# MPI topic: Collectives

##### Experimental html version of downloadable textbook, see http://www.tacc.utexas.edu/~eijkhout/istc/istc.html
3.1.1 : Practical use of collectives
3.1.2 : Synchronization
3.1.3 : Collectives in MPI
3.2 : Reduction
3.2.1 : Reduce to all
3.2.2 : Inner product as allreduce
3.2.3 : Reduction operations
3.2.4 : Data buffers
3.3 : Rooted collectives: broadcast, reduce
3.3.1 : Reduce to a root
3.3.2 : Reduce in place
3.3.3 : Broadcast
3.4 : Rooted collectives: gather and scatter
3.4.1 : Examples
3.4.2 : Allgather
3.5 : All-to-all
3.5.1 : All-to-all as data transpose
3.5.2 : All-to-all-v
3.6 : Reduce-scatter
3.6.1 : Examples
3.7 : Barrier
3.8 : Variable-size-input collectives
3.8.1 : Example of Gatherv
3.8.2 : Example of Allgatherv
3.8.3 : Variable all-to-all
3.9 : Scan operations
3.9.1 : Exclusive scan
3.9.2 : Use of scan operations
3.10 : MPI Operators
3.10.1 : Pre-defined operators
3.10.1.1 : Minloc and maxloc
3.10.2 : User-defined operators
3.10.3 : Local reduction
3.11 : Non-blocking collectives
3.11.1 : Examples
3.11.1.1 : Array transpose
3.11.1.2 : Stencils
3.11.2 : Non-blocking barrier
3.12 : Performance of collectives
3.13 : Collectives and synchronization
3.14 : Performance considerations
3.14.1 : Scalability
3.14.2 : Complexity and scalability of collectives
3.14.2.1 : Broadcast
3.15 : Review questions
Back to Table of Contents

# 3 MPI topic: Collectives

A certain class of MPI routines are called collective', or more correctly: collective on a communicator'. This means that if process one in that communicator calls that routine, they all need to call that routine. In this chapter we will discuss collective routines that are about combining the data on all processes in that communicator, but there are also operations such as opening a shared file that are collective, which will be discussed in a later chapter.

## 3.1 Working with global information

crumb trail: > mpi-collective > Working with global information

If all processes have individual data, for instance the result of a local computation, you may want to bring that information together, for instance to find the maximal computed value or the sum of all values. Conversely, sometimes one processor has information that needs to be shared with all. For this sort of operation, MPI has collectives .

There are various cases, illustrated in figure  labelstring ,

which you can (sort of) motivate by considering some classroom activities:

• The teacher tells the class when the exam will be. This is a broadcast : the same item of information goes to everyone.

• After the exam, the teacher performs a gather operation to collect the invidivual exams.
• On the other hand, when the teacher computes the average grade, each student has an individual number, but these are now combined to compute a single number. This is a reduction .
• Now the teacher has a list of grades and gives each student their grade. This is a scatter operation, where one process has multiple data items, and gives a different one to all the other processes.

This story is a little different from what happens with MPI processes, because these are more symmetric; the process doing the reducing and broadcasting is no different from the others. Any process can function as the root process in such a collective.

Exercise

How would you realize the following scenarios with MPI collectives?

• Let each process compute a random number. You want to print the maximum of these numbers to your screen.

• Each process computes a random number again. Now you want to scale these numbers by their maximum.
• Let each process compute a random number. You want to print on what processor the maximum value is computed.

### 3.1.1 Practical use of collectives

Collectives are quite common in scientific applications. For instance, if one process reads data from disc or the commandline, it can use a broadcast or a gather to get the information to other processes. Likewise, at the end of a program run, a gather or reduction can be used to collect summary information about the program run.

However, a more common scenario is that the result of a collective is needed on all processes.

Consider the computation of the standard deviation : $\sigma = \sqrt{\frac1{N-1} \sum_i^N (x_i-\mu)^2 } \qquad\hbox{where}\qquad \mu = \frac{\sum_i^Nx_i}N$ and assume that every process stores just one $x_i$ value.

1. The calculation of the average $\mu$ is a reduction, since all the distributed values need to be added.

2. Now every process needs to compute $x_i-\mu$ for its value $x_i$, so $\mu$ is needed everywhere. You can compute this by doing a reduction followed by a broadcast, but it is better to use a so-called allreduce operation, which does the reduction and leaves the result on all processors.
3. The calculation of $\sum_i(x_i-\mu)$ is another sum of distributed data, so we need another reduction operation. Depending on whether each process needs to know $\sigma$, we can again use an allreduce.

As another examples, if $x,y$ are distributed vector objects, and you want to compute $y- (x^ty)x$ which is part of the Gramm-Schmidt algorithm; see  Eijkhout:IntroHPC . Again you need to use an allreduce to reduce the inner product value on all processors.

// compute local value
localvalue = innerproduct( x[ localpart], y[ localpart ] );
// compute inner product on the every process
AllReduce( localvalue, reducedvalue );


### 3.1.2 Synchronization

crumb trail: > mpi-collective > Working with global information > Synchronization

Collectives are operations that involve all processes in a communicator. %%(See section  for an informal listing.) A collective is a single call, and it blocks on all processors, meaning that a process calling a collective cannot proceed until the other processes have similarly called the collective.

That does not mean that all processors exit the call at the same time: because of implementational details and network latency they need not be synchronized in their execution. However, semantically we can say that a process can not finish a collective until every other process has at least started the collective.

In addition to these collective operations, there are operations that are said to be collective on their communicator', but which do not involve data movement. Collective then means that all processors must call this routine; not to do so is an error that will manifest itself in hanging' code. One such example is MPI_File_open .

### 3.1.3 Collectives in MPI

crumb trail: > mpi-collective > Working with global information > Collectives in MPI

We will now explain the MPI collectives in the following order.

• [Allreduce] We use the allreduce as an introduction to the concepts behind collectives; section  3.2.1 . As explained above, this routines serves many practical scenarios.

• [Broadcast and reduce] We then introduce the concept of a root in the reduce (section  3.3.1 ) and broadcast (section  3.3.3 ) collectives.
• [Gather and scatter] The gather/scatter collectives deal with more than a single data item.

There are more collectives or variants on the above.

• If you want to gather or scatter information, but the contribution of each processor is of a different size, there are variable' collectives; they have a  v in the name (section  3.8 ).

• Sometimes you want a reduction with partial results, where each processor computes the sum (or other operation) on the values of lower-numbered processors. For this, you use a scan collective (section  3.9 ).
• If every processor needs to broadcast to every other, you use an all-to-all operation (section  3.5 ).
• A barrier is an operation that makes all processes wait until every process has reached the barrier (section  3.7 ).

Finally, there are some advanced topics in collectives.

• User-defined reduction operators; section  3.10.2 .

• Non-blocking collectives; section  3.11 .

## 3.2 Reduction

crumb trail: > mpi-collective > Reduction

### 3.2.1 Reduce to all

crumb trail: > mpi-collective > Reduction > Reduce to all

Above we saw a couple of scenarios where a quantity is reduced, with all proceses getting the result. The MPI call for this is MPI_Allreduce

C:
int MPI_Allreduce(const void* sendbuf,
void* recvbuf, int count, MPI_Datatype datatype,
MPI_Op op, MPI_Comm comm)

Semantics:
IN sendbuf: starting address of send buffer (choice)
OUT recvbuf: starting address of receive buffer (choice)
IN count: number of elements in send buffer (non-negative integer)
IN datatype: data type of elements of send buffer (handle)
IN op: operation (handle)
IN comm: communicator (handle)

Fortran:
MPI_Allreduce(sendbuf, recvbuf, count, datatype, op, comm, ierror)
TYPE(*), DIMENSION(..), INTENT(IN) :: sendbuf
TYPE(*), DIMENSION(..) :: recvbuf
INTEGER, INTENT(IN) :: count
TYPE(MPI_Datatype), INTENT(IN) :: datatype
TYPE(MPI_Op), INTENT(IN) :: op
TYPE(MPI_Comm), INTENT(IN) :: comm
INTEGER, OPTIONAL, INTENT(OUT) :: ierror

Python native:
recvobj = MPI.Comm.allreduce(self, sendobj, op=SUM)
Python numpy:
MPI.Comm.Allreduce(self, sendbuf, recvbuf, Op op=SUM)

MPL:
template
void mpl::communicator::allreduce
( F,	const T &, T & ) const;
( F,	const T *, T *,
const contiguous_layout< T > & ) const;
( F,	T & ) const;
( F,	T *, const contiguous_layout< T > & ) const;
F : reduction function
T : type

.

Example: we give each process a random number, and sum these numbers together. The result should be approximately $1/2$ times the number of processes.

// allreduce.c
float myrandom,sumrandom;
myrandom = (float) rand()/(float)RAND_MAX;
// add the random variables together
MPI_Allreduce(&myrandom,&sumrandom,
1,MPI_FLOAT,MPI_SUM,comm);
// the result should be approx nprocs/2:
if (procno==nprocs-1)
printf("Result %6.9f compared to .5\n",sumrandom/nprocs);


For Python we illustrate both the native and the numpy variant. In the numpy variant we create an array for the receive buffer, even though only one element is used.

## allreduce.py
random_number = random.randint(1,nprocs*nprocs)
# native mode send
max_random = comm.allreduce(random_number,op=MPI.MAX)
myrandom = np.empty(1,dtype=np.int)
myrandom[0] = random_number
allrandom = np.empty(nprocs,dtype=np.int)
# numpy mode send
comm.Allreduce(myrandom,allrandom[:1],op=MPI.MAX)


Exercise

Let each process compute a random number, and compute the sum of these numbers using the MPI_Allreduce routine. $\xi = \sum_i x_i$

Each process then scales its value by this sum. $x_i' \leftarrow x_i/ \xi$ Compute the sum of the scaled numbers $\xi' = \sum_i x_i'$ and check that it is 1.

