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.
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\fIfunction\fP is the user-defined function, which must have the following four arguments: invec, inoutvec, len, and datatype.
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The ANSI-C prototype for the function is the following:
The Fortran declaration of the user-defined function appears below.
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FUNCTION USER_FUNCTION( INVEC(*), INOUTVEC(*), LEN, TYPE)
<type> INVEC(LEN), INOUTVEC(LEN)
INTEGER LEN, TYPE
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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.
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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.
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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.
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General datatypes may be passed to the user function. However, use of datatypes that are not contiguous is likely to lead to inefficiencies.
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No MPI communication function may be called inside the user function.
MPI_Abort may be called inside the function in case of an error.
.SHNOTES
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.
\fBExample:\fP Example of user-defined reduce:
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Compute the product of an array of complex numbers, in C.
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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
*/
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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.
.SHNOTESONCOLLECTIVEOPERATIONS
The reduction functions (
.IMPI_Op
) do not return an error value. As a result,
if the functions detect an error, all they can do is either call
.IMPI_Abort
or silently skip the problem. Thus, if you change the error handler from
.IMPI_ERRORS_ARE_FATAL
to something else, for example,
.IMPI_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.
.SHERRORS
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.
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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.
