MPI_REDUCE_INIT(3)

📦

version 5.0.10 · 4 versions

💖 MPI_REDUCE_INIT(3) 💖

okay bestie, welcome to the Open MPI page for MPI_Reduce, MPI_Ireduce, and MPI_Reduce_init. This is giving major "let's reduce values across a group" energy, and we're gonna break it down. No cap.

✨ NAME ✨

MPI_Reduce, MPI_Ireduce, MPI_Reduce_init — reduces values on all processes within a group.

✨ SYNTAX ✨

🍓 C Syntax 🍓

#include <mpi.h>

int MPI_Reduce(const void *sendbuf, void *recvbuf, int count,
               MPI_Datatype datatype, MPI_Op op, int root,
               MPI_Comm comm)

int MPI_Ireduce(const void *sendbuf, void *recvbuf, int count,
                MPI_Datatype datatype, MPI_Op op, int root,
                MPI_Comm comm, MPI_Request *request)

int MPI_Reduce_init(const void *sendbuf, void *recvbuf, int count,
                MPI_Datatype datatype, MPI_Op op, int root,
                MPI_Comm comm, MPI_Info info, MPI_Request *request)

🧚 Fortran Syntax 🧚

USE MPI
! or the older form: INCLUDE 'mpif.h'
MPI_REDUCE(SENDBUF, RECVBUF, COUNT, DATATYPE, OP, ROOT, COMM,
             IERROR)
     <type>  SENDBUF(*), RECVBUF(*)
     INTEGER COUNT, DATATYPE, OP, ROOT, COMM, IERROR

MPI_IREDUCE(SENDBUF, RECVBUF, COUNT, DATATYPE, OP, ROOT, COMM,
            REQUEST, IERROR)
     <type>  SENDBUF(*), RECVBUF(*)
     INTEGER COUNT, DATATYPE, OP, ROOT, COMM, REQUEST, IERROR

MPI_REDUCE_INIT(SENDBUF, RECVBUF, COUNT, DATATYPE, OP, ROOT, COMM,
            INFO, REQUEST, IERROR)
     <type>  SENDBUF(*), RECVBUF(*)
     INTEGER COUNT, DATATYPE, OP, ROOT, COMM, INFO, REQUEST, IERROR

🦋 Fortran 2008 Syntax 🦋

USE mpi_f08
MPI_Reduce(sendbuf, recvbuf, count, datatype, op, root, comm, ierror)
     TYPE(*), DIMENSION(..), INTENT(IN) :: sendbuf
     TYPE(*), DIMENSION(..) :: recvbuf
     INTEGER, INTENT(IN) :: count, root
     TYPE(MPI_Datatype), INTENT(IN) :: datatype
     TYPE(MPI_Op), INTENT(IN) :: op
     TYPE(MPI_Comm), INTENT(IN) :: comm
     INTEGER, OPTIONAL, INTENT(OUT) :: ierror

MPI_Ireduce(sendbuf, recvbuf, count, datatype, op, root, comm, request,
             ierror)
     TYPE(*), DIMENSION(..), INTENT(IN), ASYNCHRONOUS :: sendbuf
     TYPE(*), DIMENSION(..), ASYNCHRONOUS :: recvbuf
     INTEGER, INTENT(IN) :: count, root
     TYPE(MPI_Datatype), INTENT(IN) :: datatype
     TYPE(MPI_Op), INTENT(IN) :: op
     TYPE(MPI_Comm), INTENT(IN) :: comm
     TYPE(MPI_Request), INTENT(OUT) :: request
     INTEGER, OPTIONAL, INTENT(OUT) :: ierror

MPI_Reduce_init(sendbuf, recvbuf, count, datatype, op, root, comm, info, request,
             ierror)
     TYPE(*), DIMENSION(..), INTENT(IN), ASYNCHRONOUS :: sendbuf
     TYPE(*), DIMENSION(..), ASYNCHRONOUS :: recvbuf
     INTEGER, INTENT(IN) :: count, root
     TYPE(MPI_Datatype), INTENT(IN) :: datatype
     TYPE(MPI_Op), INTENT(IN) :: op
     TYPE(MPI_Comm), INTENT(IN) :: comm
     TYPE(MPI_Info), INTENT(IN) :: info
     TYPE(MPI_Request), INTENT(OUT) :: request
     INTEGER, OPTIONAL, INTENT(OUT) :: ierror

