false-sharing

问题 I know the answer could be that they did not prioritize it, but it really feels like intentional omission, they already have plenty of C++20 core language/library features and this C++17 feature is still not implemented. In fact according to this table it is the only C++17 library feature that both clang and gcc did not implement. 来源： https://stackoverflow.com/questions/62025586/what-is-the-reason-why-clang-and-gcc-do-not-implement-stdhardware-constructiv

问题 I know the answer could be that they did not prioritize it, but it really feels like intentional omission, they already have plenty of C++20 core language/library features and this C++17 feature is still not implemented. In fact according to this table it is the only C++17 library feature that both clang and gcc did not implement. 来源： https://stackoverflow.com/questions/62025586/what-is-the-reason-why-clang-and-gcc-do-not-implement-stdhardware-constructiv

问题 I learnt OpenMP using Tim Matterson's lecture notes, and he gave an example of false sharing as below. The code is simple and is used to calculate pi from numerical integral of 4.0/(1+x*x) with x ranges from 0 to 1. The code uses a vector to contain the value of 4.0/(1+x*x) for each x from 0 to 1, then sum the vector at the end: #include <omp.h> static long num_steps = 100000; double step; #define NUM_THREADS 2 void main() { int i, nthreads; double pi, sum[NUM_THREADS]; step = 1.0/(double)num

来源： https://stackoverflow.com/questions/44944839/does-false-sharing-occur-when-data-is-read-in-openmp

问题 Consider the following example that proves false sharing existence: using type = std::atomic<std::int64_t>; struct alignas(128) shared_t { type a; type b; } sh; struct not_shared_t { alignas(128) type a; alignas(128) type b; } not_sh; One thread increments a by steps of 1, another thread increments b . Increments compile to lock xadd with MSVC, even though the result is unused. For a structure where a and b are separated, the values accumulated in a few seconds is about ten times greater for

问题 I am looking at the performance of OpenMP program, specifically cache and memory performance. I have found guidelines while back ago how to analyze performance with Vtune that mentioned which counters to watch out for. However now cannot seem to find the manual. If you know which manual I have in question or if you know the counters/events, please let me know. Also if you have other techniques for analyzing multithreaded memory performance, please share if you can Thanks 回答1: Here is an

问题 I would like to take advantage of OpenMP to make my task parallel. I need to subtract the same quantity to all the elements of an array and write the result in another vector. Both arrays are dynamically allocated with malloc and the first one is filled with values from a file. Each element is of type uint64_t . #pragma omp parallel for for (uint64_t i = 0; i < size; ++i) { new_vec[i] = vec[i] - shift; } Where shift is the fixed value I want to remove from every element of vec . size is the

问题 Today I got a different understand with my professor on the Parallel Programming class, about what is "false sharing". What my professor said makes little sense so I pointed it out immediately. She thought "false sharing" will cause a mistake in the program's result. I said, "false sharing" happens when different memory address are assigned to the same cache line, writing data to one of it will cause another being kicked out of the cache. If the processors write between the two false sharing

问题 System Specification: Intel Xeon E7-v3 Processor(4 sockets, 16 cores/sockets, 2 threads/core) Use of Eigen family and C++ Following is serial implementation of code snippet: Eigen::VectorXd get_Row(const int j, const int nColStart, const int nCols) { Eigen::VectorXd row(nCols); for (int k=0; k<nCols; ++k) { row(k) = get_Matrix_Entry(j,k+nColStart); } } double get_Matrix_Entry(int x , int y){ return exp(-(x-y)*(x-y)); } I need to parallelise the get_Row part as nCols can be as large as 10^6,

问题 I believe I am experiencing false sharing using OpenMP. Is there any way to identify it and fix it? My code is: https://github.com/wchan/libNN/blob/master/ResilientBackpropagation.hpp line 36. Using a 4 core CPU compared to the single threaded 1 core version yielded only 10% in additional performance. When using a NUMA 32 physical (64 virtual) CPU system, the CPU utilization is stuck at around 1.5 cores, I think this is a direct symptom of false sharing and unable to scale. I also tried