I'm not sure that I am correctly understanding what MPI_Scatterv is supposed to do. I have 79 items to scatter amounts a variable amount of nodes. However, when I use the MPI_Scatterv command I get ridiculous numbers (as if the array elements of my receiving buffer are uninitialized). Here is the relevant code snippet:
MPI_Init(&argc, &argv);
int id, procs;
MPI_Comm_rank(MPI_COMM_WORLD, &id);
MPI_Comm_size(MPI_COMM_WORLD, &procs);
//Assign each file a number and figure out how many files should be
//assigned to each node
int file_numbers[files.size()];
int send_counts[nodes] = {0};
int displacements[nodes] = {0};
for (int i = 0; i < files.size(); i++)
{
file_numbers[i] = i;
send_counts[i%nodes]++;
}
//figure out the displacements
int sum = 0;
for (int i = 0; i < nodes; i++)
{
displacements[i] = sum;
sum += send_counts[i];
}
//Create a receiving buffer
int *rec_buf = new int[79];
if (id == 0)
{
MPI_Scatterv(&file_numbers, send_counts, displacements, MPI_INT, rec_buf, 79, MPI_INT, 0, MPI_COMM_WORLD);
}
cout << "got here " << id << " checkpoint 1" << endl;
cout << id << ": " << rec_buf[0] << endl;
cout << "got here " << id << " checkpoint 2" << endl;
MPI_Barrier(MPI_COMM_WORLD);
free(rec_buf);
MPI_Finalize();
When I run that code I receive this output:
got here 1 checkpoint 1
1: -1168572184
got here 1 checkpoint 2
got here 2 checkpoint 1
2: 804847848
got here 2 checkpoint 2
got here 3 checkpoint 1
3: 1364787432
got here 3 checkpoint 2
got here 4 checkpoint 1
4: 903413992
got here 4 checkpoint 2
got here 0 checkpoint 1
0: 0
got here 0 checkpoint 2
I read the documentation for OpenMPI and looked through some code examples, I'm not sure what I'm missing any help would be great!
One of the most common MPI mistakes strikes again:
if (id == 0) // <---- PROBLEM
{
MPI_Scatterv(&file_numbers, send_counts, displacements, MPI_INT,
rec_buf, 79, MPI_INT, 0, MPI_COMM_WORLD);
}
MPI_SCATTERV is a collective MPI operation. Collective operations must be executed by all processes in the specified communicator in order to complete successfully. You are executing it only in rank 0 and that's why only it gets the correct values.
Solution: remove the conditional if (...).
But there is another subtle mistake here. Since collective operations do not provide any status output, the MPI standard enforces strict matching of the number of elements sent to some rank and the number of elements the rank is willing to receive. In your case the receiver always specifies 79 elements which might not match the corresponding number in send_counts. You should instead use:
MPI_Scatterv(file_numbers, send_counts, displacements, MPI_INT,
rec_buf, send_counts[id], MPI_INT,
0, MPI_COMM_WORLD);
Also note the following discrepancy in your code that might as well be a typo while posting the question here:
MPI_Comm_size(MPI_COMM_WORLD, &procs);
^^^^^
int send_counts[nodes] = {0};
^^^^^
int displacements[nodes] = {0};
^^^^^
While you obtain the number of ranks in the procs variable, nodes is used in the rest of your code. I guess nodes should be replaced by procs.
Related
I'm attempting to solve a homework problem to debug the following unit test.
Basically the master process generates random integers and sends them to child processes to check for primality, the result is communicated back to the master process, and the algorithm ends.
I know that the loops should be replaced with collective communication, however that is a different part of the question. I want to understand why this code I have here is causing deadlock.
From reading other questions, I know that the number of sends/receives should match each other. However I don't see how this isn't the case in my code.
The current behaviour is that a prime is found, sent back to the master process, at which point the program just hangs - until it's canceled manually using ctrl-C.
I understand that this isn't the idiomatic way of solving this problem, but would really like to know exactly where the bug is in this approach.
thanks!
