openmp

问题 The OpenMP specification document says that "When a thread encounters a task construct, it may choose to execute the task immediately or defer its execution until a later time". However, the specification does not say how this choice is made. How does the thread decide when to execute the task immediately and when to defer the execution? If this is implementation specific, how does compilers like gcc solve this? 回答1: There are two conditions coming from the standard: When an if clause is

问题 I have a code that has the following structure. #pragma omp parallel for for( i = 0; i < N; i++ ) { ..... index = get_index(...); array[index] = ...; ..... } Now the value of index is unique for each thread (it never gets overlapped for different threads), but of course OpenMP can't make a guess for this and I suppose is using synchronization objects to access array . How can I ask openmp not to use synchronization objects for array and rely on me that index value is unique for different

问题 I have a code that has the following structure. #pragma omp parallel for for( i = 0; i < N; i++ ) { ..... index = get_index(...); array[index] = ...; ..... } Now the value of index is unique for each thread (it never gets overlapped for different threads), but of course OpenMP can't make a guess for this and I suppose is using synchronization objects to access array . How can I ask openmp not to use synchronization objects for array and rely on me that index value is unique for different

问题 I am an undergraduate student working on my thesis about parallel programming. I am using OpenMP model. Now i want to use gem5 for measure performance. That's why i install gem5 Full System successfully by following link: http://cearial01.kaist.ac.kr/index.php/2016/08/26/gem5-documentation/ Now i want to compile & run a c program with OpenMP (matmul.c) using gem5. How can i compile & run this program? I mean inside which folder i stored this program file (matmul.c) for compile? How i create

问题 I've built OpenCV using Intel IPP, so I suppose that whenever possible it's used (e.g. matrix multiplication). I want to test the scalability of my parallel application by comparing it with a serial version. In order to do so, when it I do: omp_set_num_threads(1); cv::setNumThreads(1); However, by monitoring the CPU usage I see that multiple CPUs are still used. Why is that? And how can I force the program execution by using just one CPU? 回答1: Re-building OpenCV from source with following

问题 Here is my problem. I want to parallelize AES-128 encryption in C using Openmp. I am hardly getting any speedup with the following code using openmp. My machine is Quadcore intel i5 machine. Here is the code. Any suggestion how to parallelize this code further would be really really appreciated. Please take a look at the main function which is at the end of the code. AES code below consists of a few functions to achieve its functionality. Please suggest how to best extract parallelism from

问题 Here is my problem. I want to parallelize AES-128 encryption in C using Openmp. I am hardly getting any speedup with the following code using openmp. My machine is Quadcore intel i5 machine. Here is the code. Any suggestion how to parallelize this code further would be really really appreciated. Please take a look at the main function which is at the end of the code. AES code below consists of a few functions to achieve its functionality. Please suggest how to best extract parallelism from

问题 I try to compile #include <omp.h> using namespace std; vector< vector<int> > multiplyMatrixes(const vector< vector<int> > &a, const vector< vector<int> > &b, int aHeight, int aWidth, int bHeight, int bWidth) { vector < vector<int> > c(aHeight, vector<int>(bWidth, 0)); #pragma omp parallel for collapse(2) for(int row = 0; row < aHeight; row++) { for(int col = 0; col < bWidth; col++) { int value = 0; for(int i = 0; i < aWidth; i++) { value += a[row][i] * b[i][col]; } c[row][col] = value; cout<<

问题 I try to compile #include <omp.h> using namespace std; vector< vector<int> > multiplyMatrixes(const vector< vector<int> > &a, const vector< vector<int> > &b, int aHeight, int aWidth, int bHeight, int bWidth) { vector < vector<int> > c(aHeight, vector<int>(bWidth, 0)); #pragma omp parallel for collapse(2) for(int row = 0; row < aHeight; row++) { for(int col = 0; col < bWidth; col++) { int value = 0; for(int i = 0; i < aWidth; i++) { value += a[row][i] * b[i][col]; } c[row][col] = value; cout<<

问题 I have a code with following structure #pragma omp parallel { #omp for nowait { // first for loop } #omp for nowait { // first for loop } #pragma barrier <-- #pragma omp single/critical/atomic --> not sure dgemm_(....) #pragma omp for { // yet another for loop } } For dgemm_, I link with multithreaded mkl. I want mkl to use all available 8 threads. What is the best way to do so? 回答1: This is a case of nested parallelism. It is supported by MKL, but it only works if your executable is built