3 ms·

> There are still cross-architecture differences like 8-wide AVX2 vs 4-wide NEON that can trip you up Does those differences produce different results when run

• boulos 2y ago
  Depends on what you're doing. The main issue here is reductions / accumulations. That is, if you have a bunch of floats like: float sum = 0.f; for (int i = 0; i < N; i++) { sum += x[i]; } and that you vectorize that to something like (typing in this comment, errors are likely): __mm256 sum_8wide = _mm256_setzero_ps(); for (int i = 0; i < N/8; i++) { sum_8wide = _mm256_add_ps(sum_8wide, _mm256_load_ps(x[8*i]); } // Now sum up the 8 values to get the final sum float sum = _mm256_hadd_ps(...); that will result in a different accumulation than if you did those are 4-wide and then a reduction. The usual solution is to either use the lowest common denominator (e.g., use AVX instead of AVX2) or more performance oriented, use the 4-wide SIMD units on ARM to "emulate" an 8-wide virtual vector (~15 years since I wrote NEON... and again, this is in a comment): float32x4_t sum_lo = ; // zero; float32x4_t sum_hi = ; // zero; for (int i = 0; i < N/8; i++) { sum_lo = vaddq_f32(sum_lo, vload1q_f32(x[8*i)); sum_hi = vaddq_f32(sum_hi, vload1q_f32(x[8*i + 4])); } // Reduce the sum in the same order You would want to write a "virtual SIMD" wrapper library, so you don't do this manually in lots of places.