MPL note

In MPL , the reduction operator is a std::function :

float
xrank = static_cast( comm_world.rank() ),
xreduce;
// separate recv buffer
comm_world.allreduce(mpl::plus(), xrank,xreduce);
// in place
comm_world.allreduce(mpl::plus(), xrank);
if ( comm_world.rank()==comm_world.size()-1 )
std::cout << "Allreduce got: separate=" << xreduce
<< ", inplace=" << xrank << std::endl;

(Note the parentheses after the operator.)

For more about operators, see section  3.10 .

### 3.2.2 Inner product as allreduce

crumb trail: > mpi-collective > Reduction > Inner product as allreduce

One of the more common applications of the reduction operation is the inner product computation. Typically, you have two vectors $x,y$ that have the same distribution, that is, where all processes store equal parts of $x$ and $y$. The computation is then

local_inprod = 0;
for (i=0; i<localsize; i++)
local_inprod += x[i]*y[i];
MPI_Allreduce( &local_inprod, &global_inprod, 1,MPI_DOUBLE ... )


Exercise

The Gram-Schmidt method is a simple way to orthogonalize two vectors: $u \leftarrow u- (u^tv)/(u^tu)$ Implement this, and check that the result is indeed orthogonal.

### 3.2.3 Reduction operations

crumb trail: > mpi-collective > Reduction > Reduction operations

Several MPI_Op values are pre-defined. For the list, see section  3.10.1 .

For use in reductions and scans it is possible to define your own operator.

MPI_Op_create( MPI_User_function *func, int commute, MPI_Op *op);


For more details, see section  3.10.2 .

### 3.2.4 Data buffers

crumb trail: > mpi-collective > Reduction > Data buffers

Collectives are the first example you see of MPI routines that involve transfer of user data. Here, and in every other case, you see that the data description involves:

• A buffer. This can be a scalar or an array.

• A datatype. This describes whether the buffer contains integers, single/double floating point numbers, or more complicated types, to be discussed later.
• A count. This states how many of the elements of the given datatype are in the send buffer, or will be received into the receive buffer.

These three together describe what MPI needs to send through the network.

In the various languages such a buffer/count/datatype triplet is specified in different ways.

First of all, in  C the buffer is always an opaque handle , that is, a void* parameter to which you supply an address. This means that an MPI call can take two forms. For scalars:

float x,y;
MPI_Allreduce( &x,&y,1,MPI_FLOAT, ... );


and for longer buffers:

float xx[2],yy[2];
MPI_Allreduce( xx,yy,2,MPI_FLOAT, ... );


You could cast the buffers and write:

MPI_Allreduce( (void*)&x,(void*)&y,1,MPI_FLOAT, ... );
MPI_Allreduce( (void*)xx,(void*)yy,2,MPI_FLOAT, ... );


but that is not necessary. The compiler will not complain if you leave out the cast.

In  Fortran parameters are always passed by reference, so the buffer is treated the same way:

Real*4 :: x
Real*4,dimension(2) :: xx
call MPI_Allreduce( x,1,MPI_REAL4, ... )
call MPI_Allreduce( xx,2,MPI_REAL4, ... )


In discussing OO languages, we first note that the official C++ API has been removed from the standard.

Specification of the buffer/count/datatype triplet is not needed explicitly in OO languages.

Python note

In Python, all buffers are numpy objects, which carry information about their type and size.

x = numpy.zeros(1,dtype=np.float32)
comm.Allreduce( x )
xs = numpy.zeros(2,dtype=np.float64)
comm.Allreduce( xx )


MPL note

The C++ package MPL uses polymorphism in its buffer handling, in addition to the templating mechanism. Thus, there are polymorphic methods for scalars, and for buffers. For the latter, a layout specification is needed.

float x;
comm.Allreduce( x );
float xx[2];
comm.Allreduce( xx, mpl::contiguous_layout<float>(2) );


The contiguous_layout this will be discussed in more detail in section  5.3 . For now, interpret it as a way of indicating the count/type part of a buffer specification.

If your buffer is a std::vector you need to take the .data() component of it:
vector<float> xx(2);
comm.Allreduce( xx.data(), mpl::contiguous_layout<float>(2) );


C++ has improved handling of static array s, so MPL can send those without further layout specification:

// sendarray.cxx
double v[2][2][2];
comm_world.send(v, 1);  // send to rank 1
comm_world.recv(v, 0);  // receive from rank 0


## 3.3 Rooted collectives: broadcast, reduce

crumb trail: > mpi-collective > Rooted collectives: broadcast, reduce

In some scenarios there is a certain process that has a privileged status.

• One process can generate or read in the initial data for a program run. This then needs to be communicated to all other processes.

• At the end of a program run, often one process needs to output some summary information.

This process is called the root process, and we will now consider routines that have a root.

### 3.3.1 Reduce to a root

crumb trail: > mpi-collective > Rooted collectives: broadcast, reduce > Reduce to a root

In the broadcast operation a single data item was communicated to all processes. A reduction operation with MPI_Reduce

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

Fortran:
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

Python:
native:
comm.reduce(self, sendobj=None, recvobj=None, op=SUM, int root=0)
numpy:
comm.Reduce(self, sendbuf, recvbuf, Op op=SUM, int root=0)

goes the other way: each process has a data item, and these are all brought together into a single item.

Here are the essential elements of a reduction operation:

MPI_Reduce( senddata, recvdata..., operator,
root, comm );

• There is the original data, and the data resulting from the reduction. It is a design decision of MPI that it will not by default overwrite the original data. The send data and receive data are of the same size and type: if every processor has one real number, the reduced result is again one real number.

• It is possible to indicate explicitly that a single buffer is used, and thereby the original data overwritten; see section  3.3.2 for this in place' mode.
• There is a reduction operator. Popular choices are MPI_SUM , MPI_PROD and MPI_MAX , but complicated operators such as finding the location of the maximum value exist. (For the full list, see section  3.10.1 .) You can also define your own operators; section  3.10.2 .
• There is a root process that receives the result of the reduction. Since the non-root processes do not receive the reduced data, they can actually leave the receive buffer undefined.

// reduce.c
float myrandom = (float) rand()/(float)RAND_MAX,
result;
int target_proc = nprocs-1;
// add all the random variables together
MPI_Reduce(&myrandom,&result,1,MPI_FLOAT,MPI_SUM,
target_proc,comm);
// the result should be approx nprocs/2:
if (procno==target_proc)
printf("Result %6.3f compared to nprocs/2=%5.2f\n",
result,nprocs/2.);


Exercise

Write a program where each process computes a random number, and process 0 finds and prints the maximum generated value. Let each process print its value, just to check the correctness of your program.

(See  Random number generation for a discussion of random number generation.)

Collective operations can also take an array argument, instead of just a scalar. In that case, the operation is applied pointwise to each location in the array.

Exercise

Create on each process an array of length 2 integers, and put the values $1,2$ in it on each process. Do a sum reduction on that array. Can you predict what the result should be? Code it. Was your prediction right?

### 3.3.2 Reduce in place

crumb trail: > mpi-collective > Rooted collectives: broadcast, reduce > Reduce in place

By default MPI will not overwrite the original data with the reduction result, but you can tell it to do so using the MPI_IN_PLACE specifier:

// allreduceinplace.c
for (int irand=0; irand

Now every process only has a receive buffer, so this has the advantage of saving half the memory. The input value is put in the receive buffer.

The above example used MPI_IN_PLACE in MPI_Allreduce ; in MPI_Reduce it's little tricky. The reasoning is a follows:

• In MPI_Reduce every process has a buffer to contribute, but only the root needs a receive buffer. Therefore, MPI_IN_PLACE takes the place of the receive buffer on any processor except for the root …

• \dots while the root, which needs a receive buffer, has MPI_IN_PLACE takes the place of the send buffer. In order to contribute its value, the root needs to put this in the receive buffer.

Here is one way you could write the in-place version of MPI_Reduce :

if (procno==root)
MPI_Reduce(MPI_IN_PLACE,myrandoms,
nrandoms,MPI_FLOAT,MPI_SUM,root,comm);
else
MPI_Reduce(myrandoms,MPI_IN_PLACE,
nrandoms,MPI_FLOAT,MPI_SUM,root,comm);

However, as a point of style, having different versions of a a collective in different branches of a condition is infelicitous. The following may be preferable:
float *sendbuf,*recvbuf;
if (procno==root) {
sendbuf = MPI_IN_PLACE; recvbuf = myrandoms;
} else {
sendbuf = myrandoms; recvbuf = MPI_IN_PLACE;
}
MPI_Reduce(sendbuf,recvbuf,
nrandoms,MPI_FLOAT,MPI_SUM,root,comm);


In Fortran the code is less elegant because you can not do these address calculations:

// reduceinplace.F90
call random_number(mynumber)
target_proc = ntids-1;
! add all the random variables together
if (mytid.eq.target_proc) then
result = mytid
call MPI_Reduce(MPI_IN_PLACE,result,1,MPI_REAL,MPI_SUM,&
target_proc,comm,err)
else
mynumber = mytid
call MPI_Reduce(mynumber,result,1,MPI_REAL,MPI_SUM,&
target_proc,comm,err)
end if
! the result should be ntids*(ntids-1)/2:
if (mytid.eq.target_proc) then
write(*,'("Result ",f5.2," compared to n(n-1)/2=",f5.2)') &
result,ntids*(ntids-1)/2.
end if


Python note

The value MPI.IN_PLACE can be used for the send buffer:

## allreduceinplace.py
myrandom = np.empty(1,dtype=np.int)
myrandom[0] = random_number

comm.Allreduce(MPI.IN_PLACE,myrandom,op=MPI.MAX)


MPL note

In MPL , the in-place variant is activated by specifying only one instead of two buffer arguments.

float
xrank = static_cast( comm_world.rank() ),
xreduce;
// separate recv buffer
comm_world.allreduce(mpl::plus(), xrank,xreduce);
// in place
comm_world.allreduce(mpl::plus(), xrank);
if ( comm_world.rank()==comm_world.size()-1 )
std::cout << "Allreduce got: separate=" << xreduce
<< ", inplace=" << xrank << std::endl;

Reducing a buffer requires specification of a contiguous_layout :
// collectbuffer.cxx
float
xrank = static_cast( comm_world.rank() );
vector rank2p2p1{ 2*xrank,2*xrank+1 };
mpl::contiguous_layout two_floats(rank2p2p1.size());
comm_world.allreduce(mpl::plus(), rank2p2p1.data(),two_floats);
if ( iprint )
cout << "Got: " << rank2p2p1.at(0) << ","
<< rank2p2p1.at(1) << endl;


Similarly for the rooted reduce:

int root = 1;
float
xrank = static_cast( comm_world.rank() ),
xreduce;
// separate receive buffer
comm_world.reduce(mpl::plus(), root, xrank,xreduce);
// in place
comm_world.reduce(mpl::plus(), root, xrank);
if ( comm_world.rank()==root )
std::cout << "Allreduce got: separate=" << xreduce
<< ", inplace=" << xrank << std::endl;


### 3.3.3 Broadcast

crumb trail: > mpi-collective > Rooted collectives: broadcast, reduce > Broadcast

A broadcast models the scenario where one process, the root' process, owns some data, and it communicates it to all other processes.

