In my experience, when I update a varible in 1 task the variable is not updated in other tasks even if the first task that updated the variable is done executing. For example given the code,
int nThreads = atoi(argv[1]);
omp_set_num_threads(nThreads);
int currentInt = 0;
int numEdges = 1000000;
#pragma omp parallel shared(currentInt)
{
#pragma omp single
{
#pragma omp task shared(currentInt)
{
printf("I am doing kruskals: Thread %d\n", omp_get_thread_num());
while(currentInt < numEdges)
{
currentInt++;
}
printf("Kruskals Done! %d\n", currentInt);
#pragma omp shared(currentInt)
{
for(int i = 0; i < 10000000; i++){
}
printf("Helper: Current Int %d Thread %d \n", currentInt, omp_get_thread_num());
}
}
#pragma omp taskwait
}
}
It will always print currentInt 0. Even if the first task finishes before the second. I need this because I am trying to parallize an algorithm where a have a sequential task going through a large array and many parallel tasks excuting simultanously on parts of that array and once the sequential task reaches the portion of the array that a parallel task is working on the parallel task can stop itself because it is no longer needed. The parallel and sequential tasks share no dependancies so that is not a problem.
Any help will be appreciated.
I have a theoretical OpenMP question for you all.
Imagine I do the following:
#pragma omp parallel
{
#pragma omp single
{
while (!empty(linkedList)) {
#pragma omp task
doWork();
}
}
}
What happens if doWork() adds elements back into the list?
My worry is that the single thread that is spinning of the tasks will terminate before the threads doing the tasks can finish. This might mean that any elements that gets added back onto the list by the doWork function are missed. Does anybody know how this works?
Thanks!
Just embed the generator loop into another loop and use taskwait in between to ensure that all tasks have finished executing. You must also ensure proper locking of the linked list in the concurrent parts of the code, e.g. by the use of critical sections (as shown below) or finer-grained locks.
doWork(element e)
{
// ...
#pragma omp critical(listOps)
insertElement(linkedList, newElement);
// ...
}
#pragma omp parallel
{
#pragma omp single
{
do
{
#pragma omp critical(listOps)
while (!empty(linkedList)) {
element e = removeElement(linkedList);
#pragma omp task
doWork(e);
}
#pragma omp taskwait
} while (!empty(linkedList));
}
}
This is my program which calculates sum of 10000 element which is assign to 1. The sum should be 5000 for 1st thread and 5000 for other but for every run it is giving different output
#include<omp.h>
#include<stdio.h>
int main()
{
int i,sum1=0,sum2=0,a[10000],sum_final=0;
for(i=0;i<10000;i++)
{
a[i]=1;
}
#pragma omp parallel
{
if(omp_get_thread_num()==0)
{
for(i=0;i<5000;i++)
{
sum1+=a[i];
}
printf("Sum1 is %d\n",sum1);
}
if(omp_get_thread_num()==1)
{
for(i=5000;i<10000;i++)
{
sum2+=a[i];
}
printf("Sum2 is %d\n",sum2);
}
}
return 0;
}
Your loop counter should be private. I think you should try
#pragma omp parallel private(i)
I want to map tasks to three threads as follows:
Each of taskA, taskB, and taskC must be executed by separate threads.
taskA has subtasks task(1), task(2), and task(3).
taskB has subtasks task(11), task(12), and task(13).
taskC has subtasks task(21), task(22), and task(23).
If any one of taskA, taskB, and taskC finishes and there is at least one unstarted subtask of another task, the thread associated with the finished task should steal the unstarted subtask.
I was not able to achieve this setting. All I was able to do the following MWE. In this MWE, threads do not obey the rules 2, 3, 4.
