openmp

问题 Suppose I need to read many distinct, independent chunks of data from the same file saved on disk. Is it possible to multi-thread this upload? Related: Do all threads on the same processor use the same IO device to read from disk? In this case, multi-threading would not speed up the upload at all - the threads would just be waiting in line. (I am currently multi-threading with OpenMP.) 回答1: Yes, it is possible. However: Do all threads on the same processor use the same IO device to read from

问题 My openMP version did not give any speed boost. I have a dual core machine and the CPU usage is always 50%. So I tried the sample program given in Wiki. Looks like the openMP compiler (Visual Studio 2008) is not creating more than one thread. This is the program: #include <omp.h> #include <stdio.h> #include <stdlib.h> int main (int argc, char *argv[]) { int th_id, nthreads; #pragma omp parallel private(th_id) { th_id = omp_get_thread_num(); printf("Hello World from thread %d\n", th_id);

问题 My openMP version did not give any speed boost. I have a dual core machine and the CPU usage is always 50%. So I tried the sample program given in Wiki. Looks like the openMP compiler (Visual Studio 2008) is not creating more than one thread. This is the program: #include <omp.h> #include <stdio.h> #include <stdlib.h> int main (int argc, char *argv[]) { int th_id, nthreads; #pragma omp parallel private(th_id) { th_id = omp_get_thread_num(); printf("Hello World from thread %d\n", th_id);

问题 My openMP version did not give any speed boost. I have a dual core machine and the CPU usage is always 50%. So I tried the sample program given in Wiki. Looks like the openMP compiler (Visual Studio 2008) is not creating more than one thread. This is the program: #include <omp.h> #include <stdio.h> #include <stdlib.h> int main (int argc, char *argv[]) { int th_id, nthreads; #pragma omp parallel private(th_id) { th_id = omp_get_thread_num(); printf("Hello World from thread %d\n", th_id);

问题 My openMP version did not give any speed boost. I have a dual core machine and the CPU usage is always 50%. So I tried the sample program given in Wiki. Looks like the openMP compiler (Visual Studio 2008) is not creating more than one thread. This is the program: #include <omp.h> #include <stdio.h> #include <stdlib.h> int main (int argc, char *argv[]) { int th_id, nthreads; #pragma omp parallel private(th_id) { th_id = omp_get_thread_num(); printf("Hello World from thread %d\n", th_id);

问题 I am using Codeblocks and have to run an OpenMP C program. So, I added the flag -fopenmp in Codeblocks (compiler settings) and am now getting the error of ' mingw32-g++.exe: error: libgomp.spec: No such file or directory ' So after a bit of searching on the internet about the error, I downloaded TDM-GCC ( installed in C:\TDM-GCC-64). But still the same error is being shown on Codeblocks. What am I doing wrong? Here is the build log: mingw32-gcc.exe -c "D:\Language Files\MatrixMultiplication.c

问题 In have this class: template <typename C, typename R, typename D> class myClass{ public: R Query(const C &query); ... private: struct Compare{ D val; size_t index; }; #pragma omp declare reduction(minimum : Compare : omp_out = omp_in.val < omp_out.val ? omp_in : omp_out) std::vector<C> values; ... } template <typename C, typename R, typename D> R Cache<C,R,D>::Query(const C &query){ Compare min; R result; if(!values.empty()){ #pragma omp parallel for reduction(minimum:min) for(size_t i=1; i

问题 I am relatively new to using OpenMP with Fortran 90. I know that local variables in called subroutines are automatically private when using a parallel do loop. Is the same true for functions that are called from the parallel do loop? Are there any differences between external functions and functions defined in the main program? I would assume that external functions behave the same as subroutines, but I am specifically curious about functions in main program. Thanks! 回答1: The local variables

问题 I am training my neural network using tensorflow on CentOS HPC. However I got this error at start of the training process: OMP: Error #15: Initializing libiomp5.so, but found libiomp5.so already initialized. OMP: Hint: This means that multiple copies of the OpenMP runtime have been linked into the program. That is dangerous, since it can degrade performance or cause incorrect results. The best thing to do is to ensure that only a single OpenMP runtime is linked into the process, e.g. by

问题 I am training my neural network using tensorflow on CentOS HPC. However I got this error at start of the training process: OMP: Error #15: Initializing libiomp5.so, but found libiomp5.so already initialized. OMP: Hint: This means that multiple copies of the OpenMP runtime have been linked into the program. That is dangerous, since it can degrade performance or cause incorrect results. The best thing to do is to ensure that only a single OpenMP runtime is linked into the process, e.g. by