The broadcast routine MPI_Bcast

C:
int MPI_Bcast(
void* buffer, int count, MPI_Datatype datatype,
int root, MPI_Comm comm)

Fortran:
MPI_Bcast(buffer, count, datatype, root, comm, ierror)
TYPE(*), DIMENSION(..) :: buffer
INTEGER, INTENT(IN) :: count, root
TYPE(MPI_Datatype), INTENT(IN) :: datatype
TYPE(MPI_Comm), INTENT(IN) :: comm
INTEGER, OPTIONAL, INTENT(OUT) :: ierror

Python native:
rbuf = MPI.Comm.bcast(self, obj=None, int root=0)
Python numpy:
MPI.Comm.Bcast(self, buf, int root=0)

MPL:
template
void mpl::communicator::bcast
( int  root, T &  data ) const
( int  root, T *  data, const layout< T > &  l ) const

has the following structure:

MPI_Bcast( data..., root , comm);


Here:

• There is only one buffer, the send buffer. Before the call, the root process has data in this buffer; the other processes allocate a same sized buffer, but for them the contents are irrelevant.

• The root is the process that is sending its data. Typically, it will be the root of a broadcast tree.

Example: in general we can not assume that all processes get the commandline arguments, so we broadcast them from process 0.

// init.c
if (procno==0) {
if ( argc==1 || // the program is called without parameter
( argc>1 && !strcmp(argv[1],"-h") ) // user asked for help
) {
printf("\nUsage: init [0-9]+\n");
MPI_Abort(comm,1);
}
input_argument = atoi(argv[1]);
}
MPI_Bcast(&input_argument,1,MPI_INT,0,comm);


Python note

In python it is both possible to send objects, and to send more C-like buffers. The two possibilities correspond (see section  1.5.4 ) to different routine names; the buffers have to be created as numpy objects.

We illustrate both the native and numpy variants. In the native variant the result is given as a function return; in the numpy variant the send buffer is reused.

## bcast.py
# first native
if procid==root:
buffer = [ 5.0 ] * dsize
else:
buffer = [ 0.0 ] * dsize
buffer = comm.bcast(obj=buffer,root=root)
if not reduce( lambda x,y:x and y,
[ buffer[i]==5.0 for i in  range(len(buffer)) ] ):
print( "Something wrong on proc %d: native buffer <<%s>>" \
% (procid,str(buffer)) )

# then with NumPy
buffer = np.arange(dsize, dtype=np.float64)
if procid==root:
for i in range(dsize):
buffer[i] = 5.0
comm.Bcast( buffer,root=root )
if not all( buffer==5.0 ):
print( "Something wrong on proc %d: numpy buffer <<%s>>" \
% (procid,str(buffer)) )
else:
if procid==root:
print("Success.")


For the following exercise, study figure  labelstring .

Exercise

The Gauss-Jordan algorithm for solving a linear system with a matrix $A$ (or computing its inverse) runs as follows: {\small

for pivot $k=1,…,n$

\>let the vector of scalings $\ell^{(k)}_i=A_{ik}/A_{kk}$

\>for row $r\not=k$

\>\>for column $c=1,…,n$

\>\>\> $A_{rc}\leftarrow A_{rc} - \ell^{(k)}_r A_{rc}$

} where we ignore the update of the righthand side, or the formation of the inverse.

Let a matrix be distributed with each process storing one column. Implement the Gauss-Jordan algorithm as a series of broadcasts: in iteration $k$ process $k$ computes and broadcasts the scaling vector $\{\ell^{(k)}_i\}_i$. Replicate the right-hand side on all processors.

Exercise

Add partial pivoting to your implementation of Gauss-Jordan elimination.

Change your implementation to let each processor store multiple columns, but still do one broadcast per column. Is there a way to have only one broadcast per processor?

## 3.4 Rooted collectives: gather and scatter

crumb trail: > mpi-collective > Rooted collectives: gather and scatter

In the MPI_Scatter operation, the root spreads information to all other processes. The difference with a broadcast is that it involves individual information from/to every process. Thus, the gather operation typically has an array of items, one coming from each sending process, and scatter has an array,

with an individual item for each receiving process; see figure  labelstring .

These gather and scatter collectives have a different parameter list from the broadcast/reduce. The broadcast/reduce involves the same amount of data on each process, so it was enough to have a single datatype/size specification; for one buffer in the broadcast, and for both buffers in the reduce call. In the gather/scatter calls you have

• a large buffer on the root, with a datatype and size specification, and

• a smaller buffer on each process, with its own type and size specification.

In the gather and scatter calls, each processor has $n$ elements of individual data. There is also a root processor that has an array of length $np$, where $p$ is the number of processors. The gather call collects all this data from the processors to the root; the scatter call assumes that the information is initially on the root and it is spread to the individual processors.

Here is a small example:

// gather.c
// we assume that each process has a value "localsize"
// the root process collectes these values

if (procno==root)
localsizes = (int*) malloc( nprocs*sizeof(int) );

// everyone contributes their info
MPI_Gather(&localsize,1,MPI_INT,
localsizes,1,MPI_INT,root,comm);

This will also be the basis of a more elaborate example in section  3.8 .

Exercise

Let each process compute a random number. You want to print the maximum value and on what processor it is computed. What collective(s) do you use? Write a short program.

The MPI_Scatter operation is in some sense the inverse of the gather: the root process has an array of length $np$ where $p$ is the number of processors and $n$ the number of elements each processor will receive.

int MPI_Scatter
(void* sendbuf, int sendcount, MPI_Datatype sendtype,
void* recvbuf, int recvcount, MPI_Datatype recvtype,
int root, MPI_Comm comm)


Two things to note about these routines:

• The prototype for MPI_Gather

C:
int MPI_Gather(
const void* sendbuf, int sendcount, MPI_Datatype sendtype,
void* recvbuf, int recvcount, MPI_Datatype recvtype,
int root, MPI_Comm comm)

Semantics:
IN sendbuf: starting address of send buffer (choice)
IN sendcount: number of elements in send buffer (non-negative integer)
IN sendtype: data type of send buffer elements (handle)
OUT recvbuf: address of receive buffer (choice, significant only at root)
IN recvcount: number of elements for any single receive (non-negative integer, significant only at root)
IN recvtype: data type of recv buffer elements (significant only at root) (handle)
IN root: rank of receiving process (integer)
IN comm: communicator (handle)

Fortran:
MPI_Gather
(sendbuf, sendcount, sendtype, recvbuf, recvcount, recvtype,
root, comm, ierror)
TYPE(*), DIMENSION(..), INTENT(IN) :: sendbuf
TYPE(*), DIMENSION(..) :: recvbuf
INTEGER, INTENT(IN) :: sendcount, recvcount, root
TYPE(MPI_Datatype), INTENT(IN) :: sendtype, recvtype
TYPE(MPI_Comm), INTENT(IN) :: comm
INTEGER, OPTIONAL, INTENT(OUT) :: ierror

Python:
MPI.Comm.Gather
(self, sendbuf, recvbuf, int root=0)

has two count' parameters, one for the length of the individual send buffers, and one for the receive buffer. However, confusingly, the second parameter (which is only relevant on the root) does not indicate the total amount of information coming in, but rather the size of each contribution. Thus, the two count parameters will usually be the same (at least on the root); they can differ if you use different MPI_Datatype values for the sending and receiving processors.
• While every process has a sendbuffer in the gather, and a receive buffer in the scatter call, only the root process needs the long array in which to gather, or from which to scatter. However, because in SPMD mode all processes need to use the same routine, a parameter for this long array is always present. Non-root processes can use a null pointer here.
• More elegantly, the MPI_IN_PLACE option can be used buffers that are not applicable. See section  3.3.2 .

MPL note

Gathering (by gather scatter a single scalar takes a scalar argument and a raw array:

vector v;
float x;
comm_world.scatter(0, v.data(), x);

If more than a single scalar is gathered, or scattered into, it becomes necessary to specify a layout:
vector vrecv(2),vsend(2*nprocs);
mpl::contiguous_layout twonums(2);
comm_world.scatter(0, vsend.data(),twonums, vrecv.data(),twonums );


### 3.4.1 Examples

crumb trail: > mpi-collective > Rooted collectives: gather and scatter > Examples

In some applications, each process computes a row or column of a matrix, but for some calculation (such as the determinant) it is more efficient to have the whole matrix on one process. You should of course only do this if this matrix is essentially smaller than the full problem, such as an interface system or the last coarsening level in multigrid.

Figure  labelstring pictures this. Note that conceptually we are gathering a two-dimensional object, but the buffer is of course one-dimensional. You will later see how this can be done more elegantly with the subarray' datatype; section  5.3.4 .

Another thing you can do with a distributed matrix is to transpose it.

// itransposeblock.c
for (int iproc=0; iproc

In this example, each process scatters its column. This needs to be done only once, yet the scatter happens in a loop. The trick here is that a process only originates the scatter when it is the root, which happens only once. Why do we need a loop? That is because each element of a process' row originates from a different scatter operation.

