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## Tutorial

A Boost.MPI program consists of many cooperating processes (possibly running on different computers) that communicate among themselves by passing messages. Boost.MPI is a library (as is the lower-level MPI), not a language, so the first step in a Boost.MPI is to create an `mpi::environment` object that initializes the MPI environment and enables communication among the processes. The `mpi::environment` object is initialized with the program arguments (which it may modify) in your main program. The creation of this object initializes MPI, and its destruction will finalize MPI. In the vast majority of Boost.MPI programs, an instance of `mpi::environment` will be declared in `main` at the very beginning of the program.

Communication with MPI always occurs over a communicator, which can be created be simply default-constructing an object of type `mpi::communicator`. This communicator can then be queried to determine how many processes are running (the "size" of the communicator) and to give a unique number to each process, from zero to the size of the communicator (i.e., the "rank" of the process):

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

    int main(int argc, char* argv[])
    {
      mpi::environment env(argc, argv);
      mpi::communicator world;
      std::cout << "I am process " << world.rank() << " of " << world.size()
                << "." << std::endl;
      return 0;
    }

If you run this program with 7 processes, for instance, you will receive output such as:

    I am process 5 of 7.
    I am process 0 of 7.
    I am process 1 of 7.
    I am process 6 of 7.
    I am process 2 of 7.
    I am process 4 of 7.
    I am process 3 of 7.

Of course, the processes can execute in a different order each time, so the ranks might not be strictly increasing. More interestingly, the text could come out completely garbled, because one process can start writing "I am a process" before another process has finished writing "of 7.".

### Point-to-Point communication

As a message passing library, MPI's primary purpose is to routine messages from one process to another, i.e., point-to-point. MPI contains routines that can send messages, receive messages, and query whether messages are available. Each message has a source process, a target process, a tag, and a payload containing arbitrary data. The source and target processes are the ranks of the sender and receiver of the message, respectively. Tags are integers that allow the receiver to distinguish between different messages coming from the same sender.

The following program uses two MPI processes to write "Hello, world!" to the screen (`hello_world.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) {
        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 first processor (rank 0) passes the message "Hello" to the second processor (rank 1) using tag 0. The second processor prints the string it receives, along with a comma, then passes the message "world" back to processor 0 with a different tag. The first processor then writes this message with the "!" and exits. All sends are accomplished with the `communicator::send` method and all receives use a corresponding `communicator::recv` call.

#### Non-blocking communication

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.

#### User-defined data types

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 (`int`s, `float`s, 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.

### Collective operations

Point-to-point operations are the core message passing primitives in Boost.MPI. However, many message-passing applications also require higher-level communication algorithms that combine or summarize the data stored on many different processes. These algorithms support many common tasks such as "broadcast this value to all processes", "compute the sum of the values on all processors" or "find the global minimum."

#### Broadcast

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!

#### Gather

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.

#### Reduce

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.

### Managing communicators

Communication with Boost.MPI always occurs over a communicator. A communicator contains a set of processes that can send messages among themselves and perform collective operations. There can be many communicators within a single program, each of which contains its own isolated communication space that acts independently of the other communicators.

When the MPI environment is initialized, only the "world" communicator (called `MPI_COMM_WORLD` in the MPI C and Fortran bindings) is available. The "world" communicator, accessed by default-constructing a `mpi::communicator` object, contains all of the MPI processes present when the program begins execution. Other communicators can then be constructed by duplicating or building subsets of the "world" communicator. For instance, in the following program we split the processes into two groups: one for processes generating data and the other for processes that will collect the data. (`generate_collect.cpp`)

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

    enum message_tags {msg_data_packet, msg_broadcast_data, msg_finished};

    void generate_data(mpi::communicator local, mpi::communicator world);
    void collect_data(mpi::communicator local, mpi::communicator world);

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

      bool is_generator = world.rank() < 2 * world.size() / 3;
      mpi::communicator local = world.split(is_generator? 0 : 1);
      if (is_generator) generate_data(local, world);
      else collect_data(local, world);

      return 0;
    }

When communicators are split in this way, their processes retain membership in both the original communicator (which is not altered by the split) and the new communicator. However, the ranks of the processes may be different from one communicator to the next, because the rank values within a communicator are always contiguous values starting at zero. In the example above, the first two thirds of the processes become "generators" and the remaining processes become "collectors". The ranks of the "collectors" in the `world` communicator will be 2/3 `world.size()` and greater, whereas the ranks of the same collector processes in the `local` communicator will start at zero. The following excerpt from `collect_data()` (in `generate_collect.cpp`) illustrates how to manage multiple communicators:

    mpi::status msg = world.probe();
    if (msg.tag() == msg_data_packet) {
      // Receive the packet of data
      std::vector<int> data;
      world.recv(msg.source(), msg.tag(), data);

