Transcript Lecture 4

What is MPI?
 MPI = Message Passing Interface
Specification of message passing libraries for
developers and users
Not a library by itself, but specifies what such a library
should be
Specifies application programming interface (API) for
such libraries
Many libraries implement such APIs on different
platforms – MPI libraries
Goal: provide a standard for writing message
passing programs
Portable, efficient, flexible
Language binding: C, C++, FORTRAN programs
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History & Evolution
 1980s – 1990s: incompatible
libraries and software tools; need
for a standard
 1994, MPI 1.0;
 1995, MPI 1.1, revision and
clarification to MPI 1.0
 Major milestone
 C, FORTRAN
 Fully implemented in all MPI
libraries
 1997, MPI 1.2
 Corrections and clarifications to
MPI 1.1
 1997, MPI 2
 Major extension (and clarifications)
to MPI 1.1
 C++, C, FORTRAN
 Partially implemented in most
libraries; a few full implementations
(e.g. ANL MPICH2)
MPI Evolution
2
Why Use MPI?
Standardization: de facto standard for parallel
computing
Not an IEEE or ISO standard, but “industry standard”
Practically replaced all previous message passing
libraries
Portability: supported on virtually all HPC
platforms
No need to modify source code when migrating to
different machines
Performance: so far the best; high performance
and high scalability
Rich functionality:
If you know 6 MPI functions,
MPI 1.1 – 125 functions
MPI 2 – 152 functions.
you can do almost everything
in parallel.
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Programming Model
 Message passing model: data exchange through explicit
communications.
 For distributed memory, as well as shared-memory
parallel machines
 User has full control (data partition, distribution): needs
to identify parallelism and implement parallel algorithms
using MPI function calls.
 Number of CPUs in computation is static
 New tasks cannot be dynamically spawned during run time (MPI
1.1)
 MPI 2 specifies dynamic process creation and management, but
not available in most implementations.
 Not necessarily a disadvantage
 General assumption: one-to-one mapping of MPI
processes to processors (although not necessarily
always true).
4
MPI 1.1 Overview
Point to point communications
Collective communications
Process groups and communicators
Process topologies
MPI environment management
5
MPI 2 Overview
Dynamic process creation and
management
One-sided communications
MPI Input/Output (Parallel I/O)
Extended collective communications
C++ binding
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MPI Resources
MPI Standard:
http://www.mpi-forum.org/
MPI web sites/tutorials etc, see class web
site
Public domain (free) MPI implementations
MPICH and MPICH2 (from ANL)
LAM MPI
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General MPI Program Structure
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Example
#include <mpi.h>
#include <stdio.h>
On 4 processors:
Hello, I am process 1 among 4 processes
Hello, I am process 2 among 4 processes
Hello, I am process 0 among 4 processes
Hello, I am process 3 among 4 processes
int main(int argc, char **argv)
{
int my_rank, num_cpus;
MPI_Init(&argc, &argv);
MPI_Comm_rank(MPI_COMM_WORLD, &my_rank);
MPI_Comm_size(MPI_COMM_WORLD, &num_cpus);
printf(“Hello, I am process %d among %d processes\n”,
my_rank, num_cpus);
MPI_Finalize();
return 0;
}
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Example
program hello
implicit none
On 4 processors:
Hello, I am process 1 among 4 processes
Hello, I am process 2 among 4 processes
Hello, I am process 0 among 4 processes
Hello, I am process 3 among 4 processes
include ‘mpif.h’
integer :: ierr, my_rank, num_cpus
call MPI_INIT(ierr)
call MPI_COMM_RANK(MPI_COMM_WORLD, my_rank)
call MPI_COMM_SIZE(MPI_COMM_WORLD, num_cpus)
write(*,*) “Hello, I am process “, my_rank, “ among “ &
, num_cpus, “ processes”
call MPI_FINALIZE(ierr)
end program hello
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MPI Header Files
In C/C++:
#include <mpi.h>
In FORTRAN:
include ‘mpif.h’
or (in FORTRAN90 and later)
use MPI
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MPI Naming Conventions
 All names have MPI_ prefix.
