seven processes:

The result is zero one four five six two three

Note how the numbers in the resulting string are in a different order: this is a direct result of Boost.MPI reordering operations. The result in this case differed from the non-commutative result because string concatenation is not commutative: f("x", "y") is not the same as f("y", "x"), because argument order matters. For truly commutative operations (e.g., integer addition), the more efficient commutative algorithm will produce the same result as the non-commutative algorithm. Boost.MPI also performs direct mappings from function objects in <functional> to MPI_Op values predefined by MPI (e.g., MPI_SUM, MPI_MAX); if you have your own function objects that can take advantage of this mapping, see the class template is_mpi_op.

Like gather, reduce has an "all" variant called all_reduce that performs the reduction operation and broadcasts the result to all processes. This variant is useful, for instance, in establishing global minimum or maximum values.

The following code (global_min.cpp) shows a broadcasting version of the random_min.cpp example:

#include <boost/mpi.hpp>
#include <iostream>
#include <cstdlib>
namespace mpi = boost::mpi;

int main(int argc, char* argv[])
{
  mpi::environment env(argc, argv);
  mpi::communicator world;

  std::srand(world.rank());
  int my_number = std::rand();
  int minimum;

  all_reduce(world, my_number, minimum, mpi::minimum<int>());

  if (world.rank() == 0) {
      std::cout << "The minimum value is " << minimum << std::endl;
  }

  return 0;
}

In that example we provide both input and output values, requiring twice as much space, which can be a problem depending on the size of the transmitted data. If there is no need to preserve the input value, the output value can be omitted. In that case the input value will be overridden with the output value and Boost.MPI is able, in some situation, to implement the operation with a more space efficient solution (using the MPI_IN_PLACE flag of the MPI C mapping), as in the following example (in_place_global_min.cpp):

#include <boost/mpi.hpp>
#include <iostream>
#include <cstdlib>
namespace mpi = boost::mpi;

int main(int argc, char* argv[])
{
  mpi::environment env(argc, argv);
  mpi::communicator world;

  std::srand(world.rank());
  int my_number = std::rand();

  all_reduce(world, my_number, mpi::minimum<int>());

  if (world.rank() == 0) {
      std::cout << "The minimum value is " << my_number << std::endl;
  }

  return 0;
}

#include <vector>
#include <iostream>

#include <boost/mpi/communicator.hpp>
#include <boost/mpi/collectives.hpp>
#include <boost/mpi/environment.hpp>
#include <boost/mpi/cartesian_communicator.hpp>

#include <boost/test/minimal.hpp>

namespace mpi = boost::mpi;
int test_main(int argc, char* argv[])
{
  mpi::environment  env;
  mpi::communicator world;

  if (world.size() != 24)  return -1;
  mpi::cartesian_dimension dims[] = {{2, true}, {3,true}, {4,true}};
  mpi::cartesian_communicator cart(world, mpi::cartesian_topology(dims));
  for (int r = 0; r < cart.size(); ++r) {
    cart.barrier();
    if (r == cart.rank()) {
      std::vector<int> c = cart.coordinates(r);
      std::cout << "rk :" << r << " coords: "
                << c[0] << ' ' << c[1] << ' ' << c[2] << '\n';
    }
  }
  return 0;
}

When communicating data types over MPI that are not fundamental to MPI (such as strings, lists, and user-defined data types), Boost.MPI must first serialize these data types into a buffer and then communicate them; the receiver then copies the results into a buffer before deserializing into an object on the other end. For some data types, this overhead can be eliminated by using is_mpi_datatype. However, variable-length data types such as strings and lists cannot be MPI data types.

Boost.MPI supports a second technique for improving performance by separating the structure of these variable-length data structures from the content stored in the data structures. This feature is only beneficial when the shape of the data structure remains the same but the content of the data structure will need to be communicated several times. For instance, in a finite element analysis the structure of the mesh may be fixed at the beginning of computation but the various variables on the cells of the mesh (temperature, stress, etc.) will be communicated many times within the iterative analysis process. In this case, Boost.MPI allows one to first send the "skeleton" of the mesh once, then transmit the "content" multiple times. Since the content need not contain any information about the structure of the data type, it can be transmitted without creating separate communication buffers.