Exercise

Can you rewrite this code so that it uses a gather rather than a scatter? Does that change anything essential about structure of the code?

### 3.4.2 Allgather

crumb trail: > mpi-collective > Rooted collectives: gather and scatter > Allgather

The MPI_Allgather

C:
int MPI_Allgather(const void *sendbuf, int  sendcount,
MPI_Datatype sendtype, void *recvbuf, int recvcount,
MPI_Datatype recvtype, MPI_Comm comm)
int MPI_Iallgather(const void *sendbuf, int  sendcount,
MPI_Datatype sendtype, void *recvbuf, int recvcount,
MPI_Datatype recvtype, MPI_Comm comm, MPI_Request *request)

Fortran:
MPI_ALLGATHER(SENDBUF, SENDCOUNT, SENDTYPE, RECVBUF, RECVCOUNT,
RECVTYPE, COMM, IERROR)
SENDBUF (*), RECVBUF (*)
INTEGER    SENDCOUNT, SENDTYPE, RECVCOUNT, RECVTYPE, COMM,
INTEGER    IERROR
MPI_IALLGATHER(SENDBUF, SENDCOUNT, SENDTYPE, RECVBUF, RECVCOUNT,
RECVTYPE, COMM, REQUEST, IERROR)
SENDBUF(*), RECVBUF (*)
INTEGER    SENDCOUNT, SENDTYPE, RECVCOUNT, RECVTYPE, COMM
INTEGER    REQUEST, IERROR
C++ Syntax

Parameters:
sendbuf : Starting address of send buffer (choice).
sendcount: Number of elements in send buffer (integer).
sendtype: Datatype of send buffer elements (handle).
recvbuf: Starting address of recv buffer (choice).
recvcount: Number of elements received from any process (integer).
recvtype: Datatype of receive buffer elements (handle).
comm; Communicator (handle).

recvbuf: Address of receive buffer (choice).
request: Request (handle, non-blocking only).

routine does the same gather onto every process: each process winds up with the totality of all data; figure  labelstring .

This routine can be used in the simplest implementation of the dense matrix-vector product to give each processor the full input; see  Eijkhout:IntroHPC .

Some cases look like an all-gather but can be implemented more efficiently. Suppose you have two distributed vectors, and you want to create a new vector that contains those elements of the one that do not appear in the other. You could implement this by gathering the second vector on each processor, but this may be prohibitive in memory usage.

Exercise

Can you think of another algorithm for taking the set difference of two distributed vectors. Hint: look up bucket-brigade algorithm' in  [Eijkhout:IHPSClulu] . What is the time and space complexity of this algorithm? Can you think of other advantages beside a reduction in workspace?

## 3.5 All-to-all

crumb trail: > mpi-collective > All-to-all

The all-to-all operation MPI_Alltoall

int MPI_Alltoallv
(void *sendbuf, int sendcnt, MPI_Datatype sendtype,
void *recvbuf, int recvcnt, MPI_Datatype recvtype,
MPI_Comm comm)


can be seen as a collection of simultaneous broadcasts or simultaneous gathers. The parameter specification is much like an allgather, with a separate send and receive buffer, and no root specified. As with the gather call, the receive count corresponds to an individual receive, not the total amount.

Unlike the gather call, the send buffer now obeys the same principle: with a send count of 1, the buffer has a length of the number of processes.

### 3.5.1 All-to-all as data transpose

crumb trail: > mpi-collective > All-to-all > All-to-all as data transpose

The all-to-all operation can be considered as a data transpose. For instance, assume that each process knows how much data to send to every other process. If you draw a connectivity matrix of size $P\times P$, denoting who-sends-to-who, then the send information can be put in rows: $\forall_i\colon C[i,j]>0\quad\hbox{if process i sends to process j}.$ Conversely, the columns then denote the receive information: $\forall_j\colon C[i,j]>0\quad\hbox{if process j receives from process i}.$

The typical application for such data transposition is in the FFT algorithm, where it can take tens of percents of the running time on large clusters.

We will consider another application of data transposition, namely radix sort , but we will do that in a couple of steps. First of all:

Exercise

In the initial stage of radix sorting , each process considers how many elements to send to every other process. Use MPI_Alltoall to derive from this how many elements they will receive from every other process.

### 3.5.2 All-to-all-v

crumb trail: > mpi-collective > All-to-all > All-to-all-v

The major part of the radix sort algorithm consist of every process sending some of its elements to each of the other processes.

Exercise

The actual data shuffle of a radix sort can be done with MPI_Alltoallv . Finish the code of exercise  labelstring .

## 3.6 Reduce-scatter

crumb trail: > mpi-collective > Reduce-scatter

There are several MPI collectives that are functionally equivalent to a combination of others. You have already seen MPI_Allreduce which is equivalent to a reduction followed by a broadcast. Often such combinations can be more efficient than using the individual calls; see  Eijkhout:IntroHPC .

Here is another example: MPI_Reduce_scatter is equivalent to a reduction on an array of data (meaning a pointwise reduction on each array location) followed by a scatter of this array to the individual processes.

We will discuss this routine, or rather its variant MPI_Reduce_scatter_block

Semantics:
MPI_Reduce_scatter
( sendbuf, recvbuf, recvcounts, datatype, op, comm)
MPI_Reduce_scatter_block
( sendbuf, recvbuf, recvcount, datatype, op, comm)

Input parameters:
sendbuf: starting address of send buffer (choice)
recvcount: element count per block (non-negative integer)
recvcounts: non-negative integer array (of length group size)
specifying the number of elements of the result distributed to each
process.
datatype: data type of elements of send and receive buffers (handle)
op: operation (handle)
comm: communicator (handle)

Output parameters:
recvbuf: starting address of receive buffer (choice)

C:
int MPI_Reduce_scatter
(const void* sendbuf, void* recvbuf, const int recvcounts[],
MPI_Datatype datatype, MPI_Op op, MPI_Comm comm)

F:
MPI_Reduce_scatter(sendbuf, recvbuf, recvcounts, datatype, op, comm,
ierror)
TYPE(*), DIMENSION(..), INTENT(IN) :: sendbuf
TYPE(*), DIMENSION(..) :: recvbuf
INTEGER, INTENT(IN) :: recvcounts(*)
TYPE(MPI_Datatype), INTENT(IN) :: datatype
TYPE(MPI_Op), INTENT(IN) :: op
TYPE(MPI_Comm), INTENT(IN) :: comm
INTEGER, OPTIONAL, INTENT(OUT) :: ierror

Py:
comm.Reduce_scatter(sendbuf, recvbuf, recvcounts=None, Op op=SUM)

, using an important example: the sparse matrix-vector product (see  Eijkhout:IntroHPC for background information). Each process contains one or more matrix rows, so by looking at indices the process can decide what other processes it needs to receive data from, that is, each process knows how many messages it will receive, and from which processes. The problem is for a process to find out what other processes it needs to send data to.

Let's set up the data:

// reducescatter.c
int
// data that we know:
*i_recv_from_proc = (int*) malloc(nprocs*sizeof(int)),
*procs_to_recv_from, nprocs_to_recv_from=0,
// data we are going to determin:
*procs_to_send_to,nprocs_to_send_to;


Each process creates an array of ones and zeros, describing who it needs data from. Ideally, we only need the array procs_to_recv_from but initially we need the (possibly much larger) array i_recv_from_proc .

Next, the MPI_Reduce_scatter_block call then computes, on each process, how many messages it needs to send.

MPI_Reduce_scatter_block
(i_recv_from_proc,&nprocs_to_send_to,1,MPI_INT,
MPI_SUM,comm);


We do not yet have the information to which processes to send. For that, each process sends a zero-size message to each of its senders. Conversely, it then does a receive to with MPI_ANY_SOURCE to discover who is requesting data from it. The crucial point to the MPI_Reduce_scatter_block call is that, without it, a process would not know how many of these zero-size messages to expect.

/*
* Send a zero-size msg to everyone that you receive from,
* just to let them know that they need to send to you.
*/
MPI_Request send_requests[nprocs_to_recv_from];
for (int iproc=0; iproc


The MPI_Reduce_scatter

Semantics:
MPI_Reduce_scatter
( sendbuf, recvbuf, recvcounts, datatype, op, comm)
MPI_Reduce_scatter_block
( sendbuf, recvbuf, recvcount, datatype, op, comm)

Input parameters:
sendbuf: starting address of send buffer (choice)
recvcount: element count per block (non-negative integer)
recvcounts: non-negative integer array (of length group size)
specifying the number of elements of the result distributed to each
process.
datatype: data type of elements of send and receive buffers (handle)
op: operation (handle)
comm: communicator (handle)

Output parameters:
recvbuf: starting address of receive buffer (choice)

C:
int MPI_Reduce_scatter
(const void* sendbuf, void* recvbuf, const int recvcounts[],
MPI_Datatype datatype, MPI_Op op, MPI_Comm comm)

F:
MPI_Reduce_scatter(sendbuf, recvbuf, recvcounts, datatype, op, comm,
ierror)
TYPE(*), DIMENSION(..), INTENT(IN) :: sendbuf
TYPE(*), DIMENSION(..) :: recvbuf
INTEGER, INTENT(IN) :: recvcounts(*)
TYPE(MPI_Datatype), INTENT(IN) :: datatype
TYPE(MPI_Op), INTENT(IN) :: op
TYPE(MPI_Comm), INTENT(IN) :: comm
INTEGER, OPTIONAL, INTENT(OUT) :: ierror

Py:
comm.Reduce_scatter(sendbuf, recvbuf, recvcounts=None, Op op=SUM)

call is more general: instead of indicating the mere presence of a message between two processes, by having individual receive counts one can, for instance, indicate the size of the messages.

We can look at reduce-scatter as a limited form of the all-to-all data transposition discussed above (section  3.5.1 ). Suppose that the matrix $C$ contains only $0/1$, indicating whether or not a messages is send, rather than the actual amounts. If a receiving process only needs to know how many messages to receive, rather than where they come from, it is enough to know the column sum, rather than the full column (see figure  labelstring ).

