openmp

If I make a piece of code in which each thread modifies completely different parts of an array, will that maintain cache coherency?

由 限于喜欢 提交于 2019-12-12 17:58:52
问题 So I am making some parallel code using OpenMP (but this question should be reasonably applicable to other frameworks), in which I have an array of objects: std::vector<Body> bodies; And then I do a little parallel loop to do some things to the bodies . At the start of this parallel section, a team of threads is set up to execute the loop individually. The loop basically uses the values of foo on every Body (apart from the one in question) to update the value of bar on the body in question.

Summing with OpenMP using C

由 馋奶兔 提交于 2019-12-12 15:37:21
问题 I've been trying to parallelize this piece of code for about two days and keep having logical errors. The program is to find the area of an integral using the sum of the very small dx and calculate each discrete value of the integral. I am trying to implement this with openmp but I actually have no experience with openmp. I would like your help please. The actual goal is to parallelize the suma variable in the threads so every thread calculates less values of the integral. The program

#pragma omp flush to make exchange data among threads

由 故事扮演 提交于 2019-12-12 15:28:40
问题 Hi writing a very simple example on how to use omp flush to exchange data, in a producer-> consumer way,among threads I have found a funny behavior. int a=-1; int flag=1; int count=0; #pragma omp parallel num_threads(2) { int TID; TID=omp_get_thread_num(); #pragma omp sections { #pragma omp section /////////// Producer { for(int i=0; i<9;i++) { a=i; #pragma omp flush(a) flag=1; printf("Producer a: %d flag:%d TID %d \n",a,flag,TID); while(flag) { #pragma omp flush(flag) } } flag=2; #pragma omp

Manual synchronization in OpenMP while loop

由 淺唱寂寞╮ 提交于 2019-12-12 15:08:59
问题 I recently started working with OpenMP to do some 'research' for an project in university. I have a rectangular and evenly spaced grid on which I'm solving a partial differential equation with an iterative scheme. So I basically have two for-loops (one in x- and y-direction of the grid each) wrapped by a while-loop for the iterations. Now I want to investigate different parallelization schemes for this. The first (obvious) approach was to do a spatial a parallelization on the for loops. Works

Reduce console verbosity

由 北城余情 提交于 2019-12-12 13:06:51
问题 I am running some training and prediction with Keras/TensorFlow and I get some OMP information that I do not need. 2019-05-20 12:11:45.625897: I tensorflow/core/common_runtime/process_util.cc:71] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best p erformance. OMP: Info #250: KMP_AFFINITY: pid 22357 tid 22400 thread 1 bound to OS proc set 1 OMP: Info #250: KMP_AFFINITY: pid 22357 tid 22428 thread 2 bound to OS proc set 2 OMP: Info #250:

Rcpp Parallel or openmp for matrixvector product

由 大兔子大兔子 提交于 2019-12-12 12:27:05
问题 I am trying to program the naive parallel version of Conjugate gradient, so I started with the simple Wikipedia algorithm, and I want to change the dot-products and MatrixVector products by their appropriate parallel version, The Rcppparallel documentation has the code for the dot-product using parallelReduce; I think I'm gonna use that version for my code, but I'm trying to make the MatrixVector multiplication, but I haven't achieved good results compared to R base (no parallel) Some

Using OpenMP to calculate the value of PI

由 我的梦境 提交于 2019-12-12 12:04:25
问题 I'm trying to learn how to use OpenMP by parallelizing a monte carlo code that calculates the value of PI with a given number of iterations. The meat of the code is this: int chunk = CHUNKSIZE; count=0; #pragma omp parallel shared(chunk,count) private(i) { #pragma omp for schedule(dynamic,chunk) for ( i=0; i<niter; i++) { x = (double)rand()/RAND_MAX; y = (double)rand()/RAND_MAX; z = x*x+y*y; if (z<=1) count++; } } pi=(double)count/niter*4; printf("# of trials= %d , estimate of pi is %g \n"

Updating a maximum value from multiple threads

由 本小妞迷上赌 提交于 2019-12-12 11:06:57
问题 Is there a way to update a maximum from multiple threads using atomic operations? Illustrative example: std::vector<float> coord_max(128); #pragma omp parallel for for (int i = 0; i < limit; ++i) { int j = get_coord(i); // can return any value in range [0,128) float x = compute_value(j, i); #pragma omp critical (coord_max_update) coord_max[j] = std::max(coord_max[j], x); } In the above case, the critical section synchronizes access to the entire vector, whereas we only need to synchronize

Concurrent random number generation

由 百般思念 提交于 2019-12-12 10:56:53
问题 I'm writing a parallel program using open mp in which I generate a matrix of random floating point numbers and then do a number of calculations on it. I currently want to make the step where I generate the matrix run in parallel, but I have the problem that the rand() function was not meant to run concurrently. I don't want to use locks to provide mutex on rand because this is the only thing being done in the loop and it would probably just be more efficient to run it sequentially. Is there

Comparing performance of two copying techniques?

由 僤鯓⒐⒋嵵緔 提交于 2019-12-12 10:23:37
问题 For copying a huge double array to another array I have following two options: Option 1 copy(arr1, arr1+N, arr2); Option 2 #pragma omp parallel for for(int i = 0; i < N; i++) arr2[i] = arr1[i]; I want to know for a large value of N. Which of the following will be the better (takes less time) option and when?" System configuration: Memory: 15.6 GiB Processor: Intel® Core™ i5-4590 CPU @ 3.30GHz × 4 OS-Type: 64-bit compiler: gcc (Ubuntu 4.9.2-0ubuntu1~12.04) 4.9.2 回答1: Practically , if
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