      // Tell each of the collectors that we'll be broadcasting some data
      for (int dest = 1; dest < local.size(); ++dest)
        local.send(dest, msg_broadcast_data, msg.source());

      // Broadcast the actual data.
      broadcast(local, data, 0);
    }

The code in this except is executed by the "master" collector, e.g., the node with rank 2/3 `world.size()` in the `world` communicator and rank 0 in the `local` (collector) communicator. It receives a message from a generator via the `world` communicator, then broadcasts the message to each of the collectors via the `local` communicator.

For more control in the creation of communicators for subgroups of processes, the Boost.MPI `group` provides facilities to compute the union (`|`), intersection (`&`), and difference (`-`) of two groups, generate arbitrary subgroups, etc.

### Separating structure from content

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.

### Performance optimizations

#### Serialization optimizations

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)

#### Homogeneous machines

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.

### Mapping from C MPI to Boost.MPI

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.

Table 17.1. Point-to-point communication

C Function/Constant | Boost.MPI Equivalent
--- | ---
`MPI_ANY_SOURCE` | `any_source`
`MPI_ANY_TAG` | `any_tag`
`MPI_Bsend` | unsupported
`MPI_Bsend_init` | unsupported
`MPI_Buffer_attach` | unsupported
`MPI_Buffer_detach` | unsupported
`MPI_Cancel` | `request::cancel`
`MPI_Get_count` | `status::count`
`MPI_Ibsend` | unsupported
`MPI_Iprobe` | `communicator::iprobe`
`MPI_Irsend` | unsupported
`MPI_Isend` | `communicator::isend`
`MPI_Issend` | unsupported
`MPI_Irecv` | `communicator::irecv`
`MPI_Probe` | `communicator::probe`
`MPI_PROC_NULL` | unsupported
`MPI_Recv` | `communicator::recv`
`MPI_Recv_init` | unsupported
`MPI_Request_free` | unsupported
`MPI_Rsend` | unsupported
`MPI_Rsend_init` | unsupported
`MPI_Send` | `communicator::send`
`MPI_Sendrecv` | unsupported
`MPI_Sendrecv_replace` | unsupported
`MPI_Send_init` | unsupported
`MPI_Ssend` | unsupported
`MPI_Ssend_init` | unsupported
`MPI_Start` | unsupported
`MPI_Startall` | unsupported
`MPI_Test` | `request::test`
`MPI_Testall` | `test_all`
`MPI_Testany` | `test_any`
`MPI_Testsome` | `test_some`
`MPI_Test_cancelled` | `status::cancelled`
`MPI_Wait` | `request::wait`
`MPI_Waitall` | `wait_all`
`MPI_Waitany` | `wait_any`
`MPI_Waitsome` | `wait_some`

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.

Table 17.2. Datatypes

C Constant | Boost.MPI Equivalent
--- | ---
`MPI_CHAR` | `signed char`
`MPI_SHORT` | `signed short int`
`MPI_INT` | `signed int`
`MPI_LONG` | `signed long int`
`MPI_UNSIGNED_CHAR` | `unsigned char`
`MPI_UNSIGNED_SHORT` | `unsigned short int`
`MPI_UNSIGNED_INT` | `unsigned int`
`MPI_UNSIGNED_LONG` | `unsigned long int`
`MPI_FLOAT` | `float`
`MPI_DOUBLE` | `double`
`MPI_LONG_DOUBLE` | `long double`
`MPI_BYTE` | unused
`MPI_PACKED` | used internally for serialized data types
`MPI_LONG_LONG_INT` | `long long int`, if supported by compiler
`MPI_UNSIGNED_LONG_LONG_INT` | `unsigned long long int`, if supported by compiler
`MPI_FLOAT_INT` | `std::pair<float, int>`
`MPI_DOUBLE_INT` | `std::pair<double, int>`
`MPI_LONG_INT` | `std::pair<long, int>`
`MPI_2INT` | `std::pair<int, int>`
`MPI_SHORT_INT` | `std::pair<short, int>`
`MPI_LONG_DOUBLE_INT` | `std::pair<long double, int>`

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 classe. 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.