To illustrate the use of skeletons and content, we will take a somewhat more limited example wherein a master process generates random number sequences into a list and transmits them to several slave processes. The length of the list will be fixed at program startup, so the content of the list (i.e., the current sequence of numbers) can be transmitted efficiently. The complete example is available in example/random_content.cpp. We being with the master process (rank 0), which builds a list, communicates its structure via a skeleton, then repeatedly generates random number sequences to be broadcast to the slave processes via content:

// Generate the list and broadcast its structure
std::list<int> l(list_len);
broadcast(world, mpi::skeleton(l), 0);

// Generate content several times and broadcast out that content
mpi::content c = mpi::get_content(l);
for (int i = 0; i < iterations; ++i) {
  // Generate new random values
  std::generate(l.begin(), l.end(), &random);

  // Broadcast the new content of l
  broadcast(world, c, 0);
}

// Notify the slaves that we're done by sending all zeroes
std::fill(l.begin(), l.end(), 0);
broadcast(world, c, 0);

The slave processes have a very similar structure to the master. They receive (via the broadcast() call) the skeleton of the data structure, then use it to build their own lists of integers. In each iteration, they receive via another broadcast() the new content in the data structure and compute some property of the data:

// Receive the content and build up our own list
std::list<int> l;
broadcast(world, mpi::skeleton(l), 0);

mpi::content c = mpi::get_content(l);
int i = 0;
do {
  broadcast(world, c, 0);

  if (std::find_if
       (l.begin(), l.end(),
        std::bind1st(std::not_equal_to<int>(), 0)) == l.end())
    break;

  // Compute some property of the data.

  ++i;
} while (true);

The skeletons and content of any Serializable data type can be transmitted either via the send and recv members of the communicator class (for point-to-point communicators) or broadcast via the broadcast() collective. When separating a data structure into a skeleton and content, be careful not to modify the data structure (either on the sender side or the receiver side) without transmitting the skeleton again. Boost.MPI can not detect these accidental modifications to the data structure, which will likely result in incorrect data being transmitted or unstable programs.

This section provides tables that map from the functions and constants of the standard C MPI to their Boost.MPI equivalents. It will be most useful for users that are already familiar with the C or Fortran interfaces to MPI, or for porting existing parallel programs to Boost.MPI.

Boost.MPI automatically maps C and C++ data types to their MPI equivalents. The following table illustrates the mappings between C++ types and MPI datatype constants.

Boost.MPI does not provide direct wrappers to the MPI derived datatypes functionality. Instead, Boost.MPI relies on the Boost.Serialization library to construct MPI datatypes for user-defined classes. The section on user-defined data types describes this mechanism, which is used for types that marked as "MPI datatypes" using is_mpi_datatype.

The derived datatypes table that follows describes which C++ types correspond to the functionality of the C MPI's datatype constructor. Boost.MPI may not actually use the C MPI function listed when building datatypes of a certain form. Since the actual datatypes built by Boost.MPI are typically hidden from the user, many of these operations are called internally by Boost.MPI.

MPI's packing facilities store values into a contiguous buffer, which can later be transmitted via MPI and unpacked into separate values via MPI's unpacking facilities. As with datatypes, Boost.MPI provides an abstract interface to MPI's packing and unpacking facilities. In particular, the two archive classes packed_oarchive and packed_iarchive can be used to pack or unpack a contiguous buffer using MPI's facilities.

Boost.MPI supports a one-to-one mapping for most of the MPI collectives. For each collective provided by Boost.MPI, the underlying C MPI collective will be invoked when it is possible (and efficient) to do so.

Boost.MPI uses function objects to specify how reductions should occur in its equivalents to MPI_Allreduce, MPI_Reduce, and MPI_Scan. The following table illustrates how predefined and user-defined reduction operations can be mapped between the C MPI and Boost.MPI.