🌸 INPUT PARAMETERS 🌸

sendbuf — address of the send buffer (choice). 💌
count — number of elements in send buffer (integer). 🔢
datatype — data type of elements of send buffer (handle). 🏷️
op — reduce operation (handle). ⚙️
root — rank of root process (integer). 👑
comm — communicator (handle). 📡
info — info (handle, persistent). ℹ️

🎀 OUTPUT PARAMETERS 🎀

recvbuf — address of receive buffer (choice, significant only at root). 📥
request — request (handle, non-blocking only). ⏳
ierror — Fortran only: error status (integer). 🚨

👑 DESCRIPTION 👑

okay so the global reduce functions — that's MPI_Reduce, MPI_Op_create, MPI_Op_free, MPI_Allreduce, MPI_Reduce_scatter, and MPI_Scan — do a global reduce operation (think sum, max, logical AND, etc.) across all the members of a group. The reduction can be either one of a predefined list of operations, or a user-defined operation. The global reduction functions come in several flavors: a reduce that returns the result of the reduction at one node, an all-reduce that returns this result at all nodes, and a scan (parallel prefix) operation. Plus, a reduce-scatter operation combines the functionality of a reduce and a scatter operation. 💅

MPI_Reduce combines the elements provided in the input buffer of each process in the group, using the operation op, and returns the combined value in the output buffer of the process with rank root. The input buffer is defined by the arguments sendbuf, count, and datatype; the output buffer is defined by the arguments recvbuf, count, and datatype. Both have the same number of elements, with the same type. The routine is called by all group members using the same arguments for count, datatype, op, root, and comm. So all processes provide input buffers and output buffers of the same length, with elements of the same type. Each process can provide one element, or a sequence of elements — in which case the combine operation is executed element-wise on each entry of the sequence. For example, if the operation is MPI_MAX and the send buffer contains two elements that are floating-point numbers (count = 2 and datatype = MPI_FLOAT), then recvbuf(1) = global max (sendbuf(1)) and recvbuf(2) = global max(sendbuf(2)). ✨

💅 USE OF IN-PLACE OPTION 💅

When the communicator is an intracommunicator, you can perform a reduce operation in-place — meaning the output buffer is used as the input buffer. Just use the variable MPI_IN_PLACE as the value of the root process's sendbuf. In this case, the input data is taken at the root from the receive buffer, where it will be replaced by the output data. 🔄

Note that MPI_IN_PLACE is a special kind of value; it has the same restrictions on its use as MPI_BOTTOM. 🚧

Because the in-place option converts the receive buffer into a send-and-receive buffer, a Fortran binding that includes INTENT must mark these as INOUT, not OUT. 🔁

🌈 WHEN COMMUNICATOR IS AN INTER-COMMUNICATOR 🌈

When the communicator is an inter-communicator, the root process in the first group combines data from all the processes in the second group and then performs the op operation. The first group defines the root process. That process uses MPI_ROOT as the value of its root argument. The remaining processes use MPI_PROC_NULL as the value of their root argument. All processes in the second group use the rank of that root process in the first group as the value of their root argument. Only the send buffer arguments are significant in the second group, and only the receive buffer arguments are significant in the root process of the first group. 🌟

✨ PREDEFINED REDUCE OPERATIONS ✨

okay so the whole menu of built-in ops that MPI hands you? It's right here, bestie. That section also lists exactly which datatypes each op is allowed to vibe with. And on top of the pre-made ones, you can literally cook up your own operations, overloaded to work on multiple datatypes — basic or derived. The full deets on that live in the writeups for the user-defined operations (go peek at the man pages for MPI_Op_create and MPI_Op_free). 🫶

Now the important lore: the operation op is always assumed to be associative. And every predefined operation is also assumed to be commutative. You, however, are free to define operations that are associative but not commutative. The canonical evaluation order of a reduction is decided by the ranks of the processes in the group — but the implementation can absolutely take advantage of associativity, or associativity and commutativity, to switch up the order of evaluation. Which means: for ops that aren't strictly associative and commutative, the result of the reduction can change. Floating point addition is the classic culprit here. 💀

