sse

I'm thinking I should familiarize myself with x86 SIMD extensions. But before I even began I ran into trouble. I can't find a good overview on which of them are still relevant. The x86 architecture has accumulated a lot of math/multimedia extensions over decades: MMX 3DNow! SSE SSE2 SSE3 SSSE3 SSE4 AVX AVX2 AVX512 Did I forget something? Are the newer ones supersets of the older ones and vice versa? Or are they complementary? Are some of them deprecated? Which of these are still relevant? I've heard references to "legacy SSE". Are some of them mutually exclusive? I.e. do they share the same

I am trying to multiply two vectors together where each element of one vector is multiplied by the element in the same index at the other vector. I then want to sum all the elements of the resulting vector to obtain one number. For instance, the calculation would look like this for the vectors {1,2,3,4} and {5,6,7,8}: 1*5+2*6+3*7+4*8 Essentially, I am taking the dot product of the two vectors. I know there is an SSE command to do this, but the command doesn't have an intrinsic function associated with it. At this point, I don't want to write inline assembly in my C code, so I want to use only

I have a loop written in C++ which is executed for each element of a big integer array. Inside the loop, I mask some bits of the integer and then find the min and max values. I heard that if I use SSE instructions for these operations it will run much faster compared to a normal loop written using bitwise AND , and if-else conditions. My question is should I go for these SSE instructions? Also, what happens if my code runs on a different processor? Will it still work or these instructions are processor specific? SSE instructions are processor specific. You can look up which processor supports

I'm trying to optimize some matrix computations and I was wondering if it was possible to detect at compile-time if SSE/SSE2/AVX/AVX2/AVX-512/AVX-128-FMA/KCVI [1] is enabled by the compiler ? Ideally for GCC and Clang, but I can manage with only one of them. I'm not sure it is possible and perhaps I will use my own macro, but I'd prefer detecting it rather and asking the user to select it. [1] "KCVI" stands for Knights Corner Vector Instruction optimizations. Libraries like FFTW detect/utilize these newer instruction optimizations. Paul R Most compilers will automatically define: __SSE__ _

Given that _mm256_sqrt_ps() is relatively slow, and that the values I am generating are immediately truncated with _mm256_floor_ps() , looking around it seems that doing: _mm256_mul_ps(_mm256_rsqrt_ps(eightFloats), eightFloats); Is the way to go for that extra bit of performance and avoiding a pipeline stall. Unfortunately, with zero values, I of course get a crash calculating 1/sqrt(0) . What is the best way around this? I have tried this (which works and is faster), but is there a better way, or am I going to run into problems under certain conditions? _mm256_mul_ps(_mm256_rsqrt_ps(_mm256

I know how to sum one __m256 to get a single summed value. However, I have 8 vectors like Input 1: a[0], a[1], a[2], a[3], a[4], a[5], a[6], a[7], ....., ....., 8: h[0], h[1], h[2], h[3], h[4], a[5], a[6], a[7] Output a[0]+a[1]+a[2]+a[3]+a[4]+a[5]+a[6]+a[7], ...., h[0]+h[1]+h[2]+h[3]+h[4]+h[5]+h[6]+h[7] My method. Curious if there is a better way. __m256 sumab = _mm256_hadd_ps(accumulator1, accumulator2); __m256 sumcd = _mm256_hadd_ps(accumulator3, accumulator4); __m256 sumef = _mm256_hadd_ps(accumulator5, accumulator6); __m256 sumgh = _mm256_hadd_ps(accumulator7, accumulator8); __m256 sumabcd