Another application of the reduce-scatter mechanism is in the dense matrix-vector product, if a two-dimensional data distribution is used.

### 3.6.1 Examples

crumb trail: > mpi-collective > Reduce-scatter > Examples

An important application of this is establishing an irregular communication pattern. Assume that each process knows which other processes it wants to communicate with; the problem is to let the other processes know about this. The solution is to use MPI_Reduce_scatter to find out how many processes want to communicate with you

MPI_Reduce_scatter_block
(i_recv_from_proc,&nprocs_to_send_to,1,MPI_INT,
MPI_SUM,comm);

and then wait for precisely that many messages with a source value of MPI_ANY_SOURCE .
/*
* Send a zero-size msg to everyone that you receive from,
* just to let them know that they need to send to you.
*/
MPI_Request send_requests[nprocs_to_recv_from];
for (int iproc=0; iproc


Use of MPI_Reduce_scatter to implement the two-dimensional matrix-vector product. Set up separate row and column communicators with MPI_Comm_split , use MPI_Reduce_scatter to combine local products. \cverbatimsnippet[examples/mpi/c/mvp2d.c]{mvp2d}

## 3.7 Barrier

crumb trail: > mpi-collective > Barrier

A barrier call, MPI_Barrier

C:
int MPI_Barrier( MPI_Comm comm )

Fortran2008:
MPI_BARRIER(COMM, IERROR)
Type(MPI_Comm),intent(int) :: COMM
INTEGER,intent(out) :: IERROR

Fortran 95:
MPI_BARRIER(COMM, IERROR)
INTEGER :: COMM, IERROR

Input parameter:
comm : Communicator (handle)

Output parameter:
Ierror : Error status (integer), Fortran only

is a routine that blocks all processes until they have all reached the barrier call. Thus it achieves time synchronization of the processes.

This call's simplicity is contrasted with its usefulness, which is very limited. It is almost never necessary to synchronize processes through a barrier: for most purposes it does not matter if processors are out of sync. Conversely, collectives (except the new non-blocking ones; section  3.11 ) introduce a barrier of sorts themselves.

## 3.8 Variable-size-input collectives

crumb trail: > mpi-collective > Variable-size-input collectives

In the gather and scatter call above each processor received or sent an identical number of items. In many cases this is appropriate, but sometimes each processor wants or contributes an individual number of items.

Let's take the gather calls as an example. Assume that each processor does a local computation that produces a number of data elements, and this number is different for each processor (or at least not the same for all). In the regular MPI_Gather call the root processor had a buffer of size $nP$, where $n$ is the number of elements produced on each processor, and $P$ the number of processors. The contribution from processor $p$ would go into locations $pn,…,(p+1)n-1$.

For the variable case, we first need to compute the total required buffer size. This can be done through a simple MPI_Reduce with MPI_SUM as reduction operator: the buffer size is $\sum_p n_p$ where $n_p$ is the number of elements on processor $p$. But you can also postpone this calculation for a minute.

The next question is where the contributions of the processor will go into this buffer. For the contribution from processor $p$ that is $\sum_{q<p}n_p,…\sum_{q\leq p}n_p-1$. To compute this, the root processor needs to have all the $n_p$ numbers, and it can collect them with an MPI_Gather call.

We now have all the ingredients. All the processors specify a send buffer just as with MPI_Gather . However, the receive buffer specification on the root is more complicated. It now consists of:

outbuffer, array-of-outcounts, array-of-displacements, outtype


and you have just seen how to construct that information.

For example, in an MPI_Gatherv

C:
int MPI_Gatherv(
const void* sendbuf, int sendcount, MPI_Datatype sendtype,
void* recvbuf, const int recvcounts[], const int displs[],
MPI_Datatype recvtype, int root, MPI_Comm comm)

Semantics:
IN sendbuf: starting address of send buffer (choice)
IN sendcount: number of elements in send buffer (non-negative integer)
IN sendtype: data type of send buffer elements (handle)
OUT recvbuf: address of receive buffer (choice, significant only at root)
IN recvcounts: non-negative integer array (of length group size) containing the number of elements that are received from each process (significant only at root)
IN displs: integer array (of length group size). Entry i specifies the displacement relative to recvbuf at which to place the incoming data from process i (significant only at root)
IN recvtype: data type of recv buffer elements (significant only at root) (handle)
IN root: rank of receiving process (integer)
IN comm: communicator (handle)

Fortran:
MPI_Gatherv(sendbuf, sendcount, sendtype, recvbuf, recvcounts, displs, recvtype, root, comm, ierror)
TYPE(*), DIMENSION(..), INTENT(IN) :: sendbuf
TYPE(*), DIMENSION(..) :: recvbuf
INTEGER, INTENT(IN) :: sendcount, recvcounts(*), displs(*), root
TYPE(MPI_Datatype), INTENT(IN) :: sendtype, recvtype
TYPE(MPI_Comm), INTENT(IN) :: comm
INTEGER, OPTIONAL, INTENT(OUT) :: ierror

Python:
Gatherv(self, sendbuf, [recvbuf,counts], int root=0)

call each process has an individual number of items to contribute. To gather this, the root process needs to find these individual amounts with an MPI_Gather call, and locally construct the offsets array. Note how the offsets array has size ntids+1 : the final offset value is automatically the total size of all incoming data. See the example below.

There are various calls where processors can have buffers of differing sizes.

• In MPI_Scatterv the root process has a different amount of data for each recipient.

• In MPI_Gatherv , conversely, each process contributes a different sized send buffer to the received result; MPI_Allgatherv does the same, but leaves its result on all processes; MPI_Alltoallv does a different variable-sized gather on each process.

int MPI_Scatterv
(void* sendbuf, int *sendcounts, int *displs, MPI_Datatype sendtype,
void* recvbuf, int recvcount, MPI_Datatype recvtype,
int root, MPI_Comm comm)


int MPI_Allgatherv
(void *sendbuf, int sendcount, MPI_Datatype sendtype,
void *recvbuf, int *recvcounts, int *displs,
MPI_Datatype recvtype, MPI_Comm comm)


### 3.8.1 Example of Gatherv

crumb trail: > mpi-collective > Variable-size-input collectives > Example of Gatherv

We use MPI_Gatherv to do an irregular gather onto a root. We first need an MPI_Gather to determine offsets.

// gatherv.c
// we assume that each process has an array "localdata"
// of size "localsize"

// the root process decides how much data will be coming:
// allocate arrays to contain size and offset information
if (procno==root) {
localsizes = (int*) malloc( nprocs*sizeof(int) );
offsets = (int*) malloc( nprocs*sizeof(int) );
}
// everyone contributes their local size info
MPI_Gather(&localsize,1,MPI_INT,
localsizes,1,MPI_INT,root,comm);
// the root constructs the offsets array
if (procno==root) {
int total_data = 0;
for (int i=0; i

## gatherv.py
# implicitly using root=0
globalsize = comm.reduce(localsize)
if procid==0:
print("Global size=%d" % globalsize)
collecteddata = np.empty(globalsize,dtype=np.int)
counts = comm.gather(localsize)
comm.Gatherv(localdata, [collecteddata, counts])


### 3.8.2 Example of Allgatherv

crumb trail: > mpi-collective > Variable-size-input collectives > Example of Allgatherv

Prior to the actual gatherv call, we need to construct the count and displacement arrays. The easiest way is to use a reduction.

// allgatherv.c
MPI_Allgather
( &my_count,1,MPI_INT,
recv_counts,1,MPI_INT, comm );
int accumulate = 0;
for (int i=0; i


In python the receive buffer has to contain the counts and displacements arrays.

## allgatherv.py
mycount = procid+1
my_array = np.empty(mycount,dtype=np.float64)


my_count = np.empty(1,dtype=np.int)
my_count[0] = mycount
comm.Allgather( my_count,recv_counts )

accumulate = 0
for p in range(nprocs):
recv_displs[p] = accumulate; accumulate += recv_counts[p]
global_array = np.empty(accumulate,dtype=np.float64)
comm.Allgatherv( my_array, [global_array,recv_counts,recv_displs,MPI.DOUBLE] )


### 3.8.3 Variable all-to-all

crumb trail: > mpi-collective > Variable-size-input collectives > Variable all-to-all

MPI_Alltoallv

int MPI_Alltoallv
(void *sendbuf, int *sendcnts, int *sdispls, MPI_Datatype sendtype,
void *recvbuf, int *recvcnts, int *rdispls, MPI_Datatype recvtype,
MPI_Comm comm)



## 3.9 Scan operations

crumb trail: > mpi-collective > Scan operations

The MPI_Scan operation also performs a reduction, but it keeps the partial results. That is, if processor $i$ contains a number $x_i$, and $\oplus$ is an operator, then the scan operation leaves $x_0\oplus\cdots\oplus x_i$ on processor $i$. This type of operation is often called a prefix operation ; see Eijkhout:IntroHPC .

The MPI_Scan

C:
int MPI_Scan(const void* sendbuf, void* recvbuf,
int count, MPI_Datatype datatype, MPI_Op op, MPI_Comm comm)
IN sendbuf: starting address of send buffer (choice)
OUT recvbuf: starting address of receive buffer (choice)
IN count: number of elements in input buffer (non-negative integer)
IN datatype: data type of elements of input buffer (handle)
IN op: operation (handle)
IN comm: communicator (handle)

Fortran:
MPI_Scan(sendbuf, recvbuf, count, datatype, op, comm, ierror)
TYPE(*), DIMENSION(..), INTENT(IN) :: sendbuf
TYPE(*), DIMENSION(..) :: recvbuf
INTEGER, INTENT(IN) :: count
TYPE(MPI_Datatype), INTENT(IN) :: datatype
TYPE(MPI_Op), INTENT(IN) :: op
TYPE(MPI_Comm), INTENT(IN) :: comm
INTEGER, OPTIONAL, INTENT(OUT) :: ierror

Python:
res = Intracomm.scan( sendobj=None,recvobj=None,op=MPI.SUM)
res = Intracomm.exscan( sendobj=None,recvobj=None,op=MPI.SUM)

routine is an inclusive scan operation.