TEST_CASE("3a - Finding prime numbers", "[MPI]" )
{
int rank, size;
MPI_Comm_rank(MPI_COMM_WORLD, &rank);
MPI_Comm_size(MPI_COMM_WORLD, &size);
// Random number generation
std::minstd_rand generator;
unsigned min(2342), max(12342340);
std::uniform_int_distribution<> distribution(min, max);
// candidates too big, one of the size values is the master node
std::vector<unsigned> candidates(size - 1);
// Main loop continues until a prime is found
int found_prime(0);
while (found_prime == 0) {
if (rank == 0) {
// Create some candidate numbers for each process to test
std::generate(candidates.begin(), candidates.end(),
[&]() { return distribution(generator); });
// Send one to each worker
for (int worker(1); worker < size; ++worker) {
int rc = MPI_Ssend(&candidates[worker - 1], 1, MPI_UNSIGNED,
worker, 0, MPI_COMM_WORLD);
REQUIRE(rc == MPI_SUCCESS);
}
// Receive whether it was prime
for (int worker(1); worker < size; ++worker) {
unsigned result;
int rc = MPI_Recv(&result, 1, MPI_UNSIGNED, worker, 0,
MPI_COMM_WORLD, MPI_STATUS_IGNORE);
REQUIRE(rc == MPI_SUCCESS);
if (result == 1) {
found_prime = candidates[worker - 1];
std::cout << "Worker " << worker << " found prime "
<< found_prime << std::endl;
}
}
} else {
// Receive the candidate to check
unsigned candidate;
int rc = MPI_Recv(&candidate, 1, MPI_UNSIGNED, 0, 0, MPI_COMM_WORLD,
MPI_STATUS_IGNORE);
REQUIRE(rc == MPI_SUCCESS);
// Do the check
unsigned is_prime = mp::IsPrime(candidate) ? 1 : 0;
// Return the result
rc = MPI_Ssend(&is_prime, 1, MPI_UNSIGNED, 0, 0, MPI_COMM_WORLD);
REQUIRE(rc == MPI_SUCCESS);
}
}
std::cout << "Finished" << rank << std::endl;
}
I know nothing about MPI, but in your code, if rank != 0, the while loop can never be exited because found_prime is never set in the else branch (and rank is never changed too).
Edit:
As #DanielLangr said, the slaves will need a way to find out that there's no more work to come and exit (the loop).
I am writing a program that calculates the sum of every number up to 1000. For example, 1+2+3+4+5....+100. First, I assign summation jobs to 10 processors: Processor 0 gets 1-100, Processor 1 gets 101-200 and so on. The sums are stored in an array.
After all summations have been done parallelly, processors send their values to Processor 0 (Processor 0 receives values using nonblocking send/recv) and Processor 0 sums up all the values and displays the result.
Here is the code:
#include <mpi.h>
#include <iostream>
using namespace std;
int summation(int, int);
int main(int argc, char ** argv)
{
int * array;
int total_proc;
int curr_proc;
int limit = 0;
int partial_sum = 0;
int upperlimit = 0, lowerlimit = 0;
MPI_Init(&argc, &argv);
MPI_Comm_size(MPI_COMM_WORLD, &total_proc);
MPI_Comm_rank(MPI_COMM_WORLD, &curr_proc);
MPI_Request send_request, recv_request;
/* checking if 1000 is divisible by number of procs, else quit */
if(1000 % total_proc != 0)
{
MPI_Finalize();
if(curr_proc == 0)
cout << "**** 1000 is not divisible by " << total_proc << " ...quitting..."<< endl;
return 0;
}
/* number of partial summations */
limit = 1000/total_proc;
array = new int [total_proc];
/* assigning jobs to processors */
for(int i = 0; i < total_proc; i++)
{
if(curr_proc == i)
{
upperlimit = upperlimit + limit;
lowerlimit = (upperlimit - limit) + 1;
partial_sum = summation(upperlimit, lowerlimit);
array[i] = partial_sum;
}
else
{
upperlimit = upperlimit + limit;
lowerlimit = (upperlimit - limit) + 1;
}
}
cout << "** Partial Sum From Process " << curr_proc << " is " << array[curr_proc] << endl;
/* send and receive - non blocking */
for(int i = 1; i < total_proc; i++)
{
if(curr_proc == i) /* (i = current processor) */
{
MPI_Isend(&array[i], 1, MPI_INT, 0, i, MPI_COMM_WORLD, &send_request);
cout << "-> Process " << i << " sent " << array[i] << " to Process 0" << endl;