 In FORTRAN:
 All subroutine names upper case, last argument is return code
call MPI_XXXX(arg1,arg2,…,ierr)
call MPI_XXXX_XXXX(arg1,arg2,…,ierr)
 A few functions without return code
 In C: mixed uppercase/lowercase
If ierr == MPI_SUCCESS,
Everything is ok; otherwise,
something is wrong.
ierr = MPI_Xxxx(arg1,arg2,…);
ierr = MPI_Xxxx_xxx(arg1,arg2,…);
 MPI constants all uppercase
MPI_COMM_WORLD, MPI_SUCCESS, MPI_DOUBLE, MPI_SUM, …
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Initialization
 Initialization: MPI_Init() initializes MPI environment;
(MPI_Init_thread() if multiple threads)
 Must be called before any other MPI routine (so put it at the
beginning of code) except MPI_Initialized() routine.
 Can be called only once; subsequent calls are erroneous.
 MPI_Initialized() to check if MPI_Init() is called
int MPI_Init(int *argc, char ***argv)
int main(int argc, char ** argv)
{
MPI_Init(&argc, &argv);
int flag;
MPI_Initialized(&flag);
if(flag != 0) … // MPI_Init called
… …
MPI_Finalize();
return 0;
}
MPI_INIT(ierr)
program test
integer ierr
call MPI_INIT(ierr)
…
call MPI_FINALIZE(ierr)
end program test
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Termination
MPI_Finalize() cleans up MPI environment
Must be called before exits.
No other MPI routine can be called after this call,
even MPI_INIT()
Exception: MPI_Initialized() (and
MPI_Get_version(), MPI_Finalized()).
Abnormal termination: MPI_Abort()
Makes a best attempt to abort all tasks
int MPI_Finalize(void)
MPI_FINALIZE(IERR)
integer IERR
int MPI_Abort(MPI_Comm comm, int errorcode)
MPI_ABORT(COMM,ERRORCODE,IERR)
integer COMM, ERRORCODE, IERR
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MPI Processes
 MPI is process-oriented: program consists of multiple
processes, each corresponding to one processor.
 MIMD: Each process runs its own code. In practice, runs
its own copy of the same code (SPMD).
 MPI process and threads: MPI process can contain a
single thread (common case) or multiple threads.
 Most MPI implementations do not support multiple threads.
Needs special processing with that support.
 We will assume a single thread per process from now on.
 MPI processes are identified by their ranks:
 If total nprocs processes in computation, rank ranges from 0,
1, …, nprocs-1. (true in C and FORTRAN).
 nprocs does not change during computation.
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Communicators and
Process Groups
 Communicator: is a group of processes that can
communicate with one another.
 Most MPI routines require a communicator argument to
specify the collection of processes the communication is
based on.
 All processes in the computation form the communicator
MPI_COMM_WORLD.
 MPI_COMM_WORLD is pre-defined by MPI, available anywhere
 Can create subgroups/subcommunicators within
MPI_COMM_WORLD.
 A process may belong to different communicators, and have
different ranks in different communicators.
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How many CPUs, Which one am I …
 How many CPUs: MPI_COMM_SIZE()
 Who am I: MPI_COMM_RANK()
 Can compute data decomposition etc.
 Know total number of grid points, total number of cpus and
current cpu id; can calc which portion of data current cpu is to
work on.
 E.g. Poisson equ on a square
 Ranks also used to specify source and destination of
communications.
…
my_rank value different on different processors !
int my_rank, ncpus;
MPI_Comm_rank(MPI_COMM_WORLD, &my_rank);
MPI_Comm_size(MPI_COMM_WORLD, &ncpus);
…
int MPI_Comm_rank(MPI_Comm comm, int *rank)
int MPI_Comm_size(MPI_Comm comm, int *size)
MPI_COMM_RANK(comm,rank,ierr)
MPI_COMM_SIZE(comm,size,ierr)
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Compiling, Running
 MPI standard does not specify how to start up the
program
 Compiling and running MPI code implementation dependent
 MPI implementations provide utilities/commands for
compiling/running MPI codes
 Compile: mpicc, mpiCC, mpif77, mpif90, mpCC, mpxlf …
mpiCC –o myprog myfile.C (cluster)
mpif90 –o myprog myfile.f90 (cluster)
CC –Ipath_mpi_include –o myprog myfile.C –lmpi (SGI)
mpCC –o myprog myfile.C (IBM)
 Run: mpirun, poe, prun, ibrun …
mpirun –np 2 myprog (cluster)
mpiexec –np 2 myprog (cluster)
poe myprog –node 1 –tasks_per_node 2 … (IBM)
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Example
#include <mpi.h>
#include <stdio.h>
#include <string.h>
int main(int argc, char **argv)
{
char message[256];
int my_rank;
MPI_Status status;
mpirun –np 2 test_hello
Process 1 received: Hello, there!