Predefined operators only work with the MPI types listed below (in Predefined Reduce Operations, plus the MINLOC and MAXLOC section further down). User-defined operators, on the other hand, can operate on general, derived datatypes. When that happens, each argument the reduce operation gets applied to is one element described by that datatype — and that element can totes contain several basic values. More on that in Section 4.9.4 of the MPI Standard, “User-Defined Operations.” 📖

🌈 the ops themselves 🌈

These are the predefined operations supplied for MPI_Reduce and its little friends MPI_Allreduce, MPI_Reduce_scatter, and MPI_Scan. You invoke them by dropping the following into op:

MPI_MAX — maximum
MPI_MIN — minimum
MPI_SUM — sum
MPI_PROD — product
MPI_LAND — logical and
MPI_BAND — bit-wise and
MPI_LOR — logical or
MPI_BOR — bit-wise or
MPI_LXOR — logical xor
MPI_BXOR — bit-wise xor
MPI_MAXLOC — max value and location
MPI_MINLOC — min value and location

The two divas MPI_MINLOC and MPI_MAXLOC get their own dedicated moment below (MINLOC and MAXLOC) because they're built different. For all the other predefined ops, here's the full rundown of which op + datatype combos are actually allowed. 👑

🧺 datatype groups (iykyk) 🧺

First, let's group the basic MPI datatypes like this:

C integer: MPI_INT, MPI_LONG, MPI_SHORT, MPI_UNSIGNED_SHORT, MPI_UNSIGNED, MPI_UNSIGNED_LONG
Fortran integer: MPI_INTEGER
Floating-point: MPI_FLOAT, MPI_DOUBLE, MPI_REAL, MPI_DOUBLE_PRECISION, MPI_LONG_DOUBLE
Logical: MPI_LOGICAL
Complex: MPI_COMPLEX
Byte: MPI_BYTE

🎀 which types each op fw 🎀

so now that we have our little groups, here's the valid datatypes for every option:

MPI_MAX, MPI_MIN — C integer, Fortran integer, floating-point
MPI_SUM, MPI_PROD — C integer, Fortran integer, floating-point, complex
MPI_LAND, MPI_LOR, MPI_LXOR — C integer, logical
MPI_BAND, MPI_BOR, MPI_BXOR — C integer, Fortran integer, byte

🌸 Example 1 🌸

A routine that computes the dot product of two vectors that are distributed across a group of processes and returns the answer at process zero. iconic behavior tbh.

SUBROUTINE PAR_BLAS1(m, a, b, c, comm)
REAL a(m), b(m)       ! local slice of array
REAL c                ! result (at process zero)
REAL sum
INTEGER m, comm, i, ierr

! local sum
sum = 0.0
DO i = 1, m
   sum = sum + a(i)*b(i)
END DO

! global sum
CALL MPI_REDUCE(sum, c, 1, MPI_REAL, MPI_SUM, 0, comm, ierr)
RETURN

🌟 Example 2 🌟

A routine that computes the product of a vector and an array that are distributed across a group of processes and returns the answer at process zero. slay.

SUBROUTINE PAR_BLAS2(m, n, a, b, c, comm)
REAL a(m), b(m,n)    ! local slice of array
REAL c(n)            ! result
REAL sum(n)
INTEGER n, comm, i, j, ierr

! local sum
DO j= 1, n
  sum(j) = 0.0
  DO i = 1, m
    sum(j) = sum(j) + a(i)*b(i,j)
  END DO
END DO

! global sum
CALL MPI_REDUCE(sum, c, n, MPI_REAL, MPI_SUM, 0, comm, ierr)

! return result at process zero (and garbage at the other nodes)
RETURN

💫 MINLOC AND MAXLOC 💫

ok so the operator MPI_MINLOC is literally "find me the global minimum AND also an index glued onto that minimum value" energy. MPI_MAXLOC is the same deal but for the max. 🌸 One absolute classic use case: compute a global minimum (or maximum) and the rank of the process that's holding that value. main character identification, honestly.