// scan.c
// add all the random variables together
MPI_Scan(&myrandom,&result,1,MPI_FLOAT,MPI_SUM,comm);
// the result should be approaching nprocs/2:
if (procno==nprocs-1)
printf("Result %6.3f compared to nprocs/2=%5.2f\n",
result,nprocs/2.);


In python native mode the result is a function return value, with numpy the result is passed as the second parameter.

## scan.py
mycontrib = 10+random.randint(1,nprocs)
myfirst = 0
mypartial = comm.scan(mycontrib)
sbuf = np.empty(1,dtype=np.int)
rbuf = np.empty(1,dtype=np.int)
sbuf[0] = mycontrib
comm.Scan(sbuf,rbuf)


You can use any of the given reduction operators, (for the list, see section  3.10.1 ), or a user-defined one. In the latter case, the MPI_Op operations do not return an error code.

### 3.9.1 Exclusive scan

crumb trail: > mpi-collective > Scan operations > Exclusive scan

Often, the more useful variant is the exclusive scan MPI_Exscan

C:
int MPI_Exscan(const void *sendbuf, void *recvbuf, int count,
MPI_Datatype datatype, MPI_Op op, MPI_Comm comm)
int MPI_Iexscan(const void *sendbuf, void *recvbuf, int count,
MPI_Datatype datatype, MPI_Op op, MPI_Comm comm,
MPI_Request *request)

Fortran:

MPI_EXSCAN(SENDBUF, RECVBUF, COUNT, DATATYPE, OP, COMM, IERROR)
SENDBUF(*), RECVBUF(*)
INTEGER    COUNT, DATATYPE, OP, COMM, IERROR
MPI_IEXSCAN(SENDBUF, RECVBUF, COUNT, DATATYPE, OP, COMM, REQUEST, IERROR)
SENDBUF(*), RECVBUF(*)
INTEGER    COUNT, DATATYPE, OP, COMM, REQUEST, IERROR

Input Parameters

sendbuf: Send buffer (choice).
count: Number of elements in input buffer (integer).
datatype: Data type of elements of input buffer (handle).
op: Operation (handle).
comm: Communicator (handle).

Output Parameters

recvbuf: Receive buffer (choice).
request: Request (handle, non-blocking only).

with the same prototype.

The result of the exclusive scan is undefined on processor 0 ( None in python), and on processor 1 it is a copy of the send value of processor 1. In particular, the MPI_Op need not be called on these two processors.

Exercise

The exclusive definition, which computes $x_0\oplus x_{i-1}$ on processor $i$, can easily be derived from the inclusive operation for operations such as MPI_SUM or MPI_PROD . Are there operators where that is not the case?

### 3.9.2 Use of scan operations

crumb trail: > mpi-collective > Scan operations > Use of scan operations

The MPI_Scan operation is often useful with indexing data. Suppose that every processor $p$ has a local vector where the number of elements $n_p$ is dynamically determined. In order to translate the local numbering $0… n_p-1$ to a global numbering one does a scan with the number of local elements as input. The output is then the global number of the first local variable.

Exercise

Do you use MPI_Scan or MPI_Exscan for this operation? How would you describe the result of the other scan operation, given the same input?

Exclusive scan examples:

// exscan.c
int my_first=0,localsize;
// localsize = ..... result of local computation ....
// find myfirst location based on the local sizes
err = MPI_Exscan(&localsize,&my_first,
1,MPI_INT,MPI_SUM,comm); CHK(err);

## exscan.py
localsize = 10+random.randint(1,nprocs)
myfirst = 0
mypartial = comm.exscan(localsize,0)


It is possible to do a segmented scan . Let $x_i$ be a series of numbers that we want to sum to $X_i$ as follows. Let $y_i$ be a series of booleans such that $\begin{cases} X_i=x_i&\hbox{if y_i=0}\\ X_i=X_{i-1}+x_i&\hbox{if y_i=1} \end{cases}$ (This is the basis for the implementation of the sparse matrix vector product as prefix operation; see Eijkhout:IntroHPC .) This means that $X_i$ sums the segments between locations where $y_i=0$ and the first subsequent place where $y_i=1$. To implement this, you need a user-defined operator $\begin{pmatrix} X\\ x\\ y \end{pmatrix} = \begin{pmatrix} X_1\\ x_1\\ y_1 \end{pmatrix} \bigoplus \begin{pmatrix} X_2\\ x_2\\ y_2 \end{pmatrix} \colon \begin{cases} X=x_1+x_2&\hbox{if y_2==1}\\ X=x_2&\hbox{if y_2==0} \end{cases}$ This operator is not communitative, and it needs to be declared as such with MPI_Op_create ; see section  3.10.2

## 3.10 MPI Operators

crumb trail: > mpi-collective > MPI Operators

MPI operators are used in reduction operators. Most common operators, such as sum or maximum, have been built into the MPI library, but it is possible to define new operators.

### 3.10.1 Pre-defined operators

crumb trail: > mpi-collective > MPI Operators > Pre-defined operators

The following is the list of pre-defined operators MPI_Op values.

\include mpi-opstable

MPL note

In MPL , operators are classes with an operator() method. For example:

float
xrank = static_cast( comm_world.rank() ),
xreduce;
// separate recv buffer
comm_world.allreduce(mpl::plus(), xrank,xreduce);
// in place
comm_world.allreduce(mpl::plus(), xrank);
if ( comm_world.rank()==comm_world.size()-1 )
std::cout << "Allreduce got: separate=" << xreduce
<< ", inplace=" << xrank << std::endl;

(Note the parentheses after the operator; without that, mpl::min is a type, and you get an error message about type name is not allowed'.)

#### 3.10.1.1 Minloc and maxloc

crumb trail: > mpi-collective > MPI Operators > Pre-defined operators > Minloc and maxloc

The MPI_MAXLOC and MPI_MINLOC operations yield both the maximum and the rank on which it occurs. Their result is a struct of the data over which the reduction happens, and an int.

In C, the types to use in the reduction call are: MPI_FLOAT_INT , MPI_LONG_INT , MPI_DOUBLE_INT , MPI_SHORT_INT , MPI_2INT , MPI_LONG_DOUBLE_INT . Likewise, the input needs to consist of such structures: the input should be an array of such struct types, where the int is the rank of the number.

The Fortran interface to MPI was designed to use pre-Fortran90 features, so it is not using Fortran derived types ( Type keyword). Instead, all integer indices are stored in whatever the type is that is being reduced. The available result types are then MPI_2REAL , MPI_2DOUBLE_PRECISION , MPI_2INTEGER .

Likewise, the input needs to be arrays of such type. Consider this example:

Real8,dimension(2,N) :: input,output
call MPI_Reduce( input,output, N, MPI_2DOUBLE_PRECISION, &
MPI_MAXLOC, root, comm )


### 3.10.2 User-defined operators

crumb trail: > mpi-collective > MPI Operators > User-defined operators

In addition to predefined operators, MPI has the possibility of user-defined operators to use in a reduction or scan operation.

The routine for this is MPI_Op_create

Semantics:
MPI_OP_CREATE( function, commute, op)
[ IN function] user defined function (function)
[ IN commute] true if commutative; false otherwise.
[ OUT op] operation (handle)

C:
int MPI_Op_create
(MPI_User_function *function, int commute,
MPI_Op *op)

Fortran 2008:
USE mpi_f08
MPI_Op_create(user_fn, commute, op, ierror)
PROCEDURE(MPI_User_function) :: user_fn
LOGICAL, INTENT(IN) :: commute
TYPE(MPI_Op), INTENT(OUT) :: op
INTEGER, OPTIONAL, INTENT(OUT) :: ierror

Fortran90:
MPI_OP_CREATE( FUNCTION, COMMUTE, OP, IERROR)
EXTERNAL FUNCTION
LOGICAL COMMUTE
INTEGER OP, IERROR

Python:
MPI.Op.create(cls,function,bool commute=False)

, which takes a user function and turns it into an object of type MPI_Op , which can then be used in any reduction:
MPI_Op rwz;
MPI_Op_create(reduce_without_zero,1,&rwz);
MPI_Allreduce(data+procno,&positive_minimum,1,MPI_INT,rwz,comm);

rwz = MPI.Op.Create(reduceWithoutZero)
positive_minimum = np.zeros(1,dtype=np.intc)
comm.Allreduce(data[procid],positive_minimum,rwz);


The user function needs to have the following prototype:

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


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


For example, here is an operator for finding the smallest non-zero number in an array of nonnegative integers:

// reductpositive.c
void reduce_without_zero(void *in,void *inout,int *len,MPI_Datatype *type) {
// r is the already reduced value, n is the new value
int n = *(int*)in, r = *(int*)inout;
int m;
if (n==0) { // new value is zero: keep r
m = r;
} else if (r==0) {
m = n;
} else if (n


Python note

The python equivalent of such a function receives bare buffers as arguments. Therefore, it is best to turn them first into NumPy arrays using np.frombuffer :

## reductpositive.py
def reduceWithoutZero(in_buf, inout_buf, datatype):
typecode = MPI._typecode(datatype)
assert typecode is not None ## check MPI datatype is built-in
dtype = np.dtype(typecode)

in_array = np.frombuffer(in_buf, dtype)
inout_array = np.frombuffer(inout_buf, dtype)

n = in_array[0]; r = inout_array[0]
if n==0:
m = r
elif r==0:
m = n
elif n

The assert statement accounts for the fact that this mapping of MPI datatype to NumPy dtype only works for built-in MPI datatypes.

The function has an array length argument  len , to allow for pointwise reduction on a whole array at once. The inoutvec array contains partially reduced results, and is typically overwritten by the function.

There are some restrictions on the user function:

• It may not call MPI functions, except for MPI_Abort .

• It must be associative; it can be optionally commutative, which fact is passed to the MPI_Op_create call.