6 MPI functions:
MPI_Init()
MPI_Finalize()
MPI_Comm_rank()
MPI_Comm_size()
MPI_Send()
MPI_Recv()
MPI_Init(&argc, &argv);
MPI_Comm_rank(MPI_COMM_WORLD,&my_rank);
if(my_rank==0){
strcpy(message,”Hello, there!”);
MPI_Send(message,strlen(message)+1,MPI_CHAR,1,99,MPI_COMM_WORLD);
}
else if(my_rank==1) {
MPI_Recv(message,256,MPI_CHAR,0,99,MPI_COMM_WORLD,&status);
printf(“Process %d received: %s\n”,my_rank,message);
}
MPI_Finalize();
return 0;
}
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MPI Communications
Point-to-point communications
Involves a sender and a receiver, one
processor to another processor
Only the two processors participate in
communication
Collective communications
All processors within a communicator
participate in communication (by calling same
routine, may pass different arguments);
Barrier, reduction operations, gather, …
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Point-to-Point Communications
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Send / Receive
…
MPI_Send(message,strlen(message)+1,MPI_CHAR,1,99,MPI_COMM_WORLD);
MPI_Recv(message,256,MPI_CHAR,0,99,MPI_COMM_WORLD,&status);
…
Message data: what to send/receive?
Where is the message? Where to put it?
What kind of data is it? What is the size?
Message envelope: where to send/receive?
Sender, receiver
Communication context
Message tag.
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Send
int MPI_Send(void *buf,int count,MPI_Datatype datatype,
int dest, int tag, MPI_Comm comm)
MPI_SEND(BUF,COUNT,DATATYPE,DEST,TAG,COMM,IERROR)
<type>BUF(*)
integer COUNT,DATATYPE,DEST,TAG,COMM,IERROR
 buf – memory address of start of message
 count – number of data items
 datatype – what type each data item is (integer,
character, double, float …)
 dest – rank of receiving process
 tag – additional identification of message
 comm – communicator, usually MPI_COMM_WORLD
char message[256];
MPI_Send(message,strlen(message)+1,MPI_CHAR,1,99,MPI_COMM_WORLD); 23
Receive
int MPI_Recv(void *buf,int count,MPI_Datatype datatype,int source,int tag,
MPI_Comm comm,MPI_Status *status)
MPI_RECV(BUF,COUNT,DATATYPE,SOURCE,TAG,COMM,STATUS,IERROR)
<type>BUF(*)
integer COUNT,DATATYPE,SOURCE,TAG,COMM,STATUS(MPI_STATUS_SIZE),IERROR
 buf – initial address of receive buffer
 count – number of elements in receive buffer (size of receive buffer)
 may not equal to the count of items actually received
 Actual number of data items received can be obtained by calling
MPI_Get_count().






datatype – data type in receive buffer
source – rank of sending process
tag – additional identification for message
comm – communicator, usually MPI_COMM_WORLD
status – object containing additional info of received message
ierror – return code
char message[256];
MPI_Recv(message,256,MPI_CHAR,0,99,MPI_COMM_WORLD,&status);
Actual number of data items received can be queried from status object; it may be
smaller than count, but cannot be larger (if larger  overflow error).
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MPI_Recv Status
 In C: MPI_Status structure, 3 members; MPI_Status status
 status.MPI_TAG – tag of received message
 status.MPI_SOURCE – source rank of message
 status.MPI_ERROR – error code
 In FORTRAN: integer array; integer status(MPI_STATUS_SIZE)
 Status(MPI_TAG) – tag of received message
 status(MPI_SOURCE) – source rank of message
 status(MPI_ERROR) – error code
 Length of received message: MPI_Get_count()
Int MPI_Get_count(MPI_Status *status, MPI_Datatype datatype, int *count)
MPI_GET_COUNT(STATUS,DATATYPE,COUNT,IERROR)
integer STATUS(MPI_STATUS_SIZE),DATATYPE,COUNT,IERROR
MPI_Status status;
int count;
…
MPI_Recv(message,256,MPI_CHAR,0,99,MPI_COMM_WORLD,&status);
MPI_Get_count(&status, MPI_CHAR, &count); // count contains actual length 25
Message Data
Consists of count successive entries of
the type indicated by datatype, starting
with the entry at the address buf.