MPI defines several special communicators, including MPI_COMM_WORLD (including all processes that the local process can communicate with), MPI_COMM_SELF (including only the local process), and MPI_COMM_EMPTY (including no processes). These special communicators are all instances of the communicator class in Boost.MPI.

Boost.MPI supports groups of processes through its group class.

Boost.MPI provides manipulation of communicators through the communicator class.

Boost.MPI currently provides support for inter-communicators via the intercommunicator class.

Boost.MPI currently provides no support for attribute caching.

Boost.MPI will provide complete support for creating communicators with different topologies and later querying those topologies. Support for graph topologies is provided via an interface to the Boost Graph Library (BGL), where a communicator can be created which matches the structure of any BGL graph, and the graph topology of a communicator can be viewed as a BGL graph for use in existing, generic graph algorithms.

Boost.MPI supports environmental inquires through the environment class.

Boost.MPI translates MPI errors into exceptions, reported via the exception class.

The MPI timing facilities are exposed via the Boost.MPI timer class, which provides an interface compatible with the Boost Timer library.

MPI startup and shutdown are managed by the construction and destruction of the Boost.MPI environment class.

Boost.MPI does not provide any support for the profiling facilities in MPI 1.1.

This file is a top-level convenience header that includes all of the Boost.MPI library headers. Users concerned about compile time may wish to include only specific headers from the Boost.MPI library.

This header provides an STL-compliant allocator that uses the MPI-2 memory allocation facilities.

namespace boost {
  namespace mpi {
    template<typename T> class allocator;

    template<> class allocator<void>;
    template<typename T1, typename T2> 
      bool operator==(const allocator< T1 > &, const allocator< T2 > &);
    template<typename T1, typename T2> 
      bool operator!=(const allocator< T1 > &, const allocator< T2 > &);
  }
}

This header defines facilities to support MPI communicators with cartesian topologies. If known at compiled time, the dimension of the implied grid can be statically enforced, through the templatized communicator class. Otherwise, a non template, dynamic, base class is provided.

namespace boost {
  namespace mpi {
    struct cartesian_dimension;

    template<> struct is_mpi_datatype<cartesian_dimension>;

    class cartesian_topology;
    class cartesian_communicator;

    // Test if the dimensions values are identical. 
    bool operator==(cartesian_dimension const & d1, 
                    cartesian_dimension const & d2);

    // Test if the dimension values are different. 
    bool operator!=(cartesian_dimension const & d1, 
                    cartesian_dimension const & d2);

    // Pretty printing of a cartesian dimension (size, periodic) 
    std::ostream & 
    operator<<(std::ostream & out, cartesian_dimension const & d);
    bool operator==(cartesian_topology const & t1, 
                    cartesian_topology const & t2);
    bool operator!=(cartesian_topology const & t1, 
                    cartesian_topology const & t2);

    // Pretty printing of a cartesian topology. 
    std::ostream & 
    operator<<(std::ostream & out, cartesian_topology const & t);
    std::vector< int > & cartesian_dimensions(int, std::vector< int > &);
  }
}

This header contains MPI collective operations, which implement various parallel algorithms that require the coordination of all processes within a communicator. The header collectives_fwd.hpp provides forward declarations for each of these operations. To include only specific collective algorithms, use the headers boost/mpi/collectives/algorithm_name.hpp.

