For example this is the reason why in nested loops the #pragma omp for should always be in the outermost "possible" loop. Using OpenMP is a catch only if you parallelize sufficently CPU intensive functions (which is not really the case of a simple array sum.).Why Does the pthread library take most of the CPU time And I modified the parallel code to have more threads (e.g. I found that the sequential code is faster than parallel one. I got the code which calculated the pi value from LDP parallel programming HOWTO. In your test case the setting up takes most of the time. Im a beginner in parallel programming using pthread in Linux. If you bench an OpenMP code try to set up the pool thread beforehand with a simple operation with #pragma omp parallel.However for more CPU intensive operations using OpenMP can induce speed up even with 1000 samples (with the processor I used).But if I were to use 256 threads it would surely be around 6000000. In this case (with the processor I used) the minimum number of samples is around 100000. Otherwise there is no point dividing the operation into threads. In other words the amount of operations inside a parallel section must be high enough to render the time needed for thread pool management negligable. We can see that we do not observe scalability with a small number of samples. At the second call (with i=256) we dont see the benefit of using OpenMP but the timings are coherent. If we take a closer look at the 'intensive case' the first time we enter the test_ function (with i=128) the time cost is way higher in the OpenMP case than the in No OpenMP case. There is an offset the first time using OpenMP (the first #pragma omp crossed) because the pool thread must be set in place.How you benchmark your program will depend on your own success criteria. The result is that the program is faster for this input. Graph for the complex operation (axis are in log scale !) Parallel: Lowest: 18889 Highest: 4.29496e+09 Time: 20.230400ms. Graph for the simple operation (axis are in log scale !) Time measurements are done with chrono using high_precision_clock and the limit precision on my machine is microseconds hence the use of std::chrono::microseconds (no point looking for higher precision) OpenMP (OpenMP) version is compiled with : icpc test_omp.cpp -O3 -std=c++0x -openmp Sequential (No OpenMP) version is compiled with : icpc test_omp.cpp -O3 -std=c++0x Test_intensive(i, ary, sum, elapsed_milli) However, it still runs slower, I am intending that it may run about twice as fast or well faster. I divided the for loop into two threads and run them in parallel using pthreads. I tried with both heap allocated and stack allocated arrays and got similar results. However, the OpenMP implementation surprisingly is slower than the sequential implementation. If you call Break() (note: not Stop() due to out of order execution you may get a too large value then) on that object, the loop will break at earliest convenience.I am trying to find sum of elements in an array as I show below. What you probably want to do instead is to use this version of Parallel.ForEach that gives you a ParallelLoopState with every iteration. Since you're running tasks, elements may be handled out of order. Note that that may not be the highest prime in the range, just the last found. This will also make the parallel version buggy, since it won't return the first found prime in the range, but actually the last found one since it overwrites nextP every time a new prime is found in the range. The reason is that the parallel ForAll won't terminate when it finds the first prime but will always loop though the whole range before returning, while your non parallel version will return the first value right away when its found.
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