MPI data types:
Basic data types: one for each data type in
hosting languages of C/C++, FORTRAN
Derived data type: will learn later.
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Basic MPI Data Types
MPI datatype
C datatype
MPI datatype
FORTRAN datatype
MPI_CHAR
signed char
MPI_INTEGER
INTEGER
MPI_SHORT
signed short
MPI_INT
signed int
MPI_REAL
REAL
MPI_LONG
signed long
DOUBLE PRECISION
MPI_UNSIGNED_CHAR
unsigned char
MPI_DOUBLE_PREC
ISION
MPI_UNSIGNED_SHORT
unsigned short
MPI_COMPLEX
COMPLEX
MPI_UNSIGNED
unsigned int
MPI_LOGICAL
LOGICAL
MPI_UNSIGNED_LONG
unsigned long int
MPI_CHARACTER
CHARACTER(1)
MPI_DOUBLE
double
MPI_BYTE
MPI_FLOAT
float
MPI_LONG_DOUBLE
long double
MPI_PACKED
MPI_BYTE
MPI_PACKED
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Example
int num_students;
num_students: 0  1
double grade[10];
note: 0  1
char note[1024];
grade: 0  2
int tag1=1001, tag2=1002;
int rank,ncpus;
MPI_Status status;
…
MPI_Comm_rank(MPI_COMM_WORLD,&rank);
… // set num_students,grade,note in rank=0 cpu
if(rank==0){
MPI_Send(&num_students,1,MPI_INT,1,tag1,MPI_COMM_WORLD);
MPI_Send(grade, 10, MPI_DOUBLE,2,tag1,MPI_COMM_WORLD);
MPI_Send(note,strlen(note)+1,MPI_CHAR,1,tag2,MPI_COMM_WORLD);
}
if(rank==1){
MPI_Recv(&num_students,1,MPI_INT,0,tag1,MPI_COMM_WORLD,&status);
MPI_Recv(note,1024,MPI_CHAR,0,tag2,MPI_COMM_WORLD,&status);
}
if(rank==2){
MPI_Recv(grade,10,MPI_DOUBLE,0,tag1,MPI_COMM_WORLD,&status);
}
…
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Message Envelope
 Envelope:
 Source
 Destination
 Tag
 Communicator
 MPI_Send: source is the calling process, destination
process specified;
 Destination can be MPI_PROC_NULL – will return asap, no
effect.
 MPI_Recv: destination is the calling process, source is
specified
 Source can be a wildcard, MPI_ANY_SOURCE.
 Source can also be MPI_PROC_NULL - Return asap, no effect,
receive buffer not modified.
 Tag: non-negative number, 0, 1, …, UB; UB can be
determined by querying MPI environment (UB>=32767).
 For MPI_Recv, can be a wildcard, MPI_ANY_TAG.
 Communicator: specified, usually MPI_COMM_WORLD.
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In Order for a Message To be Received …
Message envelopes must match
Message must be directed to the process calling
MPI_Recv
Message source must match that specified by
MPI_Recv, unless MPI_ANY_SOURCE is specified.
Message tag must match that specified by MPI_Recv,
unless MPI_ANY_TAG is specified
Message communicator must match that specified by
MPI_Recv.
Data type must match
Datatype specified by MPI_Send and MPI_Recv
must match.
(MPI_PACKED can match any other data type.)
Can be more complicated when derived data types
are involved.
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Example
A: 01
double A[10], B[15];
int rank, tag = 1001, tag1=1002;
MPI_Status status;
MPI_Comm_rank(MPI_COMM_WORLD,&rank);
…
if(rank==0)
MPI_Send(A, 10, MPI_DOUBLE, 1, tag, MPI_COMM_WORLD);
else if(rank==1){
MPI_Recv(B, 15, MPI_DOUBLE, 0, tag, MPI_COMM_WORLD, &status); // ok
// MPI_Recv(B, 15, MPI_FLOAT, 0, tag, MPI_COMM_WORLD, &status);  wrong
// MPI_Recv(B,15,MPI_DOUBLE,0,tag1,MPI_COMM_WORLD,&status);  un-match
// MPI_Recv(B,15,MPI_DOUBLE,1,tag,MPI_COMM_WORLD,&status);  un-match
// MPI_Recv(B,15,MPI_DOUBLE,MPI_ANY_SOURCE,tag,MPI_COMM_WORLD,&status);  ok
// MPI_Recv(B,15,MPI_DOUBLE,0,MPI_ANY_TAG,MPI_COMM_WORLD,&status);  ok
}
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Blocking Send/Recv
 MPI_Send is blocking: will not return until the message
data and envelope is safely stored away.
