I am using openMP to parallelize for loop. However, the compiler fails to build with: "unrecognized OpenMP #pragma". The output shows it occurs on "for" keyword.
I have already enabled openMP support in language (visual studio). If I try compiling with any other pragmas e.g single critical it seems to work fine. But it can't recognize "for".
#pragma omp parallel for
for (int i = 0; i < method_cnt; i++)
{
//Perform calculation
}
Your syntax is wrong. for and parallel for must be followed by a for-loop and not by a block:
#pragma omp parallel for
{ <--- wrong
for (int i = 0; i < method_cnt; i++)
{
//Perform calculation
}
} <--- wrong
The correct syntax is:
#pragma omp parallel for
for (int i = 0; i < method_cnt; i++)
{
//Perform calculation
}
Related
OpenMP clauses and brackets could exists same time in code. Is there any regulation of coding style about nested OpenMp clauses?
e.g:
#pragma omp parallel
for (int i = 0; i < N; i++) {
code1();
# pragma omp for // Should this line be intended?
for (int j = 0; j < M; j++) {
code2();
# pragma omp critical {
code3(); // Should this block and brackets be intended?
}
}
code4();
}
From an OpenMP perspective there's no real guideline about how to indent the code.
The way I write the code would look like this:
#pragma omp parallel
for (int i = 0; i < N; i++) {
code1();
#pragma omp for // Should this line be intended?
for (int j = 0; j < M; j++) {
code2();
#pragma omp critical
{ // this curly brace needs to go on its own line
code3(); // Should this block and brackets be intended?
}
}
code4();
}
So, the pragmas start in the first column and the base language code follows whatever style you are using. The rational is that if you deleted all OpenMP pragmas you still get "pretty" base language code.
I seem to also recall that compiler pragmas have to have their '#' in the first column. I'll leave it other to correct my memory on this, as I'm not sure about if ISO C/C++ actually requires it. I haven't seen any compiler lately, that would enforce it.
I am currently attempting to parallelize a maximum value search using openMP 2.0 and Visual Studio 2012. I feel like this problem is so simple, it could be used as a textbook example. However, I run into a race condition I do not understand.
The code passage in question is:
double globalMaxVal = std::numeric_limits<double>::min();;
#pragma omp parallel for
for(int i = 0; i < numberOfLoops; i++)
{
{/* ... */} // In this section I determine maxVal
// Besides reading out values from two std::vector via the [] operator, I do not access or manipulate any global variables.
#pragma omp flush(globalMaxVal) // IF I COMMENT OUT THIS LINE I RUN INTO A RACE CONDITION
#pragma omp critical
if(maxVal > globalMaxVal)
{
globalMaxVal = maxVal;
}
}
I do not grasp why it is necessary to flush globalMaxVal. The openMP 2.0 documentation states: "A flush directive without a variable-list is implied for the following directives: [...] At entry to and exit from critical [...]" Yet, I get results diverging from the non-parallelized implementation, if I leave out the flush directive.
I realize that above's code might not be the prettiest or most efficient way to solve my problem, but at the moment I want to understand, why I am seeing this race condition.
Any help would be greatly appreciated!
EDIT:
Below I've now added a minimal, complete and verifiable example below requiring only openMP and the standard library. I've been able to reproduce the problem described above with this code.
For me some runs yield a globalMaxVal != 99, if I omit the flush directive. With the directive, it works just fine.
#include <algorithm>
#include <iostream>
#include <random>
#include <Windows.h>
#include <omp.h>
int main()
{
// Repeat parallelized code 20 times
for(int r = 0; r < 20; r++)
{
int globalMaxVal = 0;
#pragma omp parallel for
for(int i = 0; i < 100; i++)
{
int maxVal = i;
// Some dummy calculations to use computation time
std::random_device rd;
std::mt19937 generator(rd());
std::uniform_real_distribution<double> particleDistribution(-1.0, 1.0);
for(int j = 0; j < 1000000; j++)
particleDistribution(generator);
// The actual code bit again
#pragma omp flush(globalMaxVal) // IF I COMMENT OUT THIS LINE I RUN INTO A RACE CONDITION
#pragma omp critical
if(maxVal > globalMaxVal)
{
globalMaxVal = maxVal;
}
}
// Report outcome - expected to be 99
std::cout << "Run: " << r << ", globalMaxVal: " << globalMaxVal << std::endl;
}
system("pause");
return 0;
}
EDIT 2:
After further testing, we've found that compiling the code in Visual Studio without optimization (/Od) or in Linux gives correct results, whereas the bugs surface in Visual Studio 2012 (Microsoft C/C++ compiler version 17.00.61030) with activated optimization (/O2).
I took some of my old OpenMP exercises to practice a little bit, but I have difficulties to find the solution for on in particular.
The goal is to write the most simple OpenMP code that correspond to the dependency graph.
The graphs are visible here: http://imgur.com/a/8qkYb
First one is simple.
It correspond to the following code:
#pragma omp parallel
{
#pragma omp simple
{
#pragma omp task
{
A1();
A2();
}
#pragma omp task
{
B1();
B2();
}
#pragma omp task
{
C1();
C2();
}
}
}
Second one is still easy.
