avx2

来源： https://stackoverflow.com/questions/61165307/vectorize-random-init-and-print-for-bigint-with-decimal-digit-array-with-avx2

来源： https://stackoverflow.com/questions/37329329/c-fastest-method-to-swap-two-memory-blocks-of-equal-size-solution-feasibilit

来源： https://stackoverflow.com/questions/31466848/avx-256-bit-code-performing-slightly-worse-than-equivalent-128-bit-ssse3-code

来源： https://stackoverflow.com/questions/31466848/avx-256-bit-code-performing-slightly-worse-than-equivalent-128-bit-ssse3-code

来源： https://stackoverflow.com/questions/63360909/avx2-simd-performs-relatively-worse-to-scalar-at-higher-optimization-level

问题 Matrix transpose in pure golang is slow, and using package gonum needs structure transformation which costs extra time. So a assembly version may be a better solution. Sizes of the matrix vary ( [][]byte ) or can be fixed ( [64][512]byte ), and the element type may be int32 or int64 for general scenarios. Below is a golang version: m := 64 n := 512 // orignial matrix M := make([][]byte, m) for i := 0; i < m; i++ { M[i] = make([]byte, n) } func transpose(M [][]byte) [][]byte { m := len(M) n :=

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 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 have been using the following "trick" in C code with SSE2 for single precision floats for a while now: static inline __m128 SSEI_m128shift(__m128 data) { return (__m128)_mm_srli_si128(_mm_castps_si128(data), 4); } For data like [1.0, 2.0, 3.0, 4.0] , it results in [2.0, 3.0, 4.0, 0.0] , i.e. it does a left shift by one position and fills the data structure with a zero. If I remember correctly, the above inline function compiles down to a single instruction (with gcc at least). I am somehow