namespace boost {
  namespace mpi {
    template<typename T> 
      void all_gather(const communicator &, const T &, std::vector< T > &);
    template<typename T> void all_gather(const communicator &, const T &, T *);
    template<typename T> 
      void all_gather(const communicator &, const T *, int, 
                      std::vector< T > &);
    template<typename T> 
      void all_gather(const communicator &, const T *, int, T *);
    template<typename T, typename Op> 
      void all_reduce(const communicator &, const T *, int, T *, Op);
    template<typename T, typename Op> 
      void all_reduce(const communicator &, const T &, T &, Op);
    template<typename T, typename Op> 
      T all_reduce(const communicator &, const T &, Op);
    template<typename T, typename Op> 
      void all_reduce(const communicator &, inplace_t< T * >, int, Op);
    template<typename T, typename Op> 
      void all_reduce(const communicator &, inplace_t< T >, Op);
    template<typename T> 
      void all_to_all(const communicator &, const std::vector< T > &, 
                      std::vector< T > &);
    template<typename T> void all_to_all(const communicator &, const T *, T *);
    template<typename T> 
      void all_to_all(const communicator &, const std::vector< T > &, int, 
                      std::vector< T > &);
    template<typename T> 
      void all_to_all(const communicator &, const T *, int, T *);
    template<typename T> void broadcast(const communicator &, T &, int);
    template<typename T> void broadcast(const communicator &, T *, int, int);
    template<typename T> 
      void broadcast(const communicator &, skeleton_proxy< T > &, int);
    template<typename T> 
      void broadcast(const communicator &, const skeleton_proxy< T > &, int);
    template<typename T> 
      void gather(const communicator &, const T &, std::vector< T > &, int);
    template<typename T> 
      void gather(const communicator &, const T &, T *, int);
    template<typename T> void gather(const communicator &, const T &, int);
    template<typename T> 
      void gather(const communicator &, const T *, int, std::vector< T > &, 
                  int);
    template<typename T> 
      void gather(const communicator &, const T *, int, T *, int);
    template<typename T> 
      void gather(const communicator &, const T *, int, int);
    template<typename T> 
      void gatherv(const communicator &, const std::vector< T > &, T *, 
                   const std::vector< int > &, const std::vector< int > &, 
                   int);
    template<typename T> 
      void gatherv(const communicator &, const T *, int, T *, 
                   const std::vector< int > &, const std::vector< int > &, 
                   int);
    template<typename T> 
      void gatherv(const communicator &, const std::vector< T > &, int);
    template<typename T> 
      void gatherv(const communicator &, const T *, int, int);
    template<typename T> 
      void gatherv(const communicator &, const T *, int, T *, 
                   const std::vector< int > &, int);
    template<typename T> 
      void gatherv(const communicator &, const std::vector< T > &, T *, 
                   const std::vector< int > &, int);
    template<typename T> 
      void scatter(const communicator &, const std::vector< T > &, T &, int);
    template<typename T> 
      void scatter(const communicator &, const T *, T &, int);
    template<typename T> void scatter(const communicator &, T &, int);
    template<typename T> 
      void scatter(const communicator &, const std::vector< T > &, T *, int, 
                   int);
    template<typename T> 
      void scatter(const communicator &, const T *, T *, int, int);
    template<typename T> void scatter(const communicator &, T *, int, int);
    template<typename T> 
      void scatterv(const communicator &, const std::vector< T > &, 
                    const std::vector< int > &, const std::vector< int > &, 
                    T *, int, int);
    template<typename T> 
      void scatterv(const communicator &, const T *, 
                    const std::vector< int > &, const std::vector< int > &, 
                    T *, int, int);
    template<typename T> void scatterv(const communicator &, T *, int, int);
    template<typename T> 
      void scatterv(const communicator &, const T *, 
                    const std::vector< int > &, T *, int);
    template<typename T> 
      void scatterv(const communicator &, const std::vector< T > &, 
                    const std::vector< int > &, T *, int);
    template<typename T, typename Op> 
      void reduce(const communicator &, const T &, T &, Op, int);
    template<typename T, typename Op> 
      void reduce(const communicator &, const T &, Op, int);
    template<typename T, typename Op> 
      void reduce(const communicator &, const T *, int, T *, Op, int);
    template<typename T, typename Op> 
      void reduce(const communicator &, const T *, int, Op, int);
    template<typename T, typename Op> 
      void scan(const communicator &, const T &, T &, Op);
    template<typename T, typename Op> 
      T scan(const communicator &, const T &, Op);
    template<typename T, typename Op> 
      void scan(const communicator &, const T *, int, T *, Op);
  }
}

This header provides forward declarations for all of the collective operations contained in the header collectives.hpp.

