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

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 to manipulated them in XML.

Some serializable types, like gps_position above, have a fixed amount of data stored at fixed field positions. When this is the case, Boost.MPI can optimize their serialization and transmission to avoid extraneous copy operations. To enable this optimization, users should 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 optimiation, 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(int argc, char* argv[])
{
  mpi::environment env(argc, argv);
  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 fro mthe /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(int argc, char* argv[])
{
  mpi::environment env(argc, argv);
  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 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(int argc, char* argv[])
{
  mpi::environment env(argc, argv);
  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(int argc, char* argv[])
{
  mpi::environment env(argc, argv);
  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.

To obtain optimal performance for small fixed-length data types not containing any pointers it is very important to mark them using the type traits of Boost.MPI and Boost.Serialization.

It was alredy discussed that fixed length types containing no pointers can be using as is_mpi_datatype, e.g.:

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

or the equivalent macro

BOOST_IS_MPI_DATATYPE(gps_position)

In addition it can give a substantial performance gain to turn off tracking and versioning for these types, if no pointers to these types are used, by using the traits classes or helper macros of Boost.Serialization:

BOOST_CLASS_TRACKING(gps_position,track_never)
BOOST_CLASS_IMPLEMENTATION(gps_position,object_serializable)

More optimizations are possible on homogeneous machines, by avoiding MPI_Pack/MPI_Unpack calls but using direct bitwise copy. This feature can be enabled by defining the macro BOOST_MPI_HOMOGENEOUS when building Boost.MPI and when building the application.

In addition all classes need to be marked both as is_mpi_datatype and as is_bitwise_serializable, by using the helper macro of Boost.Serialization:

BOOST_IS_BITWISE_SERIALIZABLE(gps_position)

Usually it is safe to serialize a class for which is_mpi_datatype is true by using binary copy of the bits. The exception are classes for which some members should be skipped for serialization.