MPI_Irecv(&array[i], 1, MPI_INT, i, i, MPI_COMM_WORLD, &recv_request);
//cout << "<- Process 0 received " << array[i] << " from Process " << i << endl;
}
}
MPI_Finalize();
if(curr_proc == 0)
{
for(int i = 1; i < total_proc; i++)
array[0] = array[0] + array[i];
cout << "Sum is " << array[0] << endl;
}
return 0;
}
int summation(int u, int l)
{
int result = 0;
for(int i = l; i <= u; i++)
result = result + i;
return result;
}
Output:
** Partial Sum From Process 0 is 5050
** Partial Sum From Process 3 is 35050
-> Process 3 sent 35050 to Process 0
<- Process 0 received 35050 from Process 3
** Partial Sum From Process 4 is 45050
-> Process 4 sent 45050 to Process 0
<- Process 0 received 45050 from Process 4
** Partial Sum From Process 5 is 55050
-> Process 5 sent 55050 to Process 0
<- Process 0 received 55050 from Process 5
** Partial Sum From Process 6 is 65050
** Partial Sum From Process 8 is 85050
-> Process 8 sent 85050 to Process 0
<- Process 0 received 85050 from Process 8
-> Process 6 sent 65050 to Process 0
** Partial Sum From Process 1 is 15050
** Partial Sum From Process 2 is 25050
-> Process 2 sent 25050 to Process 0
<- Process 0 received 25050 from Process 2
<- Process 0 received 65050 from Process 6
** Partial Sum From Process 7 is 75050
-> Process 1 sent 15050 to Process 0
<- Process 0 received 15050 from Process 1
-> Process 7 sent 75050 to Process 0
<- Process 0 received 75050 from Process 7
** Partial Sum From Process 9 is 95050
-> Process 9 sent 95050 to Process 0
<- Process 0 received 95050 from Process 9
Sum is -1544080023
Printing the contents of the array:
5050
536870912
-1579286148
-268433415
501219332
32666
501222192
32666
1
0
I'd like to know what is causing this.
If I print the array before MPI_Finalize is invoked it works fine.
The most important flaw your program has is how you divide the work. In MPI, every process is executing the main function. Therefore, you must ensure that all the processes execute your summation function if you want them to collaborate on building the result.
You don't need the for loop. Every process is executing the main separately. They just have different curr_proc values, and you can compute which portion of the job they have to perform based on that:
/* assigning jobs to processors */
int chunk_size = 1000 / total_proc;
lowerlimit = curr_proc * chunk_size;
upperlimit = (curr_proc+1) * chunk_size;
partial_sum = summation(upperlimit, lowerlimit);
Then, how the master process receives all the other processes' partial sum is not correct.
MPI rank values (curr_proc) start form 0 up to MPI_Comm_size output value (total_proc-1).
Only the process #1 is sending/receiving data.
You are using the immediate version of send and receive: MPI_Isend and MPI_recv but you are not waiting until those requests are completed. You should use MPI_Waitall for that purpose.
The correct version would be something like the following:
if( curr_proc == 0 ) {
// master process receives all data
for( int i = 1; i < total_proc; i++ )
MPI_Recv( &array[i], MPI_INT, 1, i, 0, MPI_COMM_WORLD );
} else {
// other processes send data to the master
MPI_Send( &partial_sum, MPI_INT, 1, 0, 0, MPI_COMM_WORLD );
}
This all-to-one communication pattern is known as gather. In MPI there is a function which already performs this functionality: MPI_Gather.
Finally, what you intent to perform is known as reduction: take a given amount of numeric values and generate a single output value by continuously performing a single operation (a sum, in your case). In MPI there is a function which does that, too: MPI_Reduce.
I strongly suggest you to do some basic guided exercises before trying to make your own. MPI is difficult to understand at the beginning. Building a good base is vital for you to be able to add complexity later on. A hands on tutorial is also a good way of getting started into MPI.
