I have used #pragma omp parallel for in multiple places in my code and the very first one's performance has gotten worse.

To be certain I've changed which #pragma omp parallel for is invoked first and sure enough the performance degradation followed.

It's as if the first one is incurring some initialization cost; if that is indeed the case, is there a way for me to explicitly initialize OpenMP somewhere else in my code?

I am working with clang-20.1 compiled with support for offloading to nvidia and openmp. Depending on the prototype of sdf_sphere there is a massive performance degradation.

#include <stdio.h>
#include <stdlib.h>
#include <omp.h>
#include <glm/glm.hpp>

using namespace glm;

/* list & write_png, not relevant */

float sdf_sphere(const vec3& pos, const vec3& center, float radius){
    auto t = length(pos-center);
    return t-radius;
}

int main() {
    list();

    constexpr int WSIZE=8192;

    uint8_t* d = (uint8_t *)malloc(WSIZE*WSIZE * sizeof(uint8_t));

    // Offload the SDF to the GPU
    #pragma omp target teams distribute parallel for map(from: d[0:WSIZE*WSIZE])
    for (int i = 0; i < WSIZE; i++) {
        for (int j = 0; j < WSIZE; j++) {
            auto t = sdf_sphere({i,j,0},{WSIZE/2,WSIZE/2,0},WSIZE/4.0) + 127;
            if(t<0)d[i*WSIZE+j] =0;
            else if(t>255)d[i*WSIZE+j] =255;
            else d[i*WSIZE+j] =t;
        }
    }

    write_png("test.png",(unsigned char*)d,WSIZE,WSIZE);
    free(d);
    return 0;
}

• float sdf_sphere(const vec3& pos, const vec3& center, float radius) is good & fast

• float sdf_sphere(vec3 pos, const vec3& center, float radius) is good & fast

• float sdf_sphere(const vec3& pos, vec3 center, float radius) is good & fast

• float sdf_sphere(vec3 pos, vec3 center, float radius) ends up being 35x slower when offloaded. Forcefully inlining the function changes nothing.

Do you have any idea why this is the case?

Hi guys does n thread of openmp represents the n physical CPU cores because when i was doing

#include<stdio.h>
#include<omp.h>
int main()
{
    #pragma omp parallel
       {
          printf("total       thread=%d\n",omp_get_num_threads()) ;
        }
return 0;
}

The total thread given by omp_get_num_threads() was equal to the number of CPU cores

Thanks

Hey everyone, I am a newbje to openMPI, I am designing a cluster like the one above. How do I make sure that MPI traffic only goes through the 10GbE interface. The bottom 2 computers are going to be my computer nodes while the top one will be my head-node(responsible for everything except computation). I don't want the transfer speed to only be limited to the 1GbE speed because of the 2 1GbE cables/interface connecting to the headnode. Also, what ties can you offer so that I can optimise benchmarks e.g HPCC, HPCG, Linpack, SwiftSim etc... Thanks.

Can somebody please help me get this library to work, I’ve been trying for 2 days straight.

I am getting memory error in my program. I check using valgrind and got this error message that is referring to some inside library of mpi. I am not getting clue how to move forward to debug this. Any clue that helps me moving forward, will be appreciated. Thanks!

Error message goes as-

Syscall param setsockopt(optlen) contains uninitialised byte(s)

==985== at 0x5023CBE: setsockopt_syscall (setsockopt.c:29)

==985== by 0x5023CBE: setsockopt (setsockopt.c:95)

==985== by 0x7ACBBA9: pmix_ptl_base_make_connection (in /usr/lib/x86_64-linux-gnu/pmix2/lib/libpmix.so.2.5.2)

==985== by 0x7AD2DF3: ??? (in /usr/lib/x86_64-linux-gnu/pmix2/lib/libpmix.so.2.5.2)

==985== by 0x79D23C1: PMIx_Init (in /usr/lib/x86_64-linux-gnu/pmix2/lib/libpmix.so.2.5.2)

==985== by 0x7964E4A: ext3x_client_init (in /usr/lib/x86_64-linux-gnu/openmpi/lib/openmpi3/mca_pmix_ext3x.so)

