openmp

问题 I am using python3.6 (python3.8 was tried earlier for the same problem) on windows 7. I have installed joblib==0.14.0, numpy==1.17.4, scikit-learn==0.22 and scipy==1.3.3 for some Machine Learning project. The error message i get when i try to import sklearn is: from ._openmp_helpers import _openmp_parallelism_enabled ImportError: DLL load failed: The specified module could not be found. Kindly advise how to resolve this problem? Thank you. 回答1: So it could be linked with missing OpenMP and we

问题 I'm trying to to compute pressure tensor of a crystal structure. To do so, I have to go throught all pair of particle like in the simplify code below do i=1, atom_number ! sum over atoms i type1 = ATOMS(i) do nj=POINT(i), POINT(i+1)-1 ! sum over atoms j of i's atoms list j = LIST(nj) type2 = ATOMS(j) call get_ff_param(type1,type2,Aab,Bab,Cab,Dab) call distance_sqr2(i,j,r,VECT_R) call gettensor_rij(i,j,T) r = sqrt(r) if (r<coub_cutoff) then local_sum_real(id+1) = local_sum_real(id+1) + ( erfc

问题 There is a bug in intel compiler on user-defined reduction in OpenMP which was discussed here (including the wrokaround). Now I want to pass the vector to a function and do the same thing but I get this error: terminate called after throwing an instance of 'std::bad_alloc' what(): std::bad_alloc Aborted This is the example: #include <iostream> #include <vector> #include <algorithm> #include "omp.h" #pragma omp declare reduction(vec_double_plus : std::vector<double> : \ std::transform(omp_out

问题 I am working with Monte Carlo simulations to find the decimal places of PI. So far so good but OpenMP came in and I realize that ran2, arguably the best RGN, is not threadsafe! The implementation is here. Since I have not worked with OpenMP and neither a lot on multi-threading I am stuck at making this thread safe using OpenMP. So far what I know is that a function is already thread-safe if it doesn't modify non-local memory and it doesn't call any function that does. In this case, there are

问题 I'm working on an application that uses both coarse and fine grained multi-threading. That is, we manage scheduling of large work units on a pool of threads manually, and then within those work units certain functions utilize OpenMP for finer grain multithreading. We have realized gains by selectively using OpenMP in our costliest loops, but are concerned about creating contention for the OpenMP worker pool as we add OpenMP blocks to cheaper loops. Is there a way to signal to OpenMP that a

问题 I've tried using OpenMP with a single #pragma omp parallel for , and it resulted in my programme going from a runtime of 35s (99.6% CPU) to 14s (500% CPU) , running on Intel(R) Xeon(R) CPU E3-1240 v3 @ 3.40GHz. That's the difference between compiling with g++ -O3 and g++ -O3 -fopenmp , both with gcc (Debian 4.7.2-5) 4.7.2 on Debian 7 (wheezy). Why is it only using 500% CPU at most, when the theoretical maximum would be 800%, since the CPU is 4 core / 8 threads? Shouldn't it be reaching at

问题 I wrote the Matrix-Vector product program using OpenMP and AVX2. However, I got the wrong answer because of OpenMP. The true answer is all of the value of array c would become 100. My answer was mix of 98, 99, and 100. The actual code is below. I compiled Clang with -fopenmp, -mavx, -mfma. #include "stdio.h" #include "math.h" #include "stdlib.h" #include "omp.h" #include "x86intrin.h" void mv(double *a,double *b,double *c, int m, int n, int l) { int k; #pragma omp parallel { __m256d va,vb,vc;

问题 I am currently working on a system in which I reading in a file of over ~200 million records (lines), so I am buffering the records and using OpenMP tasks to manage each batch while continuing to process input. Each record in the buffer takes roughly 60μ to process in work_on_data , and will generate a string result. To avoid critical regions, I create a vector for results, and pass record placeholders (that I insert into this vector) by address to the work_on_data function : int i = 0;

问题 I'm trying to compile a Fortran code that someone has sent me. It compiles fine on my Linux box, now I'm trying to compile it under MinGW on Windows. But when I run the gfortran command to compile and link it, it fails with the following error: undumag_main_omp.f:8175:9: use omp_lib 1 Fatal Error: Can't open module file 'omp_lib.mod' for reading at (1): No such file or directory compilation terminated. I'm using the -fopenmp switch to use OpenMP. I've installed MinGW (5.3.0) using the

问题 I am having issues with what I think is some false caching, I am only getting a small speedup when using the following code compared to not the unparalleled version. matrix1 and matrix2 are sparse matrices in a struct with (row, col, val) format. void pMultiply(struct SparseRow *matrix1, struct SparseRow *matrix2, int m1Rows, int m2Rows, struct SparseRow **result) { *result = malloc(1 * sizeof(struct SparseRow)); int resultNonZeroEntries = 0; #pragma omp parallel for atomic for(int i = 0; i <