EDIT: Forgot to mention that you don't need to enforce an even divission of the problem size (1000 in this case) by the number of resources (total_proc). Depending on the case, you can either assign the remainder to a single process:
chunk_size = 1000 / total_proc;
if( curr_proc == 0 )
chunk_size += 1000 % total_proc;
Or balance it as much as possible:
int remainder = curr_proc < ( 1000 % proc )? 1 : 0;
lowerlimit = curr_proc * chunk_size /* as usual */
+ curr_proc; /* cumulative remainder */
upperlimit = (curr_proc + 1) * chunk_size /* as usual */
+ remainder; /* curr_proc remainder */
The second case, the load unbalance will be as much as 1, while in the first case the load unbalance can reach total_proc-1 in the worst case.
You're only initializing array[i], the element that corresponds to the curr_proc id. The other elements in that array will be uninitialized, resulting in random values. In your send/receive print loop, you only access the initialized element.
I'm not that familiar with MPI so I'm guessing, but you might want to allocate array before calling MPI_Init. Or call MPI_Receive on process 0, not each individual one.
I'm trying to scatter values among processes belonging to an hypercube group (quicksort project).
Depending on the amount of processes I either create a new communicator excluding excessive processes, or I duplicate MPI_COMM_WORLD if it fits exactly any hypercube (power of 2).
In both cases, processes other than 0 receive their data, but:
- On first scenario, process 0 throws a segmentation fault 11
- On second scenario, nothing faults, but process 0 received values are gibberish.
NOTE: If I try a regular MPI_Scatter everything works well.
//Input
vector<int> LoadFromFile();
int d; //dimension of hypercube
int p; //active processes
int idle; //idle processes
vector<int> values; //values loaded
int arraySize; //number of total values to distribute
int main(int argc, char* argv[])
{
int mpiWorldRank;
int mpiWorldSize;
int mpiRank;
int mpiSize;
MPI_Init(&argc, &argv);
MPI_Comm_rank(MPI_COMM_WORLD, &mpiWorldRank);
MPI_Comm_size(MPI_COMM_WORLD, &mpiWorldSize);
MPI_Comm MPI_COMM_HYPERCUBE;
d = log2(mpiWorldSize);
p = pow(2, d); //Number of processes belonging to the hypercube
idle = mpiWorldSize - p; //number of processes in excess
int toExclude[idle]; //array of idle processes to exclude from communicator
int sendCounts[p]; //array of values sizes to be sent to processes
//
int i = 0;
while (i < idle)
{
toExclude[i] = mpiWorldSize - 1 - i;
++i;
}
//CREATING HYPERCUBE GROUP: Group of size of power of 2 -----------------
MPI_Group world_group;
MPI_Comm_group(MPI_COMM_WORLD, &world_group);
// Remove excessive processors if any from communicator
if (idle > 0)
{
MPI_Group newGroup;
MPI_Group_excl(world_group, 1, toExclude, &newGroup);
MPI_Comm_create(MPI_COMM_WORLD, newGroup, &MPI_COMM_HYPERCUBE);
//Abort any processor not part of the hypercube.
if (mpiWorldRank > p)
{
cout << "aborting: " << mpiWorldRank <<endl;
MPI_Finalize();
return 0;
}
}
else
{
MPI_Comm_dup(MPI_COMM_WORLD, &MPI_COMM_HYPERCUBE);
}
MPI_Comm_rank(MPI_COMM_HYPERCUBE, &mpiRank);
MPI_Comm_size(MPI_COMM_HYPERCUBE, &mpiSize);
//END OF: CREATING HYPERCUBE GROUP --------------------------
if (mpiRank == 0)
{
//STEP1: Read input
values = LoadFromFile();
arraySize = values.size();
}
//Transforming input vector into an array
int valuesArray[values.size()];
if(mpiRank == 0)
{
copy(values.begin(), values.end(), valuesArray);
}
//Broadcast input size to all processes
MPI_Bcast(&arraySize, 1, MPI_INT, 0, MPI_COMM_HYPERCUBE);
//MPI_Scatterv: determining size of arrays to be received and displacement
int nmin = arraySize / p;
int remainingData = arraySize % p;
int displs[p];
int recvCount;
int k = 0;
for (i=0; i<p; i++)
{
sendCounts[i] = i < remainingData
? nmin+1
: nmin;
displs[i] = k;
k += sendCounts[i];
}
recvCount = sendCounts[mpiRank];
int recvValues[recvCount];
//Following MPI_Scatter works well:
// MPI_Scatter(&valuesArray, 13, MPI_INT, recvValues , 13, MPI_INT, 0, MPI_COMM_HYPERCUBE);
MPI_Scatterv(&valuesArray, sendCounts, displs, MPI_INT, recvValues , recvCount, MPI_INT, 0, MPI_COMM_HYPERCUBE);
int j = 0;
while (j < recvCount)
{
cout << "rank " << mpiRank << " received: " << recvValues[j] << endl;
++j;
}
MPI_Finalize();
return 0;
}
First of all, you are supplying wrong arguments to MPI_Group_excl:
MPI_Group_excl(world_group, 1, toExclude, &newGroup);
// ^
The second argument specifies the number of entries in the exclusion list and should therefore be equal to idle. Since you are excluding a single rank only, the resulting group has mpiWorldSize-1 ranks and hence MPI_Scatterv expects that both sendCounts[] and displs[] have that many elements. Of those only p elements are properly initialised and and the rest are random, therefore MPI_Scatterv crashes in the root.