 The message data might be delivered to the matching receive
buffer, or copied to some temporary system buffer.
 After MPI_Send returns, user can safely access or overwrite the
send buffer.
 MPI_Recv is blocking: returns only after the receive
buffer has the received message
 After it returns, the data is here and ready for use.
Non-blocking send/recv: will be discussed later.
Non-blocking calls will return immediately; however, not safe to access
the send/receive buffers. Need to call other functions to complete
send/recv, then safe to access/modify send/receive buffers.
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Buffering
Send and matching receive operations may not
be (and are not) synchronized in reality. MPI
implementation must decide what happens
when send/recv are out of sync.
Consider:
Send occurs 5 seconds before receive is ready;
where is the message when receive is pending?
Multiple sends arrive at the same receiving task which
can receive one send at a time – what happens to the
messages that are backing up?
MPI implementation (not the MPI standard)
decides what happens in these cases. Typically
a system buffer is used to hold data in transit.
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Buffering
System buffer:
Invisible to users and
managed by MPI library
Finite resource that can be
easily exhausted
May exist on sending or
receiving side, or both
May improve performance.
User can attach own buffer
for MPI message buffering.
34
Communication Modes for Send
 Standard mode: MPI_Send
 System decides whether the outgoing message will be buffered
or not
 Usually, small messages  buffering mode; large messages, no
buffering, synchronous mode.
 Buffered mode: MPI_Bsend
 Message will be copied to buffer; Send call then returns
 User can attach own buffer for use
 Synchronous mode: MPI_Ssend
 No buffering.
 Will block until a matching receive starts receiving data
 Ready mode: MPI_Rsend
 Can be used only if a matching receive is already posted; avoid
handshake etc.
 otherwise erroneous.
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Communication Modes
MPI_Send(void *buf, int count, MPI_Datatype datatype, int dest,
int tag, MPI_Comm comm)
MPI_Bsend(void *buf, int count, MPI_Datatype datatype, int dest,
int tag, MPI_Comm comm)
MPI_Ssend(void *buf, int count, MPI_Datatype datatype, int dest,
int tag, MPI_Comm comm)
MPI_Rsend(void *buf, int count, MPI_Datatype datatype, int dest,
int tag, MPI_Comm comm)
There is only one MPI_Recv; will match any send mode
36
Properties
 Order: MPI messages are non-overtaking
 If a sender sends two messages in succession to same
destination, and both match the same receive, then this receive
will receive the first message no matter which message
physically arrives the receiving end first.
 If a receiver posts two receives in succession and both match
the same message, then the first receive will be satisfied.
 Note: if a receive matches two messages from two
different senders, the receive may receive either one
(implementation dependent).
MPI_Comm_rank(MPI_COMM_WORLD, &rank);
if(rank==0) {
MPI_Bsend(buf1,count,MPI_DOUBLE,1,tag,comm);
MPI_Bsend(buf2,count,MPI_DOUBLE,1,tag,comm);
}
else if(rank==1) {
MPI_Recv(buf1,count,MPI_DOUBLE,0,MPI_ANY_TAG,comm,&status);
MPI_Recv(buf2,count,MPI_DOUBLE,0,tag,comm,&status);
}
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Properties
Progress: if a pair of matching send/recv are
initiated on two processes, at least one of them
will complete
Send will complete, unless the receive is satisfied by
some other message
Receive will complete, unless send is consumed by
some other matching receive.
MPI_Comm_rank(MPI_COMM_WORLD, &rank);
If(rank==0) {
MPI_Bsend(buf1,count,MPI_DOUBLE,1,tag1,comm);
MPI_Ssend(buf2,count,MPI_DOUBLE,1,tag2,comm);
} else if(rank==1) {
MPI_Recv(buf1,count,MPI_DOUBLE,0,tag2,comm);
MPI_Recv(buf2,count,MPI_DOUBLE,0,tag1,comm);
}
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Properties
Fairness: no guarantee of fairness
If a message is sent to a destination, and the
destination process repeatedly posts a
receive that matches this send, however the
message may never be received since it is
each time overtaken by another message
sent from another source.