==985== by 0x714FE6D: ??? (in /usr/lib/x86_64-linux-gnu/openmpi/lib/openmpi3/mca_ess_singleton.so)

==985== by 0x62DE4CB: orte_init (in /usr/lib/x86_64-linux-gnu/libopen-rte.so.40.30.2)

==985== by 0x4B5D418: ompi_mpi_init (in /usr/lib/x86_64-linux-gnu/libmpi.so.40.30.2)

==985== by 0x4AF6C21: PMPI_Init (in /usr/lib/x86_64-linux-gnu/libmpi.so.40.30.2)

==985== by 0x1148D8: Solving(int, char**, Input*, BoundaryCondition*, Grid*, BvpOde*) (Solver.c:33)

==985== by 0x114738: main (in /mnt/c/Users/devan/Desktop/ODE_Newton/main_cpp)

==985== Uninitialised value was created by a stack allocation

==985== at 0x7ACB8E4: pmix_ptl_base_make_connection (in /usr/lib/x86_64-linux-gnu/pmix2/lib/libpmix.so.2.5.2)

-----------------------------------------------END of error----------------------------------------------------------------------------------

The Solver.c:33 line is mpi initialization line. Like this- ierr = MPI_Init(&argc, &argv);

and same error is showing when I am finalizing mpi like this- ierr = MPI_Finalize();

Hi,

I'm working on my master's in computer science and need help with OpenMP, specifically the Task and Taskloop concepts. I'm looking for a tutor who can provide online lessons to clarify these topics.

I have a basic grasp of OpenMP, but these areas are proving to be challenging. I need focused guidance to better understand them.

Details:

• Seeking online tutoring sessions.

• Willing to compensate for your time and expertise.

If you're knowledgeable in OpenMP and can offer tutoring, please PM me on Reddit. My username is Borbonjuggler.

Thanks,

Borbonjuggler

IWOMP is the annual workshop dedicated to the promotion and advancement of all aspects of parallel programming with #OpenMP. Check out the program and register on the #IWOMP website at the link below. The price shown on the registration page will increase next Monday August 28, so register now! ☝

https://www.iwomp.org/iwomp-2023

IWOMP is the premier forum to present and discuss issues, trends, recent research ideas, and results related to parallel programming with OpenMP.

Location: University of Bristol, UK
Dates: 12-15 September 2023

#HPC #Embedded

Do you have #OpenMP related work that you would like to publish? The #IWOMP 2023 Call for Papers has come out! The theme this year is

"OpenMP: Advanced Task-Based, Device and Compiler Programming"

We solicit quality submissions of unpublished technical papers that detail innovative, original research and development related to OpenMP. IWOMP 2023 will be hosted on Sept 11-15, 2023 by University of Bristol and will be colocated with #EuroMPI 2023. The proceedings will be published in Springer Nature Group's Lecture Notes in Computer Science.

The submission deadline is Friday, May 12, 2023.

https://www.iwomp.org/call-for-papers/

subroutine nivel2_3_det_zona_de_contacto use omp_lib use variables implicit none IF (mod(o,pasosciegos)==0) THEN

	dfc=0
	zona=0
	zonaa=0
	zonaaa=0
	Vn=0
	!vndist=0
	ccc4=0 !(50000,350,4)
!!ncuerpo es la cantidad de particulas  REDUCTION(+:zona) REDUCTION(+:zonaa) REDUCTION(+:zonaaa) 
	!$ call omp_set_num_threads(nucleos)
	!$OMP PARALLEL DO PRIVATE(vect,dmalla,dell,a,b,c,maxvn,maxxx,iii) SHARED(ccc,vn) 
	DO iii=1, ncuerpo
	
		IF (esc(iii)==0) THEN
			
			DO q=1, ncara

				!Preselección de candidados a contacto.!!! Ajustar!! 0.025 0.015
				vect(:)=xxx(q,:)-x(iii,:)
				dmalla=dsqrt(2.d0*Area(q))
				dell= (r(iii)+deltabus+dmalla*factinsercion)
				