Another error is the code that aborts the idle processes: it should read if (mpiWorldRank >= p).
I would recommend that the entire exclusion code is replaced by a single call to MPI_Comm_split instead:
MPI_Comm comm_hypercube;
int colour = mpiWorldRank >= p ? MPI_UNDEFINED : 0;
MPI_Comm_split(MPI_COMM_WORLD, colour, mpiWorldRank, &comm_hypercube);
if (comm_hypercube == MPI_COMM_NULL)
{
MPI_Finalize();
return 0;
}
When no process supplies MPI_UNDEFINED as its colour, the call is equivalent to MPI_Comm_dup.
Note that you should avoid using in your code names starting with MPI_ as those could clash with symbols from the MPI implementation.
Additional note: std::vector<T> uses contiguous storage, therefore you could do without copying the elements into a regular array and simply provide the address of the first element in the call to MPI_Scatter(v):
MPI_Scatterv(&values[0], ...);
I've been working on an MPI project in which the slaves all send data back to the master. For some reason the master will only receive the data if I do 2 consecutive Sends in a row. This is very odd and I think it is causing some other weird problems I am getting. Any idea what would cause this? I think the first send is sending some kind of junk data or something. The sends are the exact same line of code though.
EDIT: Code below...
if (numProcs > 0)
MPI_Barrier( MPI_COMM_WORLD ) ; //only wait if there are other processes to wait for
if (rank != 0)
{
MPI_Send(handArray, 10, MPI_DOUBLE, 0, TAG_HAND, MPI_COMM_WORLD);
MPI_Send(handArray, 10, MPI_DOUBLE, 0, TAG_HAND, MPI_COMM_WORLD);
}
//8. After the main loop the master process receives and sums together the hand counts array
// from each slave process.
else
{
int activeProcs = numProcs - 1;
getHandsFromSlaves( activeProcs, handArray );
then the master proceeds to print some data...
Here is the getHands FromSlaves method. Please note I have also tried using blocking calls for this as well with the same problems.
void getHandsFromSlaves( int& activeCount, double handTotals[10] ){
static MPI_Request request;
static int msgBuff, recvFlag;
static double handBuff[10];
MPI_Status status;
while (activeCount > 0)
{
if( request )
{
// Already listening for a message
// Test to see if message has been received
MPI_Test( &request, &recvFlag, &status );
//cout << "TAG: " << status.MPI_TAG << " SOURCE: "<< status.MPI_SOURCE << " ERROR: " << status.MPI_ERROR << endl;
if( recvFlag )
{
// Message received
if( status.MPI_TAG == TAG_HAND )
{
cout << "Hand Received!" << endl;
for(int m = 0; m < 10; ++m)
{
handTotals[m] += handBuff[m];
}
activeCount--;
}
else
{
//error report... what happened?
cout << "TAG: " << status.MPI_TAG << " SOURCE: "<< status.MPI_SOURCE << " ERROR: " << status.MPI_ERROR << endl;
}
// Reset the request handle
request = 0;
}
}
if( !request && activeCount > 0 )
// Start listening again
MPI_Irecv(&handBuff, 10, MPI_DOUBLE, MPI_ANY_SOURCE, MPI_ANY_TAG, MPI_COMM_WORLD, &request);
}
}
Well, you're probably trying to process one too many messages because your request variable is undefined on entering your getHandsFromSlaves() routine. Since on enter, request is almost certainly non-zero, you immediately try to MPI_Test for a message something even though you haven't posted an Irecv.