The operation that defines MPI_MAXLOC is

            ( u )    (  v )      ( w )
            (   )  o (    )   =  (   )
            ( i )    (  j )      ( k )

where

    w = max(u, v)

and

            ( i            if u > v
            (
      k   = ( min(i, j)    if u = v
            (
            (  j           if u < v)

MPI_MINLOC is defined similarly:

            ( u )    (  v )      ( w )
            (   )  o (    )   =  (   )
            ( i )    (  j )      ( k )

where

    w = min(u, v)

and

            ( i            if u < v
            (
      k   = ( min(i, j)    if u = v
            (
            (  j           if u > v)

Both operations are associative and commutative. 💖 Big note bestie: if MPI_MAXLOC is applied to reduce a sequence of pairs (u(0), 0), (u(1), 1), …, (u(n-1), n-1), then the value returned is (u , r), where u= max(i) u(i) and r is the index of the first global maximum in the sequence. So like, if every process supplies a value plus its rank within the group, then a reduce with op = MPI_MAXLOC hands you back the maximum value and the rank of the first process that has it. Similarly, MPI_MINLOC gets you a minimum and its index. More generally, MPI_MINLOC computes a lexicographic minimum, where elements are ordered by the first component of each pair, and ties get broken by the second component. 🎀

The reduce operation is defined to work on arguments that consist of a pair: value and index. For both Fortran and C, types are provided to describe that pair. The whole potentially-mixed-type situation is a problem in Fortran. It gets circumvented, for Fortran, by having the MPI-provided type consist of a pair of the same type as value, and coercing the index to that type too. In C, the MPI-provided pair type has distinct types and the index is an int. ✨

To use MPI_MINLOC and MPI_MAXLOC in a reduce operation, you gotta provide a datatype argument that represents a pair (value and index). MPI provides nine such predefined datatypes. MPI_MAXLOC and MPI_MINLOC can be used with each of these:

Fortran:

MPI_2REAL — pair of REALs
MPI_2DOUBLE_PRECISION — pair of DOUBLE-PRECISION variables
MPI_2INTEGER — pair of INTEGERs

MPI_FLOAT_INT — float and int
MPI_DOUBLE_INT — double and int
MPI_LONG_INT — long and int
MPI_2INT — pair of ints
MPI_SHORT_INT — short and int
MPI_LONG_DOUBLE_INT — long double and int

The data type MPI_2REAL is equivalent to:

MPI_TYPE_CONTIGUOUS(2, MPI_REAL, MPI_2REAL)

Similar statements apply for MPI_2INTEGER, MPI_2DOUBLE_PRECISION, and MPI_2INT. 🦋

The datatype MPI_FLOAT_INT is as if it were defined by this sequence of instructions:

type[0] = MPI_FLOAT
type[1] = MPI_INT
disp[0] = 0
disp[1] = sizeof(float)
block[0] = 1
block[1] = 1
MPI_TYPE_STRUCT(2, block, disp, type, MPI_FLOAT_INT)

Similar statements apply for MPI_LONG_INT and MPI_DOUBLE_INT. 💜

Example 3: each process has an array of 30 doubles, in C. For each of the 30 locations, compute the value and rank of the process containing the largest value. 🔥

...
/* each process has an array of 30 double: ain[30]
 */
double ain[30], aout[30];
int  ind[30];
struct {
    double val;
    int   rank;
} in[30], out[30];
int i, myrank, root;

MPI_Comm_rank(MPI_COMM_WORLD, &myrank);
for (i=0; i<30; ++i) {
    in[i].val = ain[i];
    in[i].rank = myrank;
}
MPI_Reduce( in, out, 30, MPI_DOUBLE_INT, MPI_MAXLOC, root, comm );
/* At this point, the answer resides on process root
 */
if (myrank == root) {
    /* read ranks out
     */
    for (i=0; i<30; ++i) {
        aout[i] = out[i].val;
        ind[i] = out[i].rank;
    }
}

Example 4: same example, in Fortran. 🧚

...
! each process has an array of 30 double: ain(30)

DOUBLE PRECISION :: ain(30), aout(30)
INTEGER :: ind(30)
DOUBLE PRECISION :: in(2,30), out(2,30)
INTEGER :: i, myrank, root, ierr

call MPI_COMM_RANK(MPI_COMM_WORLD, myrank)
DO I=1, 30
    in(1,i) = ain(i)
    in(2,i) = myrank    ! myrank is coerced to a double
END DO

call MPI_REDUCE( in, out, 30, MPI_2DOUBLE_PRECISION, MPI_MAXLOC, root, &
                                                          comm, ierr )
! At this point, the answer resides on process root