Here is my MWE:
double task(int taskid) {
int tid = omp_get_thread_num();
int nthreads = omp_get_num_threads();
printf("%d/%d: taskid=%d\n", tid, nthreads, taskid);
int i;
double t = 1.1;
for(i = 0; i < 10000000*taskid; i++) {
t *= t/i;
}
return t;
}
double taskA() {
int tid = omp_get_thread_num();
int nthreads = omp_get_num_threads();
printf("%s %d/%d\n", __FUNCTION__, tid, nthreads);
double a, b, c;
//#pragma omp parallel
//#pragma omp single
{
#pragma omp task untied shared(a)
a=task(1);
#pragma omp task untied shared(b)
b=task(2);
#pragma omp task untied shared(c)
c=task(3);
}
return a+b+c;
}
double taskB() {
int tid = omp_get_thread_num();
int nthreads = omp_get_num_threads();
printf("%s %d/%d\n", __FUNCTION__, tid, nthreads);
double a, b, c;
//#pragma omp parallel
//#pragma omp single
{
#pragma omp task untied shared(a)
a=task(11);
#pragma omp task untied shared(b)
b=task(12);
#pragma omp task untied shared(c)
c=task(13);
}
return a+b+c;
}
double taskC() {
int tid = omp_get_thread_num();
int nthreads = omp_get_num_threads();
printf("%s %d/%d\n", __FUNCTION__, tid, nthreads);
double a, b, c;
//#pragma omp parallel
//#pragma omp single
{
#pragma omp task untied shared(a)
a=task(21);
#pragma omp task untied shared(b)
b=task(22);
#pragma omp task untied shared(c)
c=task(23);
}
return a+b+c;
}
int main() {
omp_set_num_threads(3);
double a,b,c;
#pragma omp parallel
#pragma omp single
{
#pragma omp task untied
a=taskA();
#pragma omp task untied
b=taskB();
#pragma omp task untied
c=taskC();
}
#pragma omp taskwait
printf("%g %g %g\n", a, b, c);
return 0;
}
Compiled as:
icpc -Wall -fopenmp -O2 -o nestedomp nestedomp.c
Output:
taskC 1/3
1/3: taskid=21
taskA 2/3
taskB 0/3
0/3: taskid=23
2/3: taskid=22
1/3: taskid=1
1/3: taskid=2
2/3: taskid=3
0/3: taskid=11
1/3: taskid=12
2/3: taskid=13
Here, thread 0 starts processing task 23, however it must start processing 1 or 11.
You could use thread id to structure work distribution:
#pragma omp parallel num_threads(3)
{
int tid = omp_get_thread_num();
if (tid == 0)
// Task 0
} else if (tid == 1) {
// Task 1
} else
// Task 2
}
You can set the number of threads according to your needs and introduce nesting at the task level.
I have an OpenMP parallelized program that looks like that:
[...]
#pragma omp parallel
{
//initialize threads
#pragma omp for
for(...)
{
//Work is done here
}
}
Now I'm adding MPI support. What I will need is a thread that handles the communication, in my case, calls GatherAll all the time and fills/empties a linked list for receiving/sending data from the other processes. That thread should send/receive until a flag is set. So right now there is no MPI stuff in the example, my question is about the implementation of that routine in OpenMP.
How do I implement such a thread? For example, I tried to introduce a single directive here:
[...]
int kill=0
#pragma omp parallel shared(kill)
{
//initialize threads
#pragma omp single nowait
{
while(!kill)
send_receive();
}
#pragma omp for
for(...)
{
//Work is done here
}
kill=1
}
but in this case the program gets stuck because the implicit barrier after the for-loop waits for the thread in the while-loop above.
Thank you, rugermini.
You could try adding a nowait clause to your single construct:
EDIT: responding to the first comment
If you enable nested parallelism for OpenMP, you might be able to achieve what you want by making two levels of parallelism. In the top level, you have two concurrent parallel sections, one for the MPI communications, the other for local computation. This last section can itself be parallelized, which gives you a second level of parallelisation. Only threads executing this level will be affected by barriers in it.
#include <iostream>
#include <omp.h>
int main()
{
int kill = 0;
#pragma omp parallel sections
{
#pragma omp section
{
while (kill == 0){
/* manage MPI communications */
}
}
#pragma omp section
{
#pragma omp parallel
#pragma omp for
for (int i = 0; i < 10000 ; ++i) {
/* your workload */
}
kill = 1;
}
}
}
However, you must be aware that your code is going to break if you don't have at least two threads, which means you're breaking the assumption that the sequential and parallelized versions of the code should do the same thing.
It would be much cleaner to wrap your OpenMP kernel inside a more global MPI communication scheme (potentially using asynchronous communications to overlap communications with computations).
You have to be careful, because you can't just have your MPI calling thread "skip" the omp for loop; all threads in the thread team have to go through the for loop.
