...one of the most highly
regarded and expertly designed C++ library projects in the
world.
— Herb Sutter and Andrei
Alexandrescu, C++
Coding Standards
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() { mpi::environment env; 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.".
If you should still have an MPI library supporting only MPI 1.1 you will need to pass the command line arguments to the environment constructor as shown in this example:
#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; }
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() { 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 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.
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 (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 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 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.
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."
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 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() { 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 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 ouput value
can be omitted. In that case the input value will be overriden 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; }
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() { mpi::environment env; 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.
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.
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 is enabled by default
by defining the macro BOOST_MPI_HOMOGENEOUS
in the include file boost/mpi/config.hpp
.
That definition must be consistent 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.
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 20.1. Point-to-point communication
C Function/Constant |
Boost.MPI Equivalent |
---|---|
|
|
|
|
unsupported |
|
unsupported |
|
unsupported |
|
unsupported |
|
unsupported |
|
unsupported |
|
unsupported |
|
unsupported |
|
unsupported |
|
unsupported |
|
unsupported |
|
unsupported |
|
unsupported |
|
unsupported |
|
unsupported |
|
unsupported |
|
unsupported |
|
unsupported |
|
unsupported |
|
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 20.2. Datatypes
C Constant |
Boost.MPI Equivalent |
---|---|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
unused |
|
used internally for serialized data types |
|
|
|
|
|
|
|
|
|
|
|
|
|
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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 20.3. Derived datatypes
C Function/Constant |
Boost.MPI Equivalent |
---|---|
used automatically in Boost.MPI for MPI version 1.x |
|
used automatically in Boost.MPI for MPI version 2.0 and higher |
|
used automatically in Boost.MPI |
|
arrays |
|
used automatically in Boost.MPI |
|
used automatically in Boost.MPI |
|
any type used as a subobject |
|
unused |
|
any type used as a subobject |
|
unsupported |
|
used automatically in Boost.MPI |
|
user-defined classes and structs with MPI 1.x |
|
user-defined classes and structs with MPI 2.0 and higher |
|
unsupported |
|
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.
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 20.5. Collectives
C Function |
Boost.MPI Equivalent |
---|---|
most uses supported by |
|
most uses supported by |
|
most uses supported by |
|
unsupported |
|
most uses supported by |
|
supported implicitly by |
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 20.6. Reduction operations
C Constant |
Boost.MPI Equivalent |
---|---|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
unsupported |
|
|
|
unsupported |
|
used internally by Boost.MPI |
|
used internally by 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.
Table 20.7. Predefined communicators
C Constant |
Boost.MPI Equivalent |
---|---|
|
a default-constructed |
|
a |
|
a |
Boost.MPI supports groups of processes through its group
class.
Table 20.8. Group operations and constants
C Function/Constant |
Boost.MPI Equivalent |
---|---|
|
a default-constructed |
memberref boost::mpi::group::rank |
|
memberref boost::mpi::group::translate_ranks |
|
operators |
|
|
operators |
|
operators |
|
operators |
operator |
|
operator |
|
operator |
|
unsupported |
|
unsupported |
|
used automatically in Boost.MPI |
Boost.MPI provides manipulation of communicators through the communicator
class.
Table 20.9. Communicator operations
C Function |
Boost.MPI Equivalent |
---|---|
operators |
|
|
|
|
|
used automatically in Boost.MPI |
Boost.MPI currently provides support for inter-communicators via the intercommunicator
class.
Table 20.10. Inter-communicator operations
C Function |
Boost.MPI Equivalent |
---|---|
|
|
|
|
Boost.MPI currently provides no support for attribute caching.
Table 20.11. Attributes and caching
C Function/Constant |
Boost.MPI Equivalent |
---|---|
|
unsupported |
|
unsupported |
|
unsupported |
unsupported |
|
unsupported |
|
unsupported |
|
unsupported |
|
unsupported |
|
unsupported |
|
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 20.12. Process topologies
C Function/Constant |
Boost.MPI Equivalent |
---|---|
|
unnecessary; use |
|
unnecessary; use |
unsupported |
|
unsupported |
|
|
|
unsupported |
|
unsupported |
|
unsupported |
|
unsupported |
|
unsupported |
|
unsupported |
|
unsupported |
|
unsupported |
Boost.MPI supports environmental inquires through the environment
class.
Table 20.13. Environmental inquiries
C Function/Constant |
Boost.MPI Equivalent |
---|---|
|
unnecessary; use |
|
unnecessary; use |
|
unnecessary; use |
Boost.MPI translates MPI errors into exceptions, reported via the exception
class.
Table 20.14. Error handling
C Function/Constant |
Boost.MPI Equivalent |
---|---|
|
unused; errors are translated into Boost.MPI exceptions |
|
unused; errors are translated into Boost.MPI exceptions |
unused; errors are translated into Boost.MPI exceptions |
|
unused; errors are translated into Boost.MPI exceptions |
|
unused; errors are translated into Boost.MPI exceptions |
|
unused; errors are translated into Boost.MPI exceptions |
|
used internally by Boost.MPI |
|
The MPI timing facilities are exposed via the Boost.MPI timer
class, which provides
an interface compatible with the Boost
Timer library.
Table 20.15. Timing facilities
C Function/Constant |
Boost.MPI Equivalent |
---|---|
|
unnecessary; use |
use |
|
MPI startup and shutdown are managed by the construction and descruction
of the Boost.MPI environment
class.
Table 20.16. Startup/shutdown facilities
C Function |
Boost.MPI Equivalent |
---|---|
|
|
|
|
Boost.MPI does not provide any support for the profiling facilities in MPI 1.1.