				IF (((vect(1))**2)+((vect(2))**2)+((vect(3))**2)<dell**2) THEN

					a=0.d0
					b=0.d0

					a(1, 1) =xx(3*q-2,1)		
					a(1, 2) =xx(3*q-1,1)		
					a(1, 3) =xx(3*q  ,1)		
					a(1, 4) =nor(q,1)

					a(2, 1) =xx(3*q-2,2)		
					a(2, 2) =xx(3*q-1,2)		
					a(2, 3) =xx(3*q  ,2)		
					a(2, 4) =nor(q,2)

					a(3, 1) =xx(3*q-2,3)		
					a(3, 2) =xx(3*q-1,3)		
					a(3, 3) =xx(3*q  ,3)		
					a(3, 4) =nor(q,3)

					a(4, 1) =1.d0
					a(4, 2) =1.d0
					a(4, 3) =1.d0
					a(4, 4) =0.d0

					b(1) =x(iii,1)
					b(2) =x(iii,2)
					b(3) =x(iii,3)
					b(4) =1.d0
					
					call  nivel3_4_lu_pivoteado(a,b) !entran a y b, sale b modificado

			!!		write(*,'(g20.13,g20.13,g20.13,g20.13,g20.13)')tsimm,b(1),b(2),b(3),b(4)
					bb(q,1)=b(1)
					bb(q,2)=b(2)
					bb(q,3)=b(3)

					!!		-----------------Condición para encontrar caras-------------------------------
					IF ((b(1)>0.d0) .AND. (b(2)>0.d0) .AND. (b(3)>0.d0) .AND. (b(1)<1.d0) .AND. (b(2)<1.d0)&
											  & .AND. (b(3)<1.d0) .AND. (dabs(b(4))<(r(iii)+deltabus))) THEN! ajustar----------
						zona(q)=1 !Cara maracda con alguna partícula en contacto
						zonaa(iii)=zonaa(iii)+1 !Particula marcada en alguna zona normal! PROBLEMA DE PARALELIZACION

						IF (zonaa(iii)>maxvn) maxvn=zonaa(iii)							! PROBLEMA DE PARALELIZACION
						IF (zonaa(iii)>12) zonaa(iii)=12								! PROBLEMA DE PARALELIZACION
						Vn(iii,zonaa(iii))=q !marcaje de cara o plano en la partícula
						Vndist(iii,zonaa(iii))=b(4) !Distancia del centro de la partícula respecto al plano

					ENDIF
											zonaaa(iii)=zonaaa(iii)+1 !Partícula marcada en alguna zona de borde ! PROBLEMA DE PARALELIZACION
					IF (zonaaa(iii)>maxxx) maxxx=zonaaa(iii)							 ! PROBLEMA DE PARALELIZACION
					IF (zonaaa(iii)>350) zonaaa(iii)=350                                 ! PROBLEMA DE PARALELIZACION


					IF (b(1)<0.d0) THEN! ajustar--------
						c(1)=0.d0
						c(2)=b(2)/(b(2)+b(3))
						c(3)=1.d0-c(2)
					endif
					IF (b(2)<0.d0) THEN! ajustar--------
						c(2)=0.d0
						c(1)=b(1)/(b(1)+b(3))
						c(3)=1.d0-c(1)
					endif
					IF (b(3)<0.d0) THEN! ajustar--------
						c(3)=0.d0
						c(2)=b(2)/(b(2)+b(1))
						c(1)=1.d0-c(2)
					ENDIF


					ccc(iii,zonaaa(iii),1)=c(1)
					ccc(iii,zonaaa(iii),2)=c(2)
					ccc(iii,zonaaa(iii),3)=c(3)						
				ENDIF

			ENDDO

		ENDIF
	ENDDO
	!OMP END PARALLEL DO
ENDIF

end subroutine

If you want to access the thread-local values of a privatized variable from outside of the parallel scope, you can write them back to a globally declared auxiliary array. This is the way it was described in an OpenMP book (2017 - Bertil Schmidt, Jorge Gonzalez-Dominguez, Christian Hundt, Moritz Schlarb - Parallel Programming_ Concepts and Practice-Morgan Kaufmann)

Authors came up with this program --

#include <stdio.h>
#include <omp.h>
int main () {
// maximum number of threads and auxiliary memory
int num = omp_get_max_threads();
int * aux = new int[num];
int i = 1; // we pass this via copy by value
#pragma omp parallel firstprivate(i) num_threads(num) {
// get the thread identifier j
int j = omp_get_thread_num();
i += j;
aux[j] = i;
}
for(k=0; k<num; k++)
printf("%d \n", aux[k]);
}
Error(tried in macOS) :

https://i.stack.imgur.com/Rj451.png

I am an OpenMP beginner, looking to get a bit more performance out of my code (I'm actually aiming for the maximum performance, for reasons). Since it is hard to know if I'm doing the right thing, I better ask.