There's a couple ways you could do this: with nested parallism and tasks, you could launch one task to do the message passing and anther to call a work routine which has an omp parallel for in it:
#include <mpi.h>
#include <omp.h>
#include <stdio.h>
void work(int rank) {
const int n=14;
#pragma omp parallel for
for (int i=0; i<n; i++) {
int tid = omp_get_thread_num();
printf("%d:%d working on item %d\n", rank, tid, i);
}
}
void sendrecv(int rank, int sneighbour, int rneighbour, int *data) {
const int tag=1;
MPI_Sendrecv(&rank, 1, MPI_INT, sneighbour, tag,
data, 1, MPI_INT, rneighbour, tag,
MPI_COMM_WORLD, MPI_STATUS_IGNORE);
}
int main(int argc, char **argv) {
int rank, size;
int sneighbour;
int rneighbour;
int data;
int got;
MPI_Init_thread(&argc, &argv, MPI_THREAD_FUNNELED, &got);
MPI_Comm_size(MPI_COMM_WORLD,&size);
MPI_Comm_rank(MPI_COMM_WORLD,&rank);
omp_set_nested(1);
sneighbour = rank + 1;
if (sneighbour >= size) sneighbour = 0;
rneighbour = rank - 1;
if (rneighbour <0 ) rneighbour = size-1;
#pragma omp parallel
{
#pragma omp single
{
#pragma omp task
{
sendrecv(rank, sneighbour, rneighbour, &data);
printf("Got data from %d\n", data);
}
#pragma omp task
work(rank);
}
}
MPI_Finalize();
return 0;
}
Alternately, you could make your omp for loop schedule(dynamic) so that the other threads can pick up some of the slack from while the master thread is sending, and the master thread can pick up some work when it's done:
#include <mpi.h>
#include <omp.h>
#include <stdio.h>
void sendrecv(int rank, int sneighbour, int rneighbour, int *data) {
const int tag=1;
MPI_Sendrecv(&rank, 1, MPI_INT, sneighbour, tag,
data, 1, MPI_INT, rneighbour, tag,
MPI_COMM_WORLD, MPI_STATUS_IGNORE);
}
int main(int argc, char **argv) {
int rank, size;
int sneighbour;
int rneighbour;
int data;
int got;
const int n=14;
MPI_Init_thread(&argc, &argv, MPI_THREAD_FUNNELED, &got);
MPI_Comm_size(MPI_COMM_WORLD,&size);
MPI_Comm_rank(MPI_COMM_WORLD,&rank);
omp_set_nested(1);
sneighbour = rank + 1;
if (sneighbour >= size) sneighbour = 0;
rneighbour = rank - 1;
if (rneighbour <0 ) rneighbour = size-1;
#pragma omp parallel
{
#pragma omp master
{
sendrecv(rank, sneighbour, rneighbour, &data);
printf("Got data from %d\n", data);
}
#pragma omp for schedule(dynamic)
for (int i=0; i<n; i++) {
int tid = omp_get_thread_num();
printf("%d:%d working on item %d\n", rank, tid, i);
}
}
MPI_Finalize();
return 0;
}
Hmmm. If you are indeed adding MPI 'support' to your program, then you ought to be using mpi_allgather as mpi_gatherall does not exist. Note that mpi_allgather is a collective operation, that is all processes in the communicator call it. You can't have a process gathering data while the other processes do whatever it is they do. What you could do is use MPI single-sided communications to implement your idea; this will be a little tricky but no more than that if one process only reads the memory of other processes.
I'm puzzled by your use of the term 'thread' wrt MPI. I fear that you are confusing OpenMP and MPI, one of whose variants is called OpenMPI. Despite this name it is as different from OpenMP as chalk from cheese. MPI programs are written in terms of processes, not threads. The typical OpenMP implementation does indeed use threads, though the details are generally well-hidden from the programmer.
I'm seriously impressed that you are trying, or seem to be trying, to use MPI 'inside' your OpenMP code. This is exactly the opposite of work I do, and see others do on some seriously large computers. The standard mode for such 'hybrid' parallelisation is to write MPI programs which call OpenMP code. Many of today's very large computers comprise collections of what are, in effect, multicore boxes. A typical approach to programming one of these is to have one MPI process running on each box, and for each of those processes to use one OpenMP thread for each core in the box.