It is user’s responsibility to prevent the
starvation in such situations.
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Properties
Resource limitation:
Pending communications consume system
resources (e.g. buffer space)
Lack of resource may cause error or prevent
execution of MPI call.
e.g. MPI_Bsend that cannot complete due to
lack of buffer space is erroneous.
MPI_Send that cannot complete due to lack
of buffer space will only block, waiting for
buffer space to be available or for a matching
receive.
40
Deadlock
 Deadlock is a state when the program cannot proceed.
 Cyclic dependencies cause deadlock
MPI_Comm_rank(MPI_COMM_WORLD,&rank);
If(rank==0){
MPI_Recv(buf1,count,MPI_DOUBLE,1,tag,comm);
MPI_Send(buf2,count,MPI_DOUBLE,1,tag,comm);
} else if (rank==1) {
MPI_Recv(buf1,count,MPI_DOUBLE,0,tag,comm);
MPI_Send(buf2,count,MPI_DOUBLE,0,tag,comm);
}
0
1
MPI_Comm_rank(MPI_COMM_WORLD,&rank);
If(rank==0){
MPI_Ssend(buf1,count,MPI_DOUBLE,1,tag,comm);
MPI_Recv(buf2,count,MPI_DOUBLE,1,tag,comm);
} else if (rank==1) {
MPI_Ssend(buf1,count,MPI_DOUBLE,0,tag,comm);
MPI_Recv(buf2,count,MPI_DOUBLE,0,tag,comm);
}
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Deadlock
Lack of buffer space may also cause
deadlock
MPI_Comm_rank(MPI_COMM_WORLD,&rank);
If(rank==0){
MPI_Send(buf1,count,MPI_DOUBLE,1,tag,comm);
MPI_Recv(buf2,count,MPI_DOUBLE,1,tag,comm);
} else if (rank==1) {
MPI_Send(buf1,count,MPI_DOUBLE,0,tag,comm);
MPI_Recv(buf2,count,MPI_DOUBLE,0,tag,comm);
}
Deadlock if not enough
buffer space!
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Send-receive
 Two remedies: non-blocking communication, send-recv
 MPI_SENDRECV: combine send and recv in one call
 Useful in shift operations; Avoid possible deadlock with circular
shift and like operations
 Equivalent to: execute a nonblocking send and a nonblocking
recv, and then wait for them to complete
int MPI_Sendrecv(void *sendbuf, int sendcount, MPI_Datatype sendtype,
int dest, int sendtag,
void *recvbuf, int recvcount, MPI_Datatype recvtype,
int source, int recvtag,
MPI_Comm comm, MPI_Status *status)
MPI_SENDRECV(SENDBUF, SENDCOUNT, SENDTYPE, DEST, SENDTAG,
RECVBUF, RECVCOUNT, RECVTYPE, SOURCE, RECVTAG,
COMM, STATUS, IERROR)
<type> SENDBUF(*), RECVBUF(*)
INTEGER SENDCOUNT, SENDTYPE, SENDTAG, DEST, RECVCOUNT, RECVTYPE, SOURCE,
RECVTAG, COMM, IERROR, STATUS(MPI_STATUS_SIZE)
*sendbuf and *recvbuf may not be the same memory address
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Example
#include <mpi.h>
#include <stdio.h>
int main(int argc, char **argv)
{
int my_rank, ncpus;
int left_neighbor, right_neighbor;
int data_received=-1;
int send_tag = 101, recv_tag=101;
MPI_Status status;
mpirun –np 4 test_shift
Among 4 processes, process 3 received from right neighbor: 0
Among 4 processes, process 2 received from right neighbor: 3
Among 4 processes, process 0 received from right neighbor: 1
Among 4 processes, process 1 received from right neighbor: 2
MPI_Init(&argc, &argv);
MPI_Comm_rank(MPI_COMM_WORLD, &my_rank);
MPI_Comm_size(MPI_COMM_WORLD, &ncpus);
left_neighbor = (my_rank-1 + ncpus)%ncpus;
right_neighbor = (my_rank+1)%ncpus;
MPI_Sendrecv(&my_rank, 1, MPI_INT, left_neighbor, send_tag,
&data_received, 1, MPI_INT, right_neighbor, recv_tag,
MPI_COMM_WORLD, &status);
printf("Among %d processes, process %d received from right neighbor: %d\n",
ncpus, my_rank, data_received);
// clean up
MPI_Finalize();
return 0;
}
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