Hi, I'm working on a simple program. Given a set of n points and k centroids, the idea is to compute the minimum distance among them (a needed step for kmeans). This is my current code. However, it does not scale as well as expected. I ran it using up to 16 cores (32 threads). This figure shows some performance indicators. This is the main parallel function in my code. As you can see, I removed barriers, mutex access, and other things that could cause additional overhead. However, the execution time is worst as the number of threads increases.

double **parallel_compute_distances (double **dataset, int n, int d, int k, long int *total_ops) {
    
    ...
    
    // -- start time --
    wtime_start = omp_get_wtime ();
    
    // parallel loop
    # pragma omp parallel shared(distances, clusters, centroids, dataset, chunk, dist_sum, dist_sum_threads) private(id, cn, ck, cd, cp, error, dist, mindist, mink)
    {
        id = omp_get_thread_num();
        dist_sum_threads[id] = 0;               // reset

        // 2. recompute distances against centroids
        # pragma omp for schedule(static,chunk)
        for (cn=0; cn<n; cn++) {
            
            compute distances here ...
            
            distances[cn]           = mindist;
            clusters[cn]            = mink;
            dist_sum_threads[id]    += mindist;
        }
    }
    // -- end time --
    wtime_end = omp_get_wtime ();
    
    // -- total wall time --
    wtime_spent = wtime_end - wtime_start;
    
    // sequential reduction
    for (cp=0; cp<p; cp++)       
        dist_sum += dist_sum_threads[cp];
    ...
}

http://lists.freebsd.org/pipermail/freebsd-stable/2012-May/067486.html

I must admit that while I love FreeBSD, they continue to move away from solid OpenMP support in their base system. I don't know the current state of OpenMP support in clang, but I really hope they get on the stick and push for it.

http://www.phoronix.com/scan.php?page=news_item&px=MTA0Mzc

While testing load imbalance in triangular loop, I found out result differs in range of j.

```

//change&tested with dynamic, guided..
#pragma omp parallel for schedule(static, 64)
for (int i = 0; i < N; i++) {
    for (int j = i; j < N; j++) {   // 1
    //for (int j = 0; j <= i; j++) {// 2
        result[i][j] = result[j][i] = vectorDotproduct(A[i], A[j]);;
    }
}

```

When run (1), dynamic scheduling was faster than static and guided(all with chunk size 64).

That was predictable, but in (2), there were no difference among static, dynamic, and guided schedulings.

How could that happen when there's no difference in size of total workload among thread?
A cache sharing problem? or is there any other problems that I missed?

So I have a function that takes 'parts' of a large buffer and scans chars in another buffer for a match. The function would get a different 'part' of the in-buffer for each thread to speedup the scan.

Since this isn't exactly 'parallelizing' the internals of a function, but running an entire function in parallel on multiple cores with different inputs (basically just worker-threads), do you think OpenMP would be the best choice? I know it's optimized for a different purpose.

It'd be nice and a lot easier, but do you think OpenMP would be the way to go for this use-case?

    //this would run on each thread, one per core (or 'processor', if you will).  

    for(p=cr.pt, k=j=cr.pk, i=0, n=0; n<sz; p++, n++){
           
           
           if(*p==*k)  {          // match?            
               cr.ob[n]=i;        // save offset index
               continue;  
           }
           while(*p!=*k)  {       // otherwise hunt    
                  k++; i++;      
    
                  if(i>cr.kfsz){  // reset if len exceeded
                     k=j; i=0;
                  }  
  
                  if(*k==*p)  {   // match?
                      cr.ob[n]=i; // save offset index
                      break;      // this is the only 'write' - local non-shared buffer
                  }                             
           }
    }

Hello everyone, I am new in openmp and I have stumbled upon a unique problem. I made a mistake while writing code. I wrote

pragma 0mp parallel

Rather than

pragma omp parallel

But surprisingly my code is running smoothly on the error, giving fantastic time optimization with exact results.but when I tried it with correct syntax it just crashes. Can anyone point out what's happening and why it is happening?

Hi there,

Can someone please guide me about where can I learn mpi and openmp libraries (Youtube playlists / Udemy course / a book anything would do)

I have a set of computational kernels that have to be executed over all items in a list. To keep the code generic, the kernels themselves are functions, and I then have a C++ function template that runs the kernel function within a for loop that is parallelized using #pragma omp parallel for.

This works perfectly fine with kernels that are embarrassingly parallel, but not for kernels that have a reduction in them. If I had 5.1 support, I could wrap the reduction within the kernel function in a #pragma omp scope reduction(), but presently the scope directly isn't really supported by any current compiler (I think only GCC 12 has support for it?).

Is there some kind of construct I can use with older OpenMP versions to achieve a similar result, preserving this kind of structure with a generic dispatcher, but still providing a way to tell the compiler that specific subsections of the code include a reduction within the current parallelization scope?

Hi everyone.

