openmp

问题 I've run into a funny problem using OpenMP v2 under MSVC 9 SP1. when calling omp_set_num_threads from the main thread of execution then using omp_get_num_threads to check the amount set, all works well and checks out. However, in an GUI app, I call the same thing, but its own thread(created with CreateThread ), to prevent the UI from becoming unresponsive, how ever it seems that omp_set_num_threads doesn't work when called from a thread, as omp_get_num_threads always reports 1, and from tests

问题 This is related to a previous question. I am trying to parallelize a code with mixed syntax (f77 and f90) I've added into one of the many routines this portion in the code !$omp parallel shared (xdif,cdiff, dg,bfvisc,r,ro,xm ) private ( l ) !$omp do DO L=2,n-1 xdif(l)=cdiff(l)*fjc+(dg(l)+bfvisc(l))* & (4.d0*pi*r(l)**2*ro(l)/xm(n))**2 ENDDO !$omp end do !$omp end parallel After compiling (using -fopenmp ) the code runs but just for a few seconds and the only error I'm getting is: Segmentation

问题 /var/spool/torque/mom_priv/jobs/775.head.cluster.SC: line 22: 28084 Segmentation fault ./a.out I am new in Fortran and this is the first time I work with HPC and OpenMP. In my code, I have a loop that should be parallel. I use some dynamic variables that all of them are dummy in the parallel loop. I allocate the dynamic variables in parallel loop !$OMP PARALLEL DO do 250 iconf = 1,config allocate(randx(num),randy(num),randz(num),unit_cg(num), & & x(nfftdim1),y(nfftdim2),z(nfftdim3),fr1(num),

问题 I am new in using OpenMP. I think that use max reduction clause to find the max element of an array is not such a bad idea, but in fact the parallel for loop ran much slower than serial one. int main() { double sta, end, elapse_t; int bsize = 46000; int q = bsize; int max_val = 0; double *buffer; buffer = (double*)malloc(bsize*sizeof(double)); srand(time(NULL)); for(int i=0;i<q;i++) buffer[i] = rand()%10000; sta = omp_get_wtime(); //int i; #pragma omp parallel for reduction(max : max_val) for

问题 I inherited a piece of Fortran code as am tasked with parallelizing it for the 8-core machine we have. I have two version of the code, and I am trying to use openMP compiler directives to speed it up. It works on one piece of code, but not the other, and I cannot figure out why--They're almost identical! I ran each piece of code with and without the openMP tags, and the first one showed speed improvements, but not the second one. I hope I am explaining this clearly... Code sample 1:

问题 I have the original code: min = INT_MAX; for (i=0;i<N;i++) if (A[i]<min) min = A[i]; for (i=0;i<N;i++) A[i]=A[i]-min; I want to get the parallel version of this and I did this: min = INT_MAX; #pragma omp parallel private(i){ minl = INT_MAX; #pragma omp for for (i=0;i<N;i++) if (A[i]<minl) minl=A[i]; #pragma omp critical{ if (minl<min) min=minl; } #pragma omp for for (i=0;i<N;i++) A[i]=A[i]-min; } Is the parallel code right? I was wondering if it is necessary to write #pragma omp barrier

问题 This is my first post on here, so go easy on me! I have a very strange problem. I've written a c code that converts particle data to grid data (the data comes from a cosmological simulation). In order to do this conversion, I am using the gsl Monte Carlo vegas integrator. When I run it in serial, it runs just fine and gives me the correct answer (albeit slowly). As an attempt at speeding it up, I tried openmp. The problem is that when I run it in parallel, the integration times out (I set a

问题 Disclamer: as you will see (in my opinion) this question is not related to this or this. I have this code: std::vector<Wrapper> localWrappers; std::vector<float> pixelDistancesNew; std::vector<float> curSigmas; //fill the 3 vectors #pragma omp parallel for collapse(2) schedule(dynamic, 1) for(int i=0; i<localWrappers.size(); i++) for (int r = par.border; r < (localWrappers[i].cur.rows - par.border); r++) for (int c = par.border; c < (localWrappers[i].cur.cols - par.border); c++) { const float

问题 What is the best way to utilize OpenMP with a matrix-vector product? Would the for directive suffice (if so, where should I place it? I assume outer loop would be more efficient) or would I need schedule, etc..? Also, how would I take advantage different algorithms to attempt this m-v product most efficiently? Thanks 回答1: The first step you should take is the obvious one, wrap the outermost loop in a parallel for directive. As you assume. It's always worth experimenting a bit to get some

问题 I have an array that needs to contain sum of different things and therefore I want to perform reduction on each of its elements. Here's the code: cdef int *a=<int *>malloc(sizeof(int) * 3) for i in range(3): a[i]=1*i cdef int *b for i in prange(1000,nogil=True,num_threads=10): b=res() #res returns an array initialized to 1s with gil: #if commented this line gives erroneous results for k in range(3): a[k]+=b[k] for i in range(3): print a[i] Till there is with gil the code runs fine else gives