Exercise

Write the reduction function to implement the one-norm of a vector: $\|x\|_1 \equiv \sum_i |x_i|.$

The operator can be destroyed with a corresponding MPI_Op_free .

int MPI_Op_free(MPI_Op *op)


This sets the operator to MPI_OP_NULL . This is not necessary in OO languages, where the destructor takes care of it.

You can query the commutativity of an operator with MPI_Op_commutative

Semantics:
MPI_Op_commutative(op, commute)
IN  op : handle
OUT commute : true/false

C:
int MPI_Op_commutative(MPI_Op op, int *commute)

Fortran:
MPI_OP_COMMUTATIVE( op, commute)
TYPE(MPI_Op), INTENT(IN) :: op
LOGICAL, INTENT(OUT) ::  commute
INTEGER, OPTIONAL, INTENT(OUT) ::  ierror

.

### 3.10.3 Local reduction

crumb trail: > mpi-collective > MPI Operators > Local reduction

The application of an MPI_Op can be performed with the routine MPI_Reduce_local

Semantics:
MPI_REDUCE_LOCAL( inbuf, inoutbuf, count, datatype, op)

Input parameters:
inbuf: input buffer (choice)
count: number of elements in inbuf and inoutbuf buffers
(non-negative integer)
datatype: data type of elements of inbuf and inoutbuf buffers
(handle)
op: operation (handle)

Input/output parameters:
inoutbuf: combined input and output buffer (choice)

C:
int MPI_Reduce_local
(void* inbuf, void* inoutbuf, int count,
MPI_Datatype datatype, MPI_Op op)

Fortran:
MPI_REDUCE_LOCAL(INBUF, INOUBUF, COUNT, DATATYPE, OP, IERROR)
INBUF(*), INOUTBUF(*)
INTEGER :: COUNT, DATATYPE, OP, IERROR

. Using this routine and some send/receive scheme you can build your own global reductions. Note that this routine does not take a communicator because it is purely local.

## 3.11 Non-blocking collectives

crumb trail: > mpi-collective > Non-blocking collectives

Above you have seen how the Isend' and Irecv' routines can overlap communication with computation. This is not possible with the collectives you have seen so far: they act like blocking sends or receives. However, there are also non-blocking collectives , introduced in MPI 3 .

Such operations can be used to increase efficiency. For instance, computing $y \leftarrow Ax + (x^tx)y$ involves a matrix-vector product, which is dominated by computation in the sparse matrix case, and an inner product which is typically dominated by the communication cost. You would code this as

MPI_Iallreduce( .... x ..., &request);
// compute the matrix vector product
MPI_Wait(request);
// do the addition


This can also be used for 3D FFT operations  [Hoefler:case-for-nbc] . Occasionally, a non-blocking collective can be used for non-obvious purposes, such as the MPI_Ibarrier in  [Hoefler:2010:SCP] .

These have roughly the same calling sequence as their blocking counterparts, except that they output an MPI_Request . You can then use an MPI_Wait call to make sure the collective has completed.

Non-blocking collectives offer a number of performance advantages:

• Do two reductions (on the same communicator) with different operators simultaneously: $\begin{array}{l} \alpha\leftarrow x^ty\\ \beta\leftarrow \|z\|_\infty \end{array}$ which translates to:

MPI_Allreduce( &local_xy,  &global_xy, 1,MPI_DOUBLE,MPI_SUM,comm);
MPI_Allreduce( &local_xinf,&global_xin,1,MPI_DOUBLE,MPI_MAX,comm);

• do collectives on overlapping communicators simultaneously;

• overlap a non-blocking collective with a blocking one.

Remark

Blocking and non-blocking don't match: either all processes call the non-blocking or all call the blocking one. Thus the following code is incorrect:

if (rank==root)
MPI_Reduce( &x /* ... */ root,comm );
else
MPI_Ireduce( &x /* ... */ );


This is unlike the point-to-point behavior of non-blocking calls: you can catch a message with MPI_Irecv that was sent with MPI_Send .

Exercise

Revisit exercise  labelstring . Let only the first row and first column have certain data, which they broadcast through columns and rows respectively. Each process is now involved in two simultaneous collectives. Implement this with non-blocking broadcasts, and time the difference between a blocking and a non-blocking solution.

MPI_Iallreduce

Semantics

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

Input Parameters

sendbuf : starting address of send buffer (choice)
count : number of elements in send buffer (integer)
datatype : data type of elements of send buffer (handle)
op : operation (handle)
comm : communicator (handle)

Output Parameters

recvbuf : starting address of receive buffer (choice)
request : communication request (handle)


MPI_Igather

, MPI_Igatherv , MPI_Iallgather

Semantics

int MPI_Iallgather(
const void *sendbuf, int sendcount, MPI_Datatype sendtype,
void *recvbuf, int recvcount, MPI_Datatype recvtype,
MPI_Comm comm, MPI_Request *request)

Input Parameters

sendbuf : starting address of send buffer (choice)
sendcount : number of elements in send buffer (integer)
sendtype : data type of send buffer elements (handle)
recvcount : number of elements received from any process (integer)
recvtype : data type of receive buffer elements (handle)
comm : communicator (handle)

Output Parameters

recvbuf : address of receive buffer (choice)
request : communication request (handle)

, MPI_Iallgatherv ,

MPI_Iscatter

, MPI_Iscatterv ,

MPI_Ireduce

, MPI_Iallreduce , MPI_Ireduce_scatter , MPI_Ireduce_scatter_block .

MPI_Ialltoall

, MPI_Ialltoallv , MPI_Ialltoallw ,

MPI_Ibarrier

, MPI_Ibcast , MPI_Iexscan , MPI_Iscan ,

MPL note

Non-blocking collectives have the same argument list as the corresponding blocking variant, except that instead of a void result, they return an irequest

### 3.11.1 Examples

crumb trail: > mpi-collective > Non-blocking collectives > Examples

#### 3.11.1.1 Array transpose

crumb trail: > mpi-collective > Non-blocking collectives > Examples > Array transpose

To illustrate the overlapping of multiple non-blocking collectives, consider transposing a data matrix. Initially, each process has one row of the matrix; after transposition each process has a column. Since each row needs to be distributed to all processes, algorithmically this corresponds to a series of scatter calls, one originating from each process.

// itransposeblock.c
for (int iproc=0; iproc

Introducing the non-blocking MPI_Iscatter call, this becomes:
MPI_Request scatter_requests[nprocs];
for (int iproc=0; iproc


Exercise

Can you implement the same algorithm with MPI_Igather ?

#### 3.11.1.2 Stencils

crumb trail: > mpi-collective > Non-blocking collectives > Examples > Stencils

The ever-popular five-point stencil evaluation does not look like a collective operation, and indeed, it is usually evaluated with (non-blocking) send/recv operations. However, if we create a subcommunicator on each subdomain that contains precisely that domain and its neighbors, (see figure  labelstring ) we can formulate the communication pattern as a gather on each of these. With ordinary collectives this can not be formulated in a deadlock -free manner, but non-blocking collectives make this feasible.

We will see an even more elegant formulation of this operation in section  10.2 .

### 3.11.2 Non-blocking barrier

crumb trail: > mpi-collective > Non-blocking collectives > Non-blocking barrier

Probably the most surprising non-blocking collective is the non-blocking barrier MPI_Ibarrier

C:
int MPI_Ibarrier(MPI_Comm comm, MPI_Request *request)

Input Parameters
comm : communicator (handle)

Output Parameters
request : communication request (handle)

Fortran2008:
MPI_Ibarrier(comm, request, ierror)
Type(MPI_Comm),intent(int) :: comm
TYPE(MPI_Request),intent(out) :: request
INTEGER,intent(out),optional :: ierror

. The way to understand this is to think of a barrier not in terms of temporal synchronization, but state agreement: reaching a barrier is a sign that a process has attained a certain state, and leaving a barrier means that all processes are in the same state. The ordinary barrier is then a blocking wait for agreement, while with a non-blocking barrier:

• Posting the barrier means that a process has reached a certain state; and

• the request being fullfilled means that all processes have reached the barrier.

One scenario would be local refinement , where some processes decide to refine their subdomain, which fact they need to communicate to their neighbors. The problem here is that most processes are not among these neighbors, so they should not post a receive of any type. Instead, any refining process sends to its neighbors, and every process posts a barrier.

// ibarrierprobe.c
if (i_do_send) {
/*
* Pick a random process to send to,
* not yourself.
*/
int receiver = rand()%nprocs;
MPI_Ssend(&data,1,MPI_FLOAT,receiver,0,comm);
}
/*
* Everyone posts the non-block barrier
* and gets a request to test/wait for
*/
MPI_Request barrier_request;
MPI_Ibarrier(comm,&barrier_request);


Now every process alternately probes for messages and tests for completion of the barrier. Probing is done through the non-blocking MPI_Iprobe call, while testing completion of the barrier is done through MPI_Test .

for ( ; ; step++) {
int barrier_done_flag=0;
MPI_Test(&barrier_request,&barrier_done_flag,
MPI_STATUS_IGNORE);
//stop if you're done!
// if you're not done with the barrier:
int flag; MPI_Status status;
MPI_Iprobe
( MPI_ANY_SOURCE,MPI_ANY_TAG,
comm, &flag, &status );
if (flag) {
// absorb message!


We can use a non-blocking barrier to good effect, utilizing the idle time that would result from a blocking barrier. In the following code fragment processes test for completion of the barrier, and failing to detect such completion, perform some local work.

// findbarrier.c
MPI_Request final_barrier;
MPI_Ibarrier(comm,&final_barrier);

int global_finish=mysleep;
do {
int all_done_flag=0;
MPI_Test(&final_barrier,&all_done_flag,MPI_STATUS_IGNORE);
if (all_done_flag) {
break;
} else {
int flag; MPI_Status status;
// force progress
MPI_Iprobe
( MPI_ANY_SOURCE,MPI_ANY_TAG,
comm, &flag, MPI_STATUS_IGNORE );
printf("[%d] going to work for another second\n",procid);
sleep(1);
global_finish++;
}
} while (1);


## 3.12 Performance of collectives

crumb trail: > mpi-collective > Performance of collectives

It is easy to visualize a broadcast as in figure  labelstring : see figure  labelstring .

the root sends all of its data directly to every other process. While this describes the semantics of the operation, in practice the implementation works quite differently.