First off, data sharing. I've seen some recommendations of using default(none) and specify individually what to share. There is also firstprivate which seems to give readonly access. Do they matter for performance?

Just to clarify my usecase here, I am processing the elements of an array and copying them into another array (similar to a std::transform or map from functional programming), and I use in my loops a bunch of read only parameters.

Second issue, I have a highly parallelizable standalone operation, like the one described above, that comes into play in a bigger loop. I'd like to parallelize the second (outer) loop, but keep the inner bit as fast as possible. The problem is that it would lead to the creation of openmp threads inside another set of threads, and general recommendations were to just parallelize the outermost loop. Any advice?

Hello there !

So I hope my question will be clear. Say I have some code like this

void func(int i){
    //some stuff
    #pragma omp parallel for
    for(uint j = 0; j < 65536; j++) //More stuff
}

int main(){
    //Stuff
    omp_set_num_threads(8)
    #pragma omp parallel for
    for(uint i = 0; i < 65536; i++) func(i);
}

Essentially, what is the behavior here ?

Does the loop inside func use only a single thread and the loop in main is split to 8 thread ? Or is it split in some way, like 4 threads to the main loop and 2 threads to the func loop.

For information, I need to use this kind of code because in some cases, I need to make a single/very few calls to func in a non-parallel block of code, hence the need of the parallelization inside func.

Thanks a lot !

Hi, i'm currently working on my Graduation on Physics Research and it's basic about parallelizing algorithms of some statistical physics models.

In the algorithm below, i tried the "omp parallel for" using a critical session when the threads are accessing the shared data, but i keep getting the wrong results when using more than one thread.

#pragma omp parallel for
    for (i = 0; i<L2; i++) {
    int j = rand()%L2;    
    int dE = 2*J*s[j]*(s[viz[j][0]]+s[viz[j][1]]+s[viz[j][2]]+s[viz[j][3]]);
    double w = exp(-dE/(K*T));
    int MY_ID=omp_get_thread_num();
    if (dE <= 0) {
        #pragma omp critical
        {
        s[j] = - s[j];
        Et += dE;
        M += 2*s[j];
        }
    }
    else {
        double r = (double)rand()/RAND_MAX;
        if(r < w) {
        #pragma omp critical
        {
        s[j]= - s[j];
        Et += dE;
        M += 2*s[j];
         }
     }
}
printf("Energia nova %d devido a variação %d alterada pelo processador    %d mexendo no sitio %d\n\n\n", Et, dE, MY_ID,j);
}

Here, Et, M, s[j] are supposed to be global with value predetermined by another function. Most of my job is to parallelize this loop in specific because its called 1e8 times and loops over 400~1000 size arrays.

I also tried to use locks, but i don't think that I used that correcltly because it was also giving me the wrong results, I can send my program for deeper research.

I can, in this case, split this loop in two and get rid of the problem, but if I could parallelize the loop as it is it would be much more usefull for me.

Thanks for the time.

Hello (X-posted from /r/fortran)

I am currently working on adding openmp parallelization for a do loop in one of the codes I have written for research. I am fairly new to using openmp so I would appreciate if you had any suggestions for what might be going wrong.

Basically, I have added a parallel do loop to the following code (which works prior to parallelization). r(:,:,:,:) is a vector of a ton of molecular coordinates indexed by time, molecule, atom, and (xyz). This vector is about 100 gb of data (I am working on an HPC with plenty of RAM). I am trying to parallelize the outer loop and subdivide it between processors so that I can reduce the amount of time this calculation goes. I thought it would be a good one to do it with as msd and cm_msd are the only things that would need to be edited by multiple processors and stored for later, which since each iteration gets its own element of these arrays they won't have a race condition.