This header defines the communicator class, which is the basis of all communication within Boost.MPI, and provides point-to-point communication operations.

namespace boost {
  namespace mpi {
    class communicator;

    enum comm_create_kind;

    const int any_source;    // A constant representing "any process.". 
    const int any_tag;    // A constant representing "any tag.". 
    BOOST_MPI_DECL bool operator==(const communicator &, const communicator &);
    bool operator!=(const communicator &, const communicator &);
  }
}

This header provides MPI configuration details that expose the capabilities of the underlying MPI implementation, and provides auto-linking support on Windows.

MPICH_IGNORE_CXX_SEEK
BOOST_MPI_HOMOGENEOUS
BOOST_MPI_HAS_MEMORY_ALLOCATION
BOOST_MPI_HAS_NOARG_INITIALIZATION
BOOST_MPI_CALLING_CONVENTION
BOOST_MPI_BCAST_BOTTOM_WORKS_FINE
BOOST_MPI_DECL

This header provides the mapping from C++ types to MPI data types.

BOOST_IS_MPI_DATATYPE(T)

namespace boost {
  namespace mpi {
    template<typename T> struct is_mpi_integer_datatype;
    template<typename T> struct is_mpi_floating_point_datatype;
    template<typename T> struct is_mpi_logical_datatype;
    template<typename T> struct is_mpi_complex_datatype;
    template<typename T> struct is_mpi_byte_datatype;
    template<typename T> struct is_mpi_builtin_datatype;
    template<typename T> struct is_mpi_datatype;
    template<typename T> MPI_Datatype get_mpi_datatype(const T &);
  }
}

This header provides forward declarations for the contents of the header datatype.hpp. It is expected to be used primarily by user-defined C++ classes that need to specialize is_mpi_datatype.

namespace boost {
  namespace mpi {
    struct packed;
    template<typename T> MPI_Datatype get_mpi_datatype();
  }
}

This header provides the environment class, which provides routines to initialize, finalization, and query the status of the Boost MPI environment.

namespace boost {
  namespace mpi {
    class environment;
    namespace threading {
      enum level;
      std::ostream & operator<<(std::ostream &, level);
      std::istream & operator>>(std::istream &, level &);
    }
  }
}

This header provides exception classes that report MPI errors to the user and macros that translate MPI error codes into Boost.MPI exceptions.

BOOST_MPI_CHECK_RESULT(MPIFunc, Args)

namespace boost {
  namespace mpi {
    class exception;
  }
}

This header defines facilities to support MPI communicators with graph topologies, using the graph interface defined by the Boost Graph Library. One can construct a communicator whose topology is described by any graph meeting the requirements of the Boost Graph Library's graph concepts. Likewise, any communicator that has a graph topology can be viewed as a graph by the Boost Graph Library, permitting one to use the BGL's graph algorithms on the process topology.

namespace boost {
  template<> struct graph_traits<mpi::graph_communicator>;
  namespace mpi {
    class graph_communicator;

    // Returns the source vertex from an edge in the graph topology of a communicator. 
    int source(const std::pair< int, int > & edge, const graph_communicator &);

    // Returns the target vertex from an edge in the graph topology of a communicator. 
    int target(const std::pair< int, int > & edge, const graph_communicator &);

    // Returns an iterator range containing all of the edges outgoing from the given vertex in a graph topology of a communicator. 
    unspecified out_edges(int vertex, const graph_communicator & comm);

    // Returns the out-degree of a vertex in the graph topology of a communicator. 
    int out_degree(int vertex, const graph_communicator & comm);

    // Returns an iterator range containing all of the neighbors of the given vertex in the communicator's graph topology. 
    unspecified adjacent_vertices(int vertex, const graph_communicator & comm);