Table 17.3. Derived datatypes

C Function/Constant | Boost.MPI Equivalent
--- | ---
`MPI_Address` | used automatically in Boost.MPI
`MPI_Type_commit` | used automatically in Boost.MPI
`MPI_Type_contiguous` | arrays
`MPI_Type_extent` | used automatically in Boost.MPI
`MPI_Type_free` | used automatically in Boost.MPI
`MPI_Type_hindexed` | any type used as a subobject
`MPI_Type_hvector` | unused
`MPI_Type_indexed` | any type used as a subobject
`MPI_Type_lb` | unsupported
`MPI_Type_size` | used automatically in Boost.MPI
`MPI_Type_struct` | user-defined classes and structs
`MPI_Type_ub` | unsupported
`MPI_Type_vector` | used automatically in 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.

Table 17.4. Packing and unpacking

C Function | Boost.MPI Equivalent
--- | ---
`MPI_Pack` | `packed_oarchive`
`MPI_Pack_size` | used internally by Boost.MPI
`MPI_Unpack` | `packed_iarchive`

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.

Table 17.5. Collectives

C Function | Boost.MPI Equivalent
--- | ---
`MPI_Allgather` | `all_gather`
`MPI_Allgatherv` | most uses supported by `all_gather`
`MPI_Allreduce` | `all_reduce`
`MPI_Alltoall` | `all_to_all`
`MPI_Alltoallv` | most uses supported by `all_to_all`
`MPI_Barrier` | `communicator::barrier`
`MPI_Bcast` | `broadcast`
`MPI_Gather` | `gather`
`MPI_Gatherv` | most uses supported by `gather`
`MPI_Reduce` | `reduce`
`MPI_Reduce_scatter` | unsupported
`MPI_Scan` | `scan`
`MPI_Scatter` | `scatter`
`MPI_Scatterv` | most uses supported by `scatter`

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.

Table 17.6. Reduction operations

C Constant | Boost.MPI Equivalent
--- | ---
`MPI_BAND` | `bitwise_and`
`MPI_BOR` | `bitwise_or`
`MPI_BXOR` | `bitwise_xor`
`MPI_LAND` | `std::logical_and`
`MPI_LOR` | `std::logical_or`
`MPI_LXOR` | `logical_xor`
`MPI_MAX` | `maximum`
`MPI_MAXLOC` | unsupported
`MPI_MIN` | `minimum`
`MPI_MINLOC` | unsupported
`MPI_Op_create` | used internally by Boost.MPI
`MPI_Op_free` | used internally by Boost.MPI
`MPI_PROD` | `std::multiplies`
`MPI_SUM` | `std::plus`

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.

Table 17.7. Predefined communicators

C Constant | Boost.MPI Equivalent
--- | ---
`MPI_COMM_WORLD` | a default-constructed `communicator`
`MPI_COMM_SELF` | a `communicator` that contains only the current process
`MPI_COMM_EMPTY` | a `communicator` that evaluates false

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

Table 17.8. Group operations and constants

C Function/Constant | Boost.MPI Equivalent
--- | ---
`MPI_GROUP_EMPTY` | a default-constructed `group`
`MPI_Group_size` | `group::size`
`MPI_Group_rank` | memberref boost::mpi::group::rank `group::rank`
`MPI_Group_translate_ranks` | memberref boost::mpi::group::translate_ranks `group::translate_ranks`
`MPI_Group_compare` | operators `==` and `!=`
`MPI_IDENT` | operators `==` and `!=`
`MPI_SIMILAR` | operators `==` and `!=`
`MPI_UNEQUAL` | operators `==` and `!=`
`MPI_Comm_group` | `communicator::group`
`MPI_Group_union` | operator `|` for groups
`MPI_Group_intersection` | operator `&` for groups
`MPI_Group_difference` | operator `-` for groups
`MPI_Group_incl` | `group::include`
`MPI_Group_excl` | `group::exclude`
`MPI_Group_range_incl` | unsupported
`MPI_Group_range_excl` | unsupported
`MPI_Group_free` | used automatically in Boost.MPI

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

Table 17.9. Communicator operations

C Function | Boost.MPI Equivalent
--- | ---
`MPI_Comm_size` | `communicator::size`
`MPI_Comm_rank` | `communicator::rank`
`MPI_Comm_compare` | operators `==` and `!=`
`MPI_Comm_dup` | `communicator` class constructor using `comm_duplicate`
`MPI_Comm_create` | `communicator` constructor
`MPI_Comm_split` | `communicator::split`
`MPI_Comm_free` | used automatically in Boost.MPI

