simd

问题 What is the difference between non-packed and packed instruction in the context of SIMD-operations? I was reading an article on optimizing your code for SSE: http://www.cortstratton.org/articles/OptimizingForSSE.php#batch and this question arose when I read "As an added bonus, movss is a non-packed instruction, which allows us to make better use of the parallel instruction decoders.." So what is the difference? 回答1: To my understanding, packed means that conceptually more than one value is

问题 What is the difference between non-packed and packed instruction in the context of SIMD-operations? I was reading an article on optimizing your code for SSE: http://www.cortstratton.org/articles/OptimizingForSSE.php#batch and this question arose when I read "As an added bonus, movss is a non-packed instruction, which allows us to make better use of the parallel instruction decoders.." So what is the difference? 回答1: To my understanding, packed means that conceptually more than one value is

问题 I've been reading up on the recently available AVX-512 instructions, and I feel like there is a basic concept that I'm not understanding. What is the benefit of SIMD on a superscalar CPU that already performs out of order execution? Consider the following pseudo assembly code. With SIMD: load 16 floats to register simd-a load 16 floats to register simd-b multiply register simd-a by simd-b as 16 floats to register c store the results to memory And this without SIMD: load a float to register a

问题 I am planning to implement runtime detection of SIMD extensions. Is it such that if I find out that the processor has AVX2 support, it is also guaranteed to have SSE4.2 and AVX support? 回答1: Support for a more-recent Intel SIMD ISA extension implies support for previous SIMD ones. AVX2 definitely implies AVX1. I think AVX1 implies all of SSE/SSE2/SSE3/SSSE3/SSE4.1/SSE4.2 feature bits must also be set in CPUID. If not formally guaranteed, many things make this assumption and a CPU that

问题 Using C#'s Vector<T> , how can we most efficiently vectorize the operation of finding the index of a particular element in a set? As constraints, the set will always be a Span<T> of an integer primitive, and it will contain at most 1 matching element. I have come up with a solution that seems alright, but I'm curious if we can do better. Here is the approach: Create a Vector<T> consisting only of the target element, in each slot. Use Vector.Equals() between the input set vector and the vector

问题 I will preface this by saying that I am a complete beginner at SIMD intrinsics. Essentially, I have a CPU which supports the AVX2 instrinsic ( Intel(R) Core(TM) i5-7500T CPU @ 2.70GHz ). I would like to know the fastest way to compute the dot product of two std::vector<float> of size 512 . I have done some digging online and found this and this, and this stack overflow question suggests using the following function __m256 _mm256_dp_ps(__m256 m1, __m256 m2, const int mask); , However, these

问题 I will preface this by saying that I am a complete beginner at SIMD intrinsics. Essentially, I have a CPU which supports the AVX2 instrinsic ( Intel(R) Core(TM) i5-7500T CPU @ 2.70GHz ). I would like to know the fastest way to compute the dot product of two std::vector<float> of size 512 . I have done some digging online and found this and this, and this stack overflow question suggests using the following function __m256 _mm256_dp_ps(__m256 m1, __m256 m2, const int mask); , However, these

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 I have a __m128i variable and I need to shift its 128 bit value of n bits, i.e. like _mm_srli_si128 and _mm_slli_si128 work, but on bits instead of bytes. What is the most efficient way of doing this? 回答1: This is the best that I could come up with for left/right immediate shifts with SSE2: #include <stdio.h> #include <emmintrin.h> #define SHL128(v, n) \ ({ \ __m128i v1, v2; \ \ if ((n) >= 64) \ { \ v1 = _mm_slli_si128(v, 8); \ v1 = _mm_slli_epi64(v1, (n) - 64); \ } \ else \ { \ v1 = _mm_slli

问题 I have a __m128i variable and I need to shift its 128 bit value of n bits, i.e. like _mm_srli_si128 and _mm_slli_si128 work, but on bits instead of bytes. What is the most efficient way of doing this? 回答1: This is the best that I could come up with for left/right immediate shifts with SSE2: #include <stdio.h> #include <emmintrin.h> #define SHL128(v, n) \ ({ \ __m128i v1, v2; \ \ if ((n) >= 64) \ { \ v1 = _mm_slli_si128(v, 8); \ v1 = _mm_slli_epi64(v1, (n) - 64); \ } \ else \ { \ v1 = _mm_slli