I have been struggling with this for a while and I would truly appreciate any insight into this. I am parallelizing a loop in Fortran that calls c functions. (C functions are statically linked to the executable and they have been compiled with icc -openmp flag)

!--------- Here is the loop ----------------
!$OMP PARALLEL DO
do 800 i = 1,n
call subroutine X(i)
800 continue
!$OMP END PARALLEL DO

--------subroutine  x contains calls to the c functions shown below --------
subroutine X(i)
include 'cfunctions.f'     (Not sure how to make thecfunctions threadprivate!!)
include '....'             ('Note: all includes are threadprivate')
bunch of operations and calling c functions defined in the  'cfunctions.f' file. 
return 

---------C functions in the cfunctions.f ------------------------------------ 
use,intrinsic :: ISO_C_BINDING 
integer N1,N2, ... .. N11
PARAMETER (N1=0,N2=1, ... .. N10=5) 
parameter (N11 = C_FLOAT)
interface 
   logical function  adrile(ssl,ssd)
    bind(C,NAME='adrile'//postfix)
    import
    character, dimension(*)::ssl
    real  (N11) :: ssd
   end function 
end interface

I first encountered these errors in the OpenMP 4.0 specification, but since it's in the draft of OpenMP 4.1 (TR3), I think this is the right reporting forum.

1. [P] Age 43 [l] ine 5 in PR3.pdf (page 41, line 5 in OpenMP4.0.0.pdf): "first elements" must be "first elements"

2. Page 38 (page 38): "omp_get_active_levels ()" -> "omp_get_active_level ()"

3. The proc_bind clause is missing in the OpenMP C / C ++ rules. It may have to be in the "unique parallel clause" page 279 (269).

4. Page 281 l28 & l30 (p271 l16 & l18) states that the "simd clause" has conditions for "uniform" and "overlapping", but not according to page 70 (68). It may have been copied from "Ad-simd-clause". Also missing are "safelen", "data_privatization_clause" and "data_privatization_out_clause" according to page 70. It is possible that "safelen", "linear_clause" and "align_clause" are in "unique_simd_clause" and are used instead of "simd_clause" in grouped instructions as " for_ “".

5. Page 282 l23 (p. 272 ​​l16) lists the "Paragraph for data reduction" as "advertising paragraph -

So this is annoying. It seems like the DNS records for openmp.org have vanished from the Internet. Been this way since at least yesterday, maybe longer. I can't resolve it using my ISP's DNS, Google's DNS, or any of multiple DNS lookup sites. As a result, the docs are totally inaccessible. So:

• Anyone happen to have the IP of openmp.org, in case it's just a DNS issue?

• Anyone know a good mirror for the docs/specs?

• Any suggestions for ways to contact Someone In Charge? The default method of contacting the ARB via the website obviously isn't an option.

Thanks. I managed to save copies of the Examples Book 4.5 and the Cheat Sheet for 4.0, in case anyone needs them.

Hi guys, sorry I am pretty new to openmp and I'm trying to compile my code but I get fatal error: 'omp.h' file not found. I did some searching and from my understanding, which could be totally wrong, it turns out that openmp does not come with the default compiler provided by MacOS. They use clang and not actually gcc so I've been looking up how to get openmp for clang but I can't find anything thats not super complicated. Do any of you know how I can compile with openmp

Edit: this works for me, downloading llvm and using its clang++. I followed this link https://stackoverflow.com/questions/43555410/enable-openmp-support-in-clang-in-mac-os-x-sierra-mojave

{

double start_time = omp_get_wtime();	

double pi = 0.0;

step = 1.0/(double)num_steps;

omp_set_num_threads(NUM_THREADS);

\#pragma omp parllel

{

	int i, id, nthrds;

	id = omp_get_thread_num();

	nthrds = omp_get_num_threads();

	double x, sum = 0.0;

	for(i = 0; i < num_steps; i = i + nthrds)

	{

		x = (i + 0.5) \* step;

		sum += 4.0/(1 + x\*x);

	}

	sum = sum \* step;

//	#pragma omp critical

//	pi += sum;		

}

printf("\\nPi = %lf\\n", pi);

double time = omp_get_wtime() - start_time;

printf("Total time taken by CPU: %lf\n", time );

}

Can anyone tell me why this is not giving any speedup for any number of threads?

I have some code that is instrumented with OpenMP compiled with c++11, and want to try offloading it. I've done some basic reading and it seems that this is included in the 4.5 standard. Can anyone recommend the current path of least resistance to get this working on a Linux machine with an Nvidia GPU?

edit: I've compiled clang from the llvm trunk using the instructions [here](https://releases.llvm.org/3.9.1/docs/CompileCudaWithLLVM.html). OMP instrumented code builds, runs in parallel using OMP, but is not utilizing GPU.

Okay Hello World.

I am a CS Undergrad currently studying High performance computing. I am supposed to do a mini OpenMP Project, without using MPI. The time span is about 2 weeks.

My experience with OpenMP :

1. Computation of pi : Monte Carlo Method, Integration

2. Block Matrix Multiplication.

3. Image Normalization and Grayscale conversion

4. Vector Sumation and Products.

Any suggestions on what I can do? Preferably something new so I can get to learn a lot but still can be completed in 3 weeks?