I am trying simple matrix addition code to get auto vectorized. float A[100]; float A[100]; float C[100]; int n = 6; for (int i=0; i<n; i++) for (int j=0; j<n; j++) C[i*n+j] = A[i*n+j] + B[i*n+j]; I am getting following remarks after using Rpass analysis flag for the above code : the cost-model indicates that ..
Category : auto-vectorization
I am trying to vectorize the for loop. After using the Rpass flag in flag, I am getting following remark for it. int someOuterVariable = 0; for (unsigned int i = 7; i != -1; i–) { array[someOuterVariable + i] -= 0.3 * anotherArray[i]; } Remark: The cost-model indicates that vectorization is not beneficial the ..
I am wondering what should be the minimum for loop length for the auto vectorizer to enable the vectorization. ? for eg: for (int i= 0 ; i< 4 ; i+= 3) // Some operations using Rpass flag, I am getting following remark. loop control flow is not understood by vectorizer [-Rpass-analysis=loop-vectorize] can someone help ..
I am trying to test out auto-vectorization on the for loop below. I am unable to get the auto vectorization to work. The code is shown below. float dotproduct(float a[], float b[], int size) { int x = 0.0; for (int i = 0; i < size; i++) { x = x + a[i] * ..
I’ve been thinking about ecatmur’s constexpr swap() function and I believe it’s a special case of a more generic shuffle() function: template <std::size_t …I, std::size_t …J, typename T> constexpr T shuffle(T const i, std::index_sequence<J…>) noexcept { return ((std::uint8_t(i >> 8 * I) << 8 * J) | …); } I are source indices and J ..
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