In fact, there's a lot of really strange things about the code excerpt posted here. Why are the local variables static? Why would you implement your own busywait on MPI_Test() instead of using MPI_Wait()? Why are you using non-blocking receives at all if you're not doing anything useful between receives? And indeed, if you're just summing up all of the arrays anyway, why are you doing individual point-to-point receives at all instead of doing an MPI_Reduce()?
The following much shorter code seeems to do what you're trying to do above:
#include <stdio.h>
#include <mpi.h>
int main (int argc, char **argv) {
int rank, numProcs;
MPI_Init(&argc, &argv);
MPI_Comm_rank(MPI_COMM_WORLD, &rank);
MPI_Comm_size(MPI_COMM_WORLD, &numProcs);
double handArray[10];
double handTotal[10];
for (int i=0; i<10; i++)
handArray[i] = rank + i;
if (rank == 0) // Since apparently rank 0 doesn't do anything
{
for (int i=0; i<10; i++)
handArray[i] = 0;
}
MPI_Reduce(handArray, handTotal, 10, MPI_DOUBLE, MPI_SUM, 0, MPI_COMM_WORLD);
if (rank == 0) {
printf("Hand Totals= \n");
for (int i=0; i<10; i++)
printf(" %lf ", handTotal[i]);
printf("\n");
}
MPI_Finalize();
}
We only have been working on MPI for about one day in my computer programming class, and I now have to write a program for it. I am to write a program that organizes processes into two rings.
The first ring begins with process 0 and proceeds to send a message to the next even process and the last process sending its message back to process 0. For example, 0--> 2 --> 4 --> 6 --> 8 --> 0 (but it goes all the way up to 32 instead of 8). The next ring is the same, but begins with process 1 and sends to the previous off process and then back to 1. For example, 1--> 9--> 7--> 5 --> 3--> 1.
Also, I am supposed to find the max, min, and average of a very large array of integer numbers. I will have to scatter the array into pieces to each process, have each process compute a partial answer, and then reduce back the answer together on process 0 after everyone is done.
Finally, I am to scatter across the processes and each process will have to count how many of each letter appears in a section. That part really makes no sense to me. But we have just learned the very basics, so no fancy stuff please! Here's what I have so far, I have commented out some things to just remind myself of some stuff, so please ignore if necessary.
#include <iostream>
#include "mpi.h"
using namespace std;
// compile: mpicxx program.cpp
// run: mpirun -np 4 ./a.out
int main(int argc, char *argv[])
{
int rank; // unique number associated with each core
int size; // total number of cores
char message[80];
char recvd[80];
int prev_node, next_node;
int tag;
MPI_Status status;
// start MPI interface
MPI_Init(&argc, &argv);
MPI_Comm_size(MPI_COMM_WORLD, &size);
MPI_Comm_rank(MPI_COMM_WORLD, &rank);
sprintf(message, "Heeeelp! from %d", rank);
MPI_Barrier(MPI_COMM_WORLD);
next_node = (rank + 2) % size;
prev_node = (size + rank - 2) % size;
tag = 0;
if (rank % 2) {
MPI_Send(&message, 80, MPI_CHAR, prev_node, tag, MPI_COMM_WORLD);
MPI_Recv(&recvd, 80, MPI_CHAR, next_node, tag, MPI_COMM_WORLD, &status);
} else {
MPI_Send(&message, 80, MPI_CHAR, next_node, tag, MPI_COMM_WORLD);
MPI_Recv(&recvd, 80, MPI_CHAR, prev_node, tag, MPI_COMM_WORLD, &status);
}
cout << "* Rank " << rank << ": " << recvd << endl;
//max
int large_array[100];
rank == 0;
int max = 0;
MPI_Scatter(&large_array, 1, MPI_INT, large_array, 1, MPI_INT, 0, MPI_COMM_WORLD);
MPI_Reduce(&message, max, 1, MPI_INT, MPI_MAX, 0, MPI_COMM_WORLD);
