MPI_Op_create(3)                   Open MPI                   MPI_Op_create(3)



NAME
       MPI_Op_create - Creates a user-defined combination function handle.


SYNTAX
C Syntax
       #include <mpi.h>
       int MPI_Op_create(MPI_User_function *function, int commute,
            MPI_Op *op)


Fortran Syntax
       INCLUDE 'mpif.h'
       MPI_OP_CREATE(FUNCTION, COMMUTE, OP, IERROR)
            EXTERNAL  FUNCTION
            LOGICAL   COMMUTE
            INTEGER   OP, IERROR


C++ Syntax
       #include <mpi.h>
       void Op::Init(User function* function, bool commute)


INPUT PARAMETERS
       function  User-defined function (function).

       commute   True if commutative; false otherwise.


OUTPUT PARAMETERS
       op        Operation (handle).

       IERROR    Fortran only: Error status (integer).


DESCRIPTION
       MPI_Op_create binds a user-defined global operation to an op handle
       that can subsequently be used in MPI_Reduce, MPI_Allreduce,
       MPI_Reduce_scatter, and  MPI_Scan. The user-defined operation is
       assumed to be associative. If commute = true, then the operation should
       be both commutative and associative. If commute = false, then the order
       of operands is fixed and is defined to be in ascending, process rank
       order, beginning with process zero. The order of evaluation can be
       changed, taking advantage of the associativity of the operation. If
       commute = true then the order of evaluation can be changed, taking
       advantage of commutativity and associativity.

       function is the user-defined function, which must have the following
       four arguments: invec, inoutvec, len, and datatype.


       The ANSI-C prototype for the function is the following:

         typedef void MPI_User_function(void *invec, void *inoutvec,
                                        int *len,
                                        MPI_Datatype *datatype);

       The Fortran declaration of the user-defined function appears below.

         FUNCTION USER_FUNCTION( INVEC(*), INOUTVEC(*), LEN, TYPE)
         <type> INVEC(LEN), INOUTVEC(LEN)
          INTEGER LEN, TYPE

       The datatype argument is a handle to the data type that was passed into
       the call to MPI_Reduce. The user reduce function should be written such
       that the following holds: Let u[0], ..., u[len-1] be the len elements
       in the communication buffer described by the arguments invec, len, and
       datatype when the function is invoked; let v[0], ..., v[len-1] be len
       elements in the communication buffer described by the arguments
       inoutvec, len, and datatype when the function is invoked; let
       w[0], ..., w[len-1] be len elements in the communication buffer
       described by the arguments inoutvec, len, and datatype when the
       function returns; then w[i] = u[i] o v[i], for i=0 ,..., len-1, where o
       is the reduce operation that the function computes.

       Informally, we can think of invec and inoutvec as arrays of len
       elements that function is combining. The result of the reduction over-
       writes values in inoutvec, hence the name. Each invocation of the
       function results in the pointwise evaluation of the reduce operator on
       len elements: i.e, the function returns in inoutvec[i] the value
       invec[i] o inoutvec[i], for i = 0..., count-1, where o is the combining
       operation computed by the function.

       By internally comparing the value of the datatype argument to known,
       global handles, it is possible to overload the use of a single user-
       defined function for several different data types.

       General datatypes may be passed to the user function. However, use of
       datatypes that are not contiguous is likely to lead to inefficiencies.

       No MPI communication function may be called inside the user function.
       MPI_Abort may be called inside the function in case of an error.


NOTES
       Suppose one defines a library of user-defined reduce functions that are
       overloaded: The datatype argument is used to select the right execution
       path at each invocation, according to the types of the operands. The
       user-defined reduce function cannot "decode" the datatype argument that
       it is passed, and cannot identify, by itself, the correspondence
       between the datatype handles and the datatype they represent. This
       correspondence was established when the datatypes were created. Before
       the library is used, a library initialization preamble must be
       executed. This preamble code will define the datatypes that are used by
       the library and store handles to these datatypes in global, static
       variables that are shared by the user code and the library code.

       Example: Example of user-defined reduce:

       Compute the product of an array of complex numbers, in C.

           typedef struct {
               double real,imag;
           } Complex;

           /* the user-defined function
            */
           void myProd( Complex *in, Complex *inout, int *len,
                        MPI_Datatype *dptr )
           {
               int i;
               Complex c;

           for (i=0; i< *len; ++i) {
                   c.real = inout->real*in->real -
                              inout->imag*in->imag;
                   c.imag = inout->real*in->imag +
                              inout->imag*in->real;
                   *inout = c;
                   in++; inout++;
               }
           }

           /* and, to call it...
            */
           ...

           /* each process has an array of 100 Complexes
                */
               Complex a[100], answer[100];
               MPI_Op myOp;
               MPI_Datatype ctype;

           /* explain to MPI how type Complex is defined
                */
              MPI_Type_contiguous( 2, MPI_DOUBLE, &ctype );
               MPI_Type_commit( &ctype );
               /* create the complex-product user-op
                */
               MPI_Op_create( myProd, True, &myOp );

               MPI_Reduce( a, answer, 100, ctype, myOp, root, comm );

               /* At this point, the answer, which consists of 100 Complexes,
                * resides on process root
                */

       The Fortran version of MPI_Reduce will invoke a user-defined reduce
       function using the Fortran calling conventions and will pass a Fortran-
       type datatype argument; the C version will use C calling convention and
       the C representation of a datatype handle. Users who plan to mix
       languages should define their reduction functions accordingly.


NOTES ON COLLECTIVE OPERATIONS
       The reduction functions ( MPI_Op ) do not return an error value.  As a
       result, if the functions detect an error, all they can do is either
       call MPI_Abort or silently skip the problem.  Thus, if you change the
       error handler from MPI_ERRORS_ARE_FATAL to something else, for example,
       MPI_ERRORS_RETURN , then no error may be indicated.

       The reason for this is the performance problems in ensuring that all
       collective routines return the same error value.


ERRORS
       Almost all MPI routines return an error value; C routines as the value
       of the function and Fortran routines in the last argument. C++
       functions do not return errors. If the default error handler is set to
       MPI::ERRORS_THROW_EXCEPTIONS, then on error the C++ exception mechanism
       will be used to throw an MPI:Exception object.

       Before the error value is returned, the current MPI error handler is
       called. By default, this error handler aborts the MPI job, except for
       I/O function errors. The error handler may be changed with
       MPI_Comm_set_errhandler; the predefined error handler MPI_ERRORS_RETURN
       may be used to cause error values to be returned. Note that MPI does
       not guarantee that an MPI program can continue past an error.


SEE ALSO
       MPI_Reduce
       MPI_Reduce_scatter
       MPI_Allreduce
       MPI_Scan
       MPI_Op_free




1.4.1                            Jan 14, 2010                 MPI_Op_create(3)