#pragma omp parallel
{
#pragma omp simple
{
#pragma omp task
{
A1();
}
#pragma omp task
{
B1();
}
#pragma omp task
{
C1();
}
#pragma omp barrier
A2();
B2();
C2();
}
}
And now comes the last one…
which is bugging me quite a bit because the number of dependencies is unequal across all function calls. I thought there was a to explicitly state which task you should be waiting for, but I can't find what I'm looking for in the OpenMP documentation.
If anyone have an explanation for this question, I will be very grateful because I've been thinking about it for more than a month now.
First of all there is no #pragma omp simple in the OpenMP 4.5 specification.
I assume you meant #pragma omp single.
If so pragma omp barrier is a bad idea inside a single region, since only one thread will execude the code and waits for all other threads, which do not execute the region.
Additionally in the second on A2,B2 and C2 are not executed in parallel as tasks anymore.
To your acutual question:
What you are looking for seems to be the depend clause for Task constructs at OpenMP Secification pg. 169.
There is a pretty good explaination of the depend clause and how it works by Massimiliano for this question.
The last example is not that complex once you understand what is going on there: each task Tn depends on the previous iteration T-1_n AND its neighbors (T-1_n-1 and T-1_n+1). This pattern is known as Jacobi stencil. It is very common in partial differential equation solvers.
As Henkersmann said, the easiest option is using OpenMP Task's depend clause:
int val_a[N], val_b[N];
#pragma omp parallel
#pragma omp single
{
int *a = val_a;
int *b = val_b;
for( int t = 0; t < T; ++t ) {
// Unroll the inner loop for the boundary cases
#pragma omp task depend(in:a[0], a[1]) depend(out:b[0])
stencil(b, a, i);
for( int i = 1; i < N-1; ++i ) {
#pragma omp task depend(in:a[i-1],a[i],a[i+1]) \
depend(out:b[i])
stencil(b, a, i);
}
#pragma omp task depend(in:a[N-2],a[N-1]) depend(out:b[N-1])
stencil(b, a, N-1);
// Swap the pointers for the next iteration
int *tmp = a;
a = b;
b = tmp;
}
#pragma omp taskwait
}
As you may see, OpenMP task dependences are point-to-point, that means you can not express them in terms of array regions.
Another option, a bit cleaner for this specific case, is to enforce the dependences indirectly, using a barrier:
int a[N], b[N];
#pragma omp parallel
for( int t = 0; t < T; ++t ) {
#pragma omp for
for( int i = 0; i < N-1; ++i ) {
stencil(b, a, i);
}
}
This second case performs a synchronization barrier every time the inner loop finishes. The synchronization granularity is coarser, in the sense that you have only 1 synchronization point for each outer loop iteration. However, if stencil function is long and unbalanced, it is probably worth using tasks.
I'd like to use openMP to apply multi-thread.
Here is simple code that I wrote.
vector<Vector3f> a;
int i, j;
for (i = 0; i<10; i++)
{
Vector3f b;
#pragma omp parallel for private(j)
for (j = 0; j < 3; j++)
{
b[j] = j;
}
a.push_back(b);
}
for (i = 0; i < 10; i++)
{
cout << a[i] << endl;
}
I want to change it to works lik :
parallel for1
{
for2
}
or
for1
{
parallel for2
}
Code works when #pragma line is deleted. but it does not work when I use it. What's the problem?
///////// Added
Actually I use OpenMP to more complicated example,
double for loop question.
here, also When I do not apply MP, it works well.
But When I apply it,
the error occurs at vector push_back line.
vector<Class> B;
for 1
{
#pragma omp parallel for private(j)
parallel for j
{
Class A;
B.push_back(A); // error!!!!!!!
}
}
If I erase B.push_back(A) line, it works as well when I applying MP.
I could not find exact error message, but it looks like exception error about vector I guess. Debug stops at
void _Reallocate(size_type _Count)
{ // move to array of exactly _Count elements
pointer _Ptr = this->_Getal().allocate(_Count);
_TRY_BEGIN
_Umove(this->_Myfirst, this->_Mylast, _Ptr);
std::vector::push_back is not thread safe, you cannot call that without any protection against race conditions from multiple threads.
Instead, prepare the vector such that it's size is already correct and then insert the elements via operator[].
Alternatively you can protect the insertion with a critical region:
#pragma omp critical
B.push_back(A);
This way only one thread at a time will do the insertion which will fix the error but slow down the code.
In general I think you don't approach parallelization the right way, but there is no way to give better advise without a clearer and more representative problem description.
I am new to OpenMP and I am stuck with a basic operation. Here is a sample code for my question.
#include <omp.h>
int main(void)
{
int A[16] = {1,2,3,4,5 ...... 16};
#pragma omp parallel for firstprivate(A)
for(int i = 0; i < 4; i++)
{
for(int j = 0; j < 4; j++)
{
A[i*4+j] = Process(A[i*4+j]);
}
}
}
As evident,value of A is local to each thread. However, at the end, I want to write back part of A calculated by each threadto the corresponding position in global variable A. How this can be accomplished?
Simply make A shared. This is fine, because all loop iterations operate on separate elements of A. Remember that OpenMP is shared memory programming.
You can do so explicitly by using shared instead of firstprivate, or simply remove the declaration:
int A[16] = {1,2,3,4,5 ...... 16};
#pragma omp parallel for
for(int i = 0; i < 4; i++)
By default all variables declared outside of the parallel region. You can find an extended exemplary description in this answer.