MPI_Finalize();
return 0;
}
I have a small suggestion about this:
dest = rank + 2;
if (rank == size - 1)
dest = 0;
source = rank - 2;
if (rank == 0)
source = size - 1;
I think dest and source, as names, are going to be confusing (as both are destinations of messages, depending on the value of rank). Using the % operator might help improve clarity:
next_node = (rank + 2) % size;
prev_node = (size + rank - 2) % size;
You can select whether to receive or send to next_node and prev_node based on the value of rank % 2:
if (rank % 2) {
MPI_Send(&message, 80, MPI_CHAR, prev_node, tag, MPI_COMM_WORLD);
MPI_Recv(&message, 80, MPI_CHAR, next_node, tag, MPI_COMM_WORLD, &status);
} else {
MPI_Send(&message, 80, MPI_CHAR, next_node, tag, MPI_COMM_WORLD);
MPI_Recv(&message, 80, MPI_CHAR, prev_node, tag, MPI_COMM_WORLD, &status);
}
Doing this once or twice is fine, but if you find your code littered with these sorts of switches, it'd make sense to place these ring routines in a function and pass in the next and previous nodes as parameters.
When it comes time to distribute your arrays of numbers and arrays of characters, keep in mind that n / size will leave a remainder of n % size elements at the end of your array that also need to be handled. (Probably on the master node, just for simplicity.)
I added a few more output statements (and a place to store the message from the other nodes) and the simple rings program works as expected:
$ mpirun -np 16 ./a.out | sort -k3n
* Rank 0: Heeeelp! from 14
* Rank 1: Heeeelp! from 3
* Rank 2: Heeeelp! from 0
* Rank 3: Heeeelp! from 5
* Rank 4: Heeeelp! from 2
* Rank 5: Heeeelp! from 7
* Rank 6: Heeeelp! from 4
* Rank 7: Heeeelp! from 9
* Rank 8: Heeeelp! from 6
* Rank 9: Heeeelp! from 11
* Rank 10: Heeeelp! from 8
* Rank 11: Heeeelp! from 13
* Rank 12: Heeeelp! from 10
* Rank 13: Heeeelp! from 15
* Rank 14: Heeeelp! from 12
* Rank 15: Heeeelp! from 1
You can see the two rings there, each in their own direction:
#include <iostream>
#include "mpi.h"
using namespace std;
// compile: mpicxx program.cpp
// run: mpirun -np 4 ./a.out
int main(int argc, char *argv[])
{
int rank; // unique number associated with each core
int size; // total number of cores
char message[80];
char recvd[80];
int prev_node, next_node;
int tag;
MPI_Status status;
// start MPI interface
MPI_Init(&argc, &argv);
MPI_Comm_size(MPI_COMM_WORLD, &size);
MPI_Comm_rank(MPI_COMM_WORLD, &rank);
sprintf(message, "Heeeelp! from %d", rank);
// cout << "Rank " << rank << ": " << message << endl;
MPI_Barrier(MPI_COMM_WORLD);
next_node = (rank + 2) % size;
prev_node = (size + rank - 2) % size;
tag = 0;
if (rank % 2) {
MPI_Send(&message, 80, MPI_CHAR, prev_node, tag, MPI_COMM_WORLD);
MPI_Recv(&recvd, 80, MPI_CHAR, next_node, tag, MPI_COMM_WORLD, &status);
} else {
MPI_Send(&message, 80, MPI_CHAR, next_node, tag, MPI_COMM_WORLD);
MPI_Recv(&recvd, 80, MPI_CHAR, prev_node, tag, MPI_COMM_WORLD, &status);
}
cout << "* Rank " << rank << ": " << recvd << endl;
//cout << "After - Rank " << rank << ": " << message << endl;
// end MPI interface
MPI_Finalize();
return 0;
}
When it comes time to write the larger programs (array min, max, avg, and word counts), you'll need to change things slightly: only rank == 0 will be sending messages at the start; it will send to all the other processes their pieces of the puzzle. All the other processes will receive, do the work, then send back the results. rank == 0 will then need to integrate the results from all of them into a coherent single answer.