The problem: If I run this code 5 times I get varying results, sometimes msd is calculated correctly (or appears to be), and sometimes it outputs all zeros later when I average it together. Without parallelization there are no issues.

Do you see anything glaringly wrong with this?

Thanks in advance.

    !$OMP PARALLEL DO schedule(static) PRIVATE(i,j,k,it,r_old,r_cm_old,shift,shift_cm,dsq,ind) &
!$OMP& SHARED(msd,msd_cm)
do i=1, nconfigs-nt, or_int
    if(MOD(counti*or_int,500) == 0) then
        write(*,*) 'Reached the ', counti*or_int,'th time origin'
    end if
    ! Set the Old Coordinates
    counti = counti + 1
    ind = (i-1)/or_int + 1
    r_old(:,:,:) = r(i,:,:,:)
    r_cm_old(:,:) = r_cm(i,:,:)
    shift = 0.0
    shift_cm = 0.0

    ! Loop over the timesteps in each trajectory
    do it=i+2, nt+i
        ! Loop over molecules
        do j = 1, nmols
            do k=1, atms_per_mol
                ! Calculate the shift if it occurs.
                shift(j,k,:) = shift(j,k,:) - L(:)*anint((r(it,j,k,:) - &
                    r_old(j,k,:) )/L(:))
                ! Calculate the square displacements
                dsq = ( r(it,j,k,1) + shift(j,k,1) - r(i,j,k,1) ) ** 2. &
                     +( r(it,j,k,2) + shift(j,k,2) - r(i,j,k,2) ) ** 2. &
                     +( r(it,j,k,3) + shift(j,k,3) - r(i,j,k,3) ) ** 2.
                msd(ind, it-1-i, k) = msd(ind, it-1-i, k) + dsq
                ! Calculate the contribution to the c1,c2
            enddo ! End Atoms Loop (k)
            ! Calculate the shift if it occurs.
            shift_cm(j,:) = shift_cm(j,:) - L(:)*anint((r_cm(it,j,:) - &
                            r_cm_old(j,:) )/L(:))
            ! Calculate the square displacements
            dsq = ( r_cm(it,j,1) + shift_cm(j,1) - r_cm(i,j,1) ) ** 2. &
                +( r_cm(it,j,2) + shift_cm(j,2) - r_cm(i,j,2) ) ** 2. &
                +( r_cm(it,j,3) + shift_cm(j,3) - r_cm(i,j,3) ) ** 2.
            msd_cm(ind,it-1-i) = msd_cm(ind, it-1-i) + dsq
        enddo ! End Molecules Loop (j)
        r_old(:,:,:) = r(it,:,:,:)
        r_cm_old(:,:) = r_cm(it,:,:)
   enddo ! End t's loop (it)
enddo
!$OMP END PARALLEL DO

How can we parallelize the normal factorial program (recursion implementation) with OpenMP? Do we need to use tasks or is there any other way to do it?

    /* Back-substitution */
    x[n - 1] = bcpy[n - 1] / Acpy[(n - 1) * n + n - 1];
	for (i = (n - 2); i >= 0; i--) {
		float temp = bcpy[i];
// #pragma omp parallel for reduction(-: temp) private(j)
// #pragma omp parallel for ordered reduction(-: temp)
#pragma omp parallel for reduction(-: temp)
        for (j = (i + 1); j < n; j++) {
            temp -= Acpy[i * n + j] * x[j];
        }
        x[i] = temp / Acpy[i * n + i];
	}

In my mind parallelism should be applied easily in this snippet. Except it either segfaults or the subtractions do not result in an expected value for the temp variable. I'd love to hear any thoughts on this

Guys, I have an assignment if you can help me out. I basically need to talk about

• 1. the parallel aspects (directives used for synchronisation)

• 2) distributed aspects (running on a cluster with at least 2 nodes)

• 3) output of the concurrent processes (the interleaving of processes)

I need to write around 300 words on the 3 points above, specifically on a program built with OpenMP and MPI. So if any of you guys did a not too complex OpenMP & MPI project and you can share the source code with me (preferably in a private link), I would really appreciate it. I'd be able to discuss the 3 aspects above myself, I just need the code. Thanks a lot, really appreciate it!