    // Returns an iterator range that contains all of the vertices with the communicator's graph topology, i.e., all of the process ranks in the communicator. 
    std::pair< counting_iterator< int >, counting_iterator< int > > 
    vertices(const graph_communicator & comm);

    // Returns the number of vertices within the graph topology of the communicator, i.e., the number of processes in the communicator. 
    int num_vertices(const graph_communicator & comm);

    // Returns an iterator range that contains all of the edges with the communicator's graph topology. 
    unspecified edges(const graph_communicator & comm);

    // Returns the number of edges in the communicator's graph topology. 
    int num_edges(const graph_communicator & comm);
    identity_property_map get(vertex_index_t, const graph_communicator &);
    int get(vertex_index_t, const graph_communicator &, int);
  }
}

This header defines the group class, which allows one to manipulate and query groups of processes.

namespace boost {
  namespace mpi {
    class group;
    BOOST_MPI_DECL bool operator==(const group &, const group &);
    bool operator!=(const group &, const group &);
    BOOST_MPI_DECL group operator|(const group &, const group &);
    BOOST_MPI_DECL group operator&(const group &, const group &);
    BOOST_MPI_DECL group operator-(const group &, const group &);
  }
}

This header provides helpers to indicate to MPI collective operation that a buffer can be use both as an input and output.

namespace boost {
  namespace mpi {
    template<typename T> struct inplace_t;

    template<typename T> struct inplace_t<T *>;
    template<typename T> inplace_t< T > inplace(T &);
    template<typename T> inplace_t< T * > inplace(T *);
  }
}

This header defines the intercommunicator class, which permits communication between different process groups.

namespace boost {
  namespace mpi {
    class intercommunicator;
  }
}

This header defines operations for completing non-blocking communication requests.

namespace boost {
  namespace mpi {
    template<typename ForwardIterator> 
      std::pair< status, ForwardIterator > 
      wait_any(ForwardIterator, ForwardIterator);
    template<typename ForwardIterator> 
      optional< std::pair< status, ForwardIterator > > 
      test_any(ForwardIterator, ForwardIterator);
    template<typename ForwardIterator, typename OutputIterator> 
      OutputIterator 
      wait_all(ForwardIterator, ForwardIterator, OutputIterator);
    template<typename ForwardIterator> 
      void wait_all(ForwardIterator, ForwardIterator);
    template<typename ForwardIterator, typename OutputIterator> 
      optional< OutputIterator > 
      test_all(ForwardIterator, ForwardIterator, OutputIterator);
    template<typename ForwardIterator> 
      bool test_all(ForwardIterator, ForwardIterator);
    template<typename BidirectionalIterator, typename OutputIterator> 
      std::pair< OutputIterator, BidirectionalIterator > 
      wait_some(BidirectionalIterator, BidirectionalIterator, OutputIterator);
    template<typename BidirectionalIterator> 
      BidirectionalIterator 
      wait_some(BidirectionalIterator, BidirectionalIterator);
    template<typename BidirectionalIterator, typename OutputIterator> 
      std::pair< OutputIterator, BidirectionalIterator > 
      test_some(BidirectionalIterator, BidirectionalIterator, OutputIterator);
    template<typename BidirectionalIterator> 
      BidirectionalIterator 
      test_some(BidirectionalIterator, BidirectionalIterator);
  }
}

This header provides a mapping from function objects to MPI_Op constants used in MPI collective operations. It also provides several new function object types not present in the standard <functional> header that have direct mappings to MPI_Op.

namespace boost {
  namespace mpi {
    template<typename Op, typename T> struct is_mpi_op;
    template<typename Op, typename T> struct is_commutative;
    template<typename T> struct maximum;
    template<typename T> struct minimum;
    template<typename T> struct bitwise_and;
    template<typename T> struct bitwise_or;
    template<typename T> struct logical_xor;
    template<typename T> struct bitwise_xor;
  }
}

This header provides the facilities for packing Serializable data types into a buffer using MPI_Pack. The buffers can then be transmitted via MPI and then be unpacked either via the facilities in packed_oarchive.hpp or MPI_Unpack.