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

Table 17.10. Inter-communicator operations

C Function | Boost.MPI Equivalent
--- | ---
`MPI_Comm_test_inter` | use `communicator::as_intercommunicator`
`MPI_Comm_remote_size` | `boost::mpi::intercommunicator::remote_size` `intercommunicator::remote_size`
`MPI_Comm_remote_group` | `intercommunicator::remote_group`
`MPI_Intercomm_create` | `intercommunicator` constructor
`MPI_Intercomm_merge` | `intercommunicator::merge`

Boost.MPI currently provides no support for attribute caching.

Table 17.11. Attributes and caching

C Function/Constant | Boost.MPI Equivalent
--- | ---
`MPI_NULL_COPY_FN` | unsupported
`MPI_NULL_DELETE_FN` | unsupported
`MPI_KEYVAL_INVALID` | unsupported
`MPI_Keyval_create` | unsupported
`MPI_Copy_function` | unsupported
`MPI_Delete_function` | unsupported
`MPI_Keyval_free` | unsupported
`MPI_Attr_put` | unsupported
`MPI_Attr_get` | unsupported
`MPI_Attr_delete` | unsupported

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.

Table 17.12. Process topologies

C Function/Constant | Boost.MPI Equivalent
--- | ---
`MPI_GRAPH` | unnecessary; use `communicator::has_graph_topology`
`MPI_CART` | unnecessary; use `communicator::has_cartesian_topology`
`MPI_Cart_create` | unsupported
`MPI_Dims_create` | unsupported
`MPI_Graph_create` | `communicator::with_graph_topology`
`MPI_Topo_test` | `communicator::has_graph_topology`, `communicator::has_cartesian_topology`
`MPI_Graphdims_get` | `num_vertices`, `num_edges`
`MPI_Graph_get` | `vertices`, `edges`
`MPI_Cartdim_get` | unsupported
`MPI_Cart_get` | unsupported
`MPI_Cart_rank` | unsupported
`MPI_Cart_coords` | unsupported
`MPI_Graph_neighbors_count` | `out_degree`
`MPI_Graph_neighbors` | `out_edges`, `adjacent_vertices`
`MPI_Cart_shift` | unsupported
`MPI_Cart_sub` | unsupported
`MPI_Cart_map` | unsupported
`MPI_Graph_map` | unsupported

Boost.MPI supports environmental inquires through the `environment` class.

Table 17.13. Environmental inquiries

C Function/Constant | Boost.MPI Equivalent
--- | ---
`MPI_TAG_UB` | unnecessary; use `environment::max_tag`
`MPI_HOST` | unnecessary; use `environment::host_rank`
`MPI_IO` | unnecessary; use `environment::io_rank`
`MPI_Get_processor_name` | `environment::processor_name`

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

Table 17.14. Error handling

C Function/Constant | Boost.MPI Equivalent
--- | ---
`MPI_ERRORS_ARE_FATAL` | unused; errors are translated into Boost.MPI exceptions
`MPI_ERRORS_RETURN` | unused; errors are translated into Boost.MPI exceptions
`MPI_errhandler_create` | unused; errors are translated into Boost.MPI exceptions
`MPI_errhandler_set` | unused; errors are translated into Boost.MPI exceptions
`MPI_errhandler_get` | unused; errors are translated into Boost.MPI exceptions
`MPI_errhandler_free` | unused; errors are translated into Boost.MPI exceptions
`MPI_Error_string` | used internally by Boost.MPI
`MPI_Error_class` | `exception::error_class`

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

Table 17.15. Timing facilities

C Function/Constant | Boost.MPI Equivalent
--- | ---
`MPI_WTIME_IS_GLOBAL` | unnecessary; use `timer::time_is_global`
`MPI_Wtime` | use `timer::elapsed` to determine the time elapsed from some specific starting point
`MPI_Wtick` | `timer::elapsed_min`

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

Table 17.16. Startup/shutdown facilities

C Function | Boost.MPI Equivalent
--- | ---
`MPI_Init` | `environment` constructor
`MPI_Finalize` | `environment` destructor
`MPI_Initialized` | `environment::initialized`
`MPI_Abort` | `environment::abort`

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

Table 17.17. Profiling interface

C Function | Boost.MPI Equivalent
--- | ---
`PMPI_*` routines | unsupported
`MPI_Pcontrol` | unsupported