IF (myrank == root) THEN
    ! read ranks out
    DO I= 1, 30
        aout(i) = out(1,i)
        ind(i) = out(2,i)  ! rank is coerced back to an integer
    END DO
END IF

Example 5: each process has a nonempty array of values. Find the minimum global value, the rank of the process that holds it, and its index on this process. 👑

#define  LEN   1000

float val[LEN];        /* local array of values */
int count;             /* local number of values */
int myrank, minrank, minindex;
float minval;

struct {
    float value;
    int   index;
} in, out;

/* local minloc */
in.value = val[0];
in.index = 0;
for (i=1; i < count; i++)
    if (in.value > val[i]) {
        in.value = val[i];
        in.index = i;
    }

/* global minloc */
MPI_Comm_rank(MPI_COMM_WORLD, &myrank);
in.index = myrank*LEN + in.index;
MPI_Reduce( in, out, 1, MPI_FLOAT_INT, MPI_MINLOC, root, comm );
    /* At this point, the answer resides on process root
     */
if (myrank == root) {
    /* read answer out
     */
    minval = out.value;
    minrank = out.index / LEN;
    minindex = out.index % LEN;

All MPI objects (e.g., MPI_Datatype, MPI_Comm) are of type INTEGER in Fortran. 🫶

🫠 NOTES ON COLLECTIVE OPERATIONS 🫠

The reduction functions (MPI_Op) do not return an error value. So like, if the functions detect an error, literally all they can do is either call MPI_Abort or silently skip the problem. 🙈 That means if you change the error handler from MPI_ERRORS_ARE_FATAL to something else — say, MPI_ERRORS_RETURN — then no error may be indicated. periodt.

The reason for this is the performance problems in making sure all collective routines return the same error value. 💀

😭 ERRORS 😭

Almost all MPI routines return an error value; C routines as the return result of the function and Fortran routines in the last argument.

Before the error value is returned, the current MPI error handler associated with the communication object (e.g., communicator, window, file) is called. If no communication object is associated with the MPI call, then the call is considered attached to MPI_COMM_SELF and will call the associated MPI error handler. When MPI_COMM_SELF is not initialized (i.e., before MPI_Init/MPI_Init_thread, after MPI_Finalize, or when using the Sessions Model exclusively) the error raises the initial error handler. The initial error handler can be changed by calling MPI_Comm_set_errhandler on MPI_COMM_SELF when using the World model, or the mpi_initial_errhandler CLI argument to mpiexec or info key to MPI_Comm_spawn/MPI_Comm_spawn_multiple. If no other appropriate error handler has been set, then the MPI_ERRORS_RETURN error handler is called for MPI I/O functions and the MPI_ERRORS_ABORT error handler is called for all other MPI functions. 🌈

Open MPI includes three predefined error handlers that can be used:

MPI_ERRORS_ARE_FATAL — causes the program to abort all connected MPI processes. 💀
MPI_ERRORS_ABORT — an error handler that can be invoked on a communicator, window, file, or session. When called on a communicator, it acts as if MPI_Abort was called on that communicator. If called on a window or file, it acts as if MPI_Abort was called on a communicator containing the group of processes in the corresponding window or file. If called on a session, it aborts only the local process.
MPI_ERRORS_RETURN — returns an error code to the application. ✨

MPI applications can also implement their own error handlers by calling:

MPI_Comm_create_errhandler then MPI_Comm_set_errhandler
MPI_File_create_errhandler then MPI_File_set_errhandler
MPI_Session_create_errhandler then MPI_Session_set_errhandler or at MPI_Session_init
MPI_Win_create_errhandler then MPI_Win_set_errhandler

Note that MPI does not guarantee that an MPI program can continue past an error. 😭

See the MPI man page for a full list of MPI error codes.

See the Error Handling section of the MPI-3.1 standard for more information. 📚

👑 Copyright 👑

2003-2026, The Open MPI Community 💖

and that's a wrap bestie!! 🎀 this was MPI_REDUCE_INIT(3) from Open MPI, dated Sep 10, 2026 — go forth and reduce, you absolute icon 💅✨🦋

📦 where this page comes from

ubuntu stonking: openmpi-doc 5.0.10-6 · other
💖 girlified by deepseek-ai/DeepSeek-V4.1-Flash on 2026-11-03: a language model's rewrite of the original, under its terms