namespace boost {
  namespace mpi {
    class packed_iarchive;

    typedef packed_iprimitive iprimitive;
  }
}

This header provides the facilities for unpacking Serializable data types from a buffer using MPI_Unpack. The buffers are typically received via MPI and have been packed either by via the facilities in packed_iarchive.hpp or MPI_Pack.

namespace boost {
  namespace mpi {
    class packed_oarchive;

    typedef packed_oprimitive oprimitive;
  }
}

This header interacts with the Python bindings for Boost.MPI. The routines in this header can be used to register user-defined and library-defined data types with Boost.MPI for efficient (de-)serialization and separate transmission of skeletons and content.

namespace boost {
  namespace mpi {
    namespace python {
      template<typename T> 
        void register_serialized(const T & = T(), PyTypeObject * = 0);
      template<typename T> 
        void register_skeleton_and_content(const T & = T(), 
                                           PyTypeObject * = 0);
    }
  }
}

This header defines the class request, which contains a request for non-blocking communication.

namespace boost {
  namespace mpi {
    class request;
  }
}

This header provides facilities that allow the structure of data types (called the "skeleton") to be transmitted and received separately from the content stored in those data types. These facilities are useful when the data in a stable data structure (e.g., a mesh or a graph) will need to be transmitted repeatedly. In this case, transmitting the skeleton only once saves both communication effort (it need not be sent again) and local computation (serialization need only be performed once for the content).

namespace boost {
  namespace mpi {
    template<typename T> struct skeleton_proxy;

    class content;
    class packed_skeleton_iarchive;
    class packed_skeleton_oarchive;
    template<typename T> const skeleton_proxy< T > skeleton(T &);
    template<typename T> const content get_content(const T &);
  }
}

This header contains all of the forward declarations required to use transmit skeletons of data structures and the content of data structures separately. To actually transmit skeletons or content, include the header boost/mpi/skeleton_and_content.hpp.

This header defines the class status, which reports on the results of point-to-point communication.

namespace boost {
  namespace mpi {
    class status;
  }
}

This header provides the timer class, which provides access to the MPI timers.

namespace boost {
  namespace mpi {
    class timer;
  }
}

Getting started with the Boost.MPI Python module is as easy as importing boost.mpi. Our first "Hello, World!" program is just two lines long:

import boost.mpi as mpi
print "I am process %d of %d." % (mpi.rank, mpi.size)

Go ahead and run this program with several processes. Be sure to invoke the python interpreter from mpirun, e.g.,

mpirun -np 5 python hello_world.py

This will return output such as:

I am process 1 of 5.
I am process 3 of 5.
I am process 2 of 5.
I am process 4 of 5.
I am process 0 of 5.

Point-to-point operations in Boost.MPI have nearly the same syntax in Python as in C++. We can write a simple two-process Python program that prints "Hello, world!" by transmitting Python strings:

import boost.mpi as mpi

if mpi.world.rank == 0:
  mpi.world.send(1, 0, 'Hello')
  msg = mpi.world.recv(1, 1)
  print msg,'!'
else:
  msg = mpi.world.recv(0, 0)
  print (msg + ', '),
  mpi.world.send(0, 1, 'world')

There are only a few notable differences between this Python code and the example in the C++ tutorial. First of all, we don't need to write any initialization code in Python: just loading the boost.mpi module makes the appropriate MPI_Init and MPI_Finalize calls. Second, we're passing Python objects from one process to another through MPI. Any Python object that can be pickled can be transmitted; the next section will describe in more detail how the Boost.MPI Python layer transmits objects. Finally, when we receive objects with recv, we don't need to specify the type because transmission of Python objects is polymorphic.

When experimenting with Boost.MPI in Python, don't forget that help is always available via pydoc: just pass the name of the module or module entity on the command line (e.g., pydoc boost.mpi.communicator) to receive complete reference documentation. When in doubt, try it!