avx2

问题 I am try to optimizing the performance of the following naive program without changing the algorithm : naive (int n, const int *a, const int *b, int *c) //a,b are two array with given size n; { for (int k = 0; k < n; k++) for (int i = 0; i < n - k; ++i) c[k] += a[i + k] * b[i]; } My idea is as follows : First, I use OpenMP for the outer loop. For the inner loop, as it is imbalanced, I specify n-k to determine whether to use AXV2 SIMD intrinsic or simply reduce . And finally, I find that it

问题 AVX allow for bitwise logical operations such as and/or on floating point data-type __m256 and __m256d. However, C++ doesn't allow for bitwise operations on floats and doubles, reasonably. If I'm right, there's no guarantee on the internal representation of floats, whether the compiler will use IEEE754 or not, hence a programmer can't be sure about how the bits of a float will look like. Consider this example: #include <immintrin.h> #include <iostream> #include <limits> #include <cassert> int

问题 Closed. This question needs debugging details. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 2 years ago . I'm currently working on some optimizations and comparing vectorization possibilities for DSP applications, that seem ideal for AVX512, since these are just simple uncorrelated array processing loops. But on a new i9 I didn't measure any reasonable improvements when using AVX512 compared to AVX2. Any

问题 I am using Tensorflow's Anaconda distribution with MKL support. from tensorflow.python.framework import test_util test_util.IsMklEnabled() This code prints True . However, when I compile my Keras model I still get Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2 This is not the behavior I was expecting. 回答1: The MKL-DNN portions of the tensorflow execution (which is the main acceleration provided by MKL engineers) are JIT'ed at runtime. So the

问题 I am using Tensorflow's Anaconda distribution with MKL support. from tensorflow.python.framework import test_util test_util.IsMklEnabled() This code prints True . However, when I compile my Keras model I still get Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2 This is not the behavior I was expecting. 回答1: The MKL-DNN portions of the tensorflow execution (which is the main acceleration provided by MKL engineers) are JIT'ed at runtime. So the

问题 avx introduced the instruction vperm2f128 (exposed via _mm256_permute2f128_si256 ), while avx2 introduced vperm2i128 (exposed via _mm256_permute2x128_si256 ). They both seem to be doing exactly the same, and their respective latencies and throughputs also seem to be identical. So why do both instructions exist? There has to be some reasoning behind that? Is there maybe something I have overlooked? Given that avx2 operates on data structures introduced with avx, I cannot imagine that a

问题 avx introduced the instruction vperm2f128 (exposed via _mm256_permute2f128_si256 ), while avx2 introduced vperm2i128 (exposed via _mm256_permute2x128_si256 ). They both seem to be doing exactly the same, and their respective latencies and throughputs also seem to be identical. So why do both instructions exist? There has to be some reasoning behind that? Is there maybe something I have overlooked? Given that avx2 operates on data structures introduced with avx, I cannot imagine that a

问题 I'm writing a program to detect primes numbers. One part is bit sieving possible candidates out. I've written a fairly fast program but I thought I'd see if anyone has some better ideas. My program could use some fast gather and scatter instructions but I'm limited to AVX2 hardware for a x86 architecture (I know AVX-512 has these though I'd not sure how fast they are). #include <stdint.h> #include <immintrin.h> #define USE_AVX2 // Sieve the bits in array sieveX for later use void sieveFactors

问题 I am looking for an optimal method to calculate sum of all packed 32-bit integers in a __m256i or __m512i . To calculate sum of n elements, I ofter use log2(n) vpaddd and vpermd function, then extract the final result. Howerver, it is not the best option I think. Edit: best/optimal in term of speed/cycle reduction. 回答1: (Related: if you're looking for the non-existant _mm512_reduce_add_epu8 , see Summing 8-bit integers in __m512i with AVX intrinsics; vpsadbw as an hsum within qwords is much

问题 I am looking for an optimal method to calculate sum of all packed 32-bit integers in a __m256i or __m512i . To calculate sum of n elements, I ofter use log2(n) vpaddd and vpermd function, then extract the final result. Howerver, it is not the best option I think. Edit: best/optimal in term of speed/cycle reduction. 回答1: (Related: if you're looking for the non-existant _mm512_reduce_add_epu8 , see Summing 8-bit integers in __m512i with AVX intrinsics; vpsadbw as an hsum within qwords is much