openmp

问题 I have some serial codes like this: do i=1,N ... end do do j=1,M ... end do ...(1) ...(2) Above showed three blocks of serial codes with two do-s and two independent blocks. and I want to adapt it into parallel codes. One way I know of doing is: !$omp parallel do do i ... !$omp end parallel !$omp parallel do do j ... !$omp end parallel !$omp parallel !$omp section ...(1) !$omp section ...(2) !$omp end parallel Notice that in doing this way, I am threading four times. As a non-expert, I am not

问题 I'm trying to figure out how I can use OpenMP's for reduction() equivalent in CUDA. I've done some research online, and none of what I've tried worked. The code: #pragma omp parallel for reduction(+:sum) for (i = 0; i < N; i++) { float f = ... //store return from function to f out[i] = f; //store f to out[i] sum += f; //add f to sum and store in sum } I know what for reduction() does in OpenMP....it makes the last line of the for loop possible. But how can I use CUDA to express the same thing

问题 I have the following recursive program which I would like to parallelize using OpenMP: #include <iostream> #include <cmath> #include <numeric> #include <vector> #include <algorithm> #include <thread> #include <omp.h> // Determines if a point of dimension point.size() is within the sphere bool isPointWithinSphere(std::vector<int> point, const double &radius) { // Since we know that the sphere is centered at the origin, we can simply // find the euclidean distance (square root of the sum of

问题 I have the following recursive program which I would like to parallelize using OpenMP: #include <iostream> #include <cmath> #include <numeric> #include <vector> #include <algorithm> #include <thread> #include <omp.h> // Determines if a point of dimension point.size() is within the sphere bool isPointWithinSphere(std::vector<int> point, const double &radius) { // Since we know that the sphere is centered at the origin, we can simply // find the euclidean distance (square root of the sum of

问题 I'm currently experiencing an issue in wrapping some Fortran subroutines for use in a python3 script. This issue has only come up since I have attempted to use OpenMP in the subroutines. For example, if I compile a module 'test.pyd' using f2py -c -m --fcompiler=gfortran --compiler=mingw32 --f90flags='-fopenmp' test test.f90 -lgomp , in which 'test.f90' is a Fortran subroutine which contains a parallelized loop, upon attempting to import this module into my script, I encounter ImportError: DLL

问题 I have few functions that should be applied to matrix of some structures serially. For single thread I use the following code: for(int t = 0; t < maxT; ++t) { for(int i = 0; i < maxI; ++i) for(int j = 0; j < maxJ; ++j) function1(i, j); for(int i = 0; i < maxI; ++i) for(int j = 0; j < maxJ; ++j) function2(i, j); } Now I'm trying to parallelize that code: #pragma omp parallel { for(int t = 0; t < maxT; ++t) { #pragma omp single function3(); // call this function once (once for each iteration of

问题 I have a for-loop to iterate over a rather large amount of points (ca. 20000), for every point it is checked whether or not the point is inside some cylinder (that cylinder is the same for every point). Furthermore, I would like to have the highest Y coordinate from the set of points. Since I have to do this calculation a lot, and it's quite slow, I want to use OpenMP to parallelize the loop. Currently I have (somewhat reduced): #pragma omp parallel for default(shared) private

问题 Given a C string (array of characters terminating with a NULL character constant), we have to find the length of the string. Could you please suggest some ways to parallelize this for N number of threads of execution. I am having problem dividing into sub-problems as accessing a location of the array which is not present will give segmentation fault. EDIT : I am not concerned that doing this task in parallel may have much greater overhead or not. Just want to know if this can be done (using

问题 I have the following code: #pragma omp parallel { #pragma omp single { for(node* p = head; p; p = p->next) { preprocess(p); #pragma omp task process(p); } } } I would like to know when do the threads start computing the tasks. As soon as the task is created with #pragma omp task or only after all tasks are created? Edit: int* array = (int*)malloc... #pragma omp parallel { #pragma omp single { while(...){ preprocess(array); #pragma omp task firstprivate(array) process(array); } } } 回答1: In

问题 According to this question, the use of threadprivate with openmp is problematic. Here is a minimum (non-)working example of the problem: #include"omp.h" #include<iostream> extern const int a; #pragma omp threadprivate(a) const int a=2; void my_call(){ std::cout<<a<<std::endl; }; int main(){ #pragma omp parallel for for(unsigned int i=0;i<8;i++){ my_call(); } } This codes compiles with intel 15.0.2.164 but not with gcc 4.9.2-10. gcc says: g++ -std=c++11 -O3 -fopenmp -O3 -fopenmp test.cpp -o