The time that a message takes can simply be modeled as $\alpha +\beta n,$ where $\alpha$ is the latency , a one time delay from establishing the communication between two processes, and $\beta$ is the time-per-byte, or the inverse of the bandwidth , and $n$ the number of bytes sent.

Under the assumption that a processor can only send one message at a time, the broadcast in figure  labelstring would take a time proportional to the number of processors.

Exercise

What is the total time required for a broadcast involving $p$ processes? Give $\alpha$ and $\beta$ terms separately.

One way to ameliorate that is to structure the broadcast in a tree-like fashion.

This is depicted in figure  labelstring .

Exercise

How does the communication time now depend on the number of processors, again $\alpha$ and $\beta$ terms separately.

What would be a lower bound on the $\alpha,\beta$ terms?

The theory of the complexity of collectives is described in more detail in Eijkhout:IntroHPC ; see also  [Chan2007Collective] .

## 3.13 Collectives and synchronization

crumb trail: > mpi-collective > Collectives and synchronization

Collectives, other than a barrier, have a synchronizing effect between processors. For instance, in

MPI_Bcast( ....data... root);
MPI_Send(....);


the send operations on all processors will occur after the root executes the broadcast.

Conversely, in a reduce operation the root may have to wait for other processors. This is illustrated in figure  labelstring , which gives a TAU trace of a reduction operation on two nodes, with two six-core sockets (processors) each. We see that\footnote {This uses mvapich version 1.6; in version 1.9 the implementation of an on-node reduction has changed to simulate shared memory.}:

• In each socket, the reduction is a linear accumulation;

• on each node, cores zero and six then combine their result;
• after which the final accumulation is done through the network.

We also see that the two nodes are not perfectly in sync, which is normal for MPI applications. As a result, core 0 on the first node will sit idle until it receives the partial result from core 12, which is on the second node.

While collectives synchronize in a loose sense, it is not possible to make any statements about events before and after the collectives between processors:

...event 1...
MPI_Bcast(....);
...event 2....


Consider a specific scenario:

switch(rank) {
case 0:
MPI_Bcast(buf1, count, type, 0, comm);
MPI_Send(buf2, count, type, 1, tag, comm);
break;
case 1:
MPI_Recv(buf2, count, type, MPI_ANY_SOURCE, tag, comm, &status);
MPI_Bcast(buf1, count, type, 0, comm);
MPI_Recv(buf2, count, type, MPI_ANY_SOURCE, tag, comm, &status);
break;
case 2:
MPI_Send(buf2, count, type, 1, tag, comm);
MPI_Bcast(buf1, count, type, 0, comm);
break;
}


Note the MPI_ANY_SOURCE parameter in the receive calls on processor 1. One obvious execution of this would be:

1. The send from 2 is caught by processor 1;

2. Everyone executes the broadcast;
3. The send from 0 is caught by processor 1.

However, it is equally possible to have this execution:

1. Processor 0 starts its broadcast, then executes the send;

2. Processor 1's receive catches the data from 0, then it executes its part of the broadcast;
3. Processor 1 catches the data sent by 2, and finally processor 2 does its part of the broadcast.

This is illustrated in figure  labelstring .

## 3.14 Performance considerations

crumb trail: > mpi-collective > Performance considerations

In this section we will consider how collectives can be implemented in multiple ways, and the performance implications of such decisions. You can test the algorithms described here using SimGrid (section  ).

### 3.14.1 Scalability

crumb trail: > mpi-collective > Performance considerations > Scalability

We are motivated to write parallel software from two considerations. First of all, if we have a certain problem to solve which normally takes time $T$, then we hope that with $p$ processors it will take time $T/p$. If this is true, we call our parallelization scheme scalable in time . In practice, we often accept small extra terms: as you will see below, parallelization often adds a term $\log_2p$ to the running time.

Exercise

Discuss scalability of the following algorithms:

• You have an array of floating point numbers. You need to compute the sine of each

• You a two-dimensional array, denoting the interval $[-2,2]^2$. You want to make a picture of the Mandelbrot set , so you need to compute the color of each point.
• The primality test of exercise  labelstring .

There is also the notion that a parallel algorithm can be scalable in space : more processors gives you more memory so that you can run a larger problem.

Exercise

Discuss space scalability in the context of modern processor design.

### 3.14.2 Complexity and scalability of collectives

#### 3.14.2.1 Broadcast

Naive broadcast

Write a broadcast operation where the root does an MPI_Send to each other process.

What is the expected performance of this in terms of $\alpha,\beta$?

Run some tests and confirm.

Simple ring

Let the root only send to the next process, and that one send to its neighbour. This scheme is known as a bucket brigade ; see also section  4.2.3 .

What is the expected performance of this in terms of $\alpha,\beta$?

Run some tests and confirm.

Pipelined ring

In a ring broadcast, each process needs to receive the whole message before it can pass it on. We can increase the efficiency by breaking up the message and sending it in multiple parts. (See figure  labelstring .) This will be advantageous for messages that are long enough that the bandwidth cost dominates the latency.

Assume a send buffer of length more than 1. Divide the send buffer into a number of chunks. The root sends the chunks successively to the next process, and each process sends on whatever chunks it receives.

What is the expected performance of this in terms of $\alpha,\beta$? Why is this better than the simple ring?

Run some tests and confirm.

Recursive doubling

Collectives such as broadcast can be implemented through recursive doubling , where the root sends to another process, then the root and the other process send to two more, those four send to four more, et cetera. However, in an actual physical architecture this scheme can be realized in multiple ways that have drastically different performance.

First consider the implementation where process 0 is the root, and it starts by sending to process 1; then they send to 2 and 3; these four send to 4--7, et cetera. If the architecture is a linear array of procesors, this will lead to contention : multiple messages wanting to go through the same wire. (This is also related to the concept of bisecection bandwidth .)

In the following analyses we will assume wormhole routing : a message sets up a path through the network, reserving the necessary wires, and performing a send in time independent of the distance through the network. That is, the send time for any message can be modeled as $T(n)=\alpha+\beta n$ regardless source and destination, as long as the necessary connections are available.

Exercise

Analyze the running time of a recursive doubling broad cast as just described, with wormhole routing.

Implement this broadcast in terms of blocking MPI send and receive calls. If you have SimGrid available, run tests with a number of parameters.

The alternative, that avoids contention, is to let each doubling stage divide the network into separate halves. That is, process 0 sends to $P/2$, after which these two repeat the algorithm in the two halves of the network, sending to $P/4$ and $3P/4$ respectively.

Exercise

Analyze this variant of recursive doubling. Code it and measure runtimes on SimGrid.

Exercise

Revisit exercise labelstring and replace the blocking calls by non-blocking MPI_Isend  / MPI_Irecv calls.

Make sure to test that the data is correctly propagated.

MPI implementations often have multiple algorithms, which they dynamicaly switch between. Sometimes you can determine the choice yourself through environment variables.

TACC note

## 3.15 Review questions

crumb trail: > mpi-collective > Review questions

For all true/false questions, if you answer that a statement is false, give a one-line explanation.

Review

How would you realize the following scenarios with MPI collectives?

• Let each process compute a random number. You want to print the maximum of these numbers to your screen.

• Each process computes a random number again. Now you want to scale these numbers by their maximum.
• Let each process compute a random number. You want to print on what processor the maximum value is computed.

Review

MPI collectives can be sorted in at least the following categories

1. rooted vs rootless

2. using uniform buffer lengths vs variable length buffers
3. blocking vs non-blocking.

Give examples of each type.

Review

True or false: there are collective routines that do not communicate user data. If true, give an example.

Review

True or false: an MPI_Scatter call puts the same data on each process.

Review

True or false: using the option MPI_IN_PLACE you only need space for a send buffer in MPI_Reduce .

Review

True or false: using the option MPI_IN_PLACE you only need space for a send buffer in MPI_Gather .

Review

Given a distributed array, with every processor storing

double x[N]; // N can vary per processor


give the approximate MPI-based code that computes the maximum value in the array, and leaves the result on every processor.

Review

double data[Nglobal];
int myfirst = /* something */, mylast = /* something */;
for (int i=myfirst; i<mylast; i++) {
if (i>0 && i<N-1) {
process_point( data,i,Nglobal );
}
}
void process_point( double *data,int i,int N ) {
data[i-1] = g(i-1); data[i] = g(i); data[i+1] = g(i+1);
data[i] = f(data[i-1],data[i],data[i+1]);
}


Is this scalable in time? Is this scalable in space?

Review

double data[Nlocal+2]; // include left and right neighbor
int myfirst = /* something */, mylast = myfirst+Nlocal;
for (int i=0; i<Nlocal; i++) {
if (i>0 && i<N-1) {
process_point( data,i,Nlocal );
}
void process_point( double *data,int i0,int n ) {
int i = i0+1;
data[i-1] = g(i-1); data[i] = g(i); data[i+1] = g(i+1);
data[i] = f(data[i-1],data[i],data[i+1]);
}
`

Is this scalable in time? Is this scalable in space?

Review

With data as in the previous question, given the code for normalizing the array, that is, scaling each element so that $\|x\|_2=1$.

Review

Just like MPI_Allreduce is equivalent to MPI_Reduce following by MPI_Bcast , MPI_Reduce_scatter is equivalent to at least one of the following combinations. Select those that are equivalent, and discuss differences in time or space complexity:

1. MPI_Reduce followed by MPI_Scatter ;

2. MPI_Gather followed by MPI_Scatter ;
3. MPI_Allreduce followed by MPI_Scatter ;
4. MPI_Allreduce followed by a local operation (which?);
5. MPI_Allgather followed by a local operation (which?).

Review

Think of at least two algorithms for doing a broadcast? Compare them with regards to asymptotic behavior.

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