avx

来源： https://stackoverflow.com/questions/62022827/attributeerror-module-tensorflow-has-no-attribute-compat

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 wanted to try and atomically reset 256 bits using something like this: #include <x86intrin.h> #include <iostream> #include <array> #include <atomic> int main(){ std::array<std::atomic<__m256i>, 10> updateArray; __m256i allZeros = _mm256_setzero_si256(); updateArray[0].fetch_and(allZeros); } but I get compiler errors about the element not having fetch_and() . Is this not possible because 256 bit type is too large to guarantee atomicity? Is there any other way I can implement this? I am using

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 I have written a library, where I use CMake for verifying the presence of headers for MMX, SSE, SSE2, SSE4, AVX, AVX2, and AVX-512. In addition to this, I check for the presence of the instructions and if present, I add the necessary compiler flags, -msse2 -mavx -mfma etc. This is all very good, but I would like to deploy a single binary, which works across a range of generations of processors. Question: Is it possible to tell the compiler (GCC) that whenever it optimizes a function using

问题 My goal is to create a PCIe transaction with more than 64b payload. For that I need to read an ioremap() address. For 128b and 256b I can use xmm and ymm registers respectively and that works as expected. Now, I'd like to do the same for 512b zmm registers (memory-like storage?!) A code under license I'm not allowed to show here, uses assembly code for 256b: void __iomem *addr; uint8_t datareg[32]; [...] // Read memory address to ymm (to have 256b at once): asm volatile("vmovdqa %0,%%ymm1" :

问题 I am new to AVX512 instruction set and I write the following code as demo. #include <iostream> #include <array> #include <chrono> #include <vector> #include <cstring> #include <omp.h> #include <immintrin.h> #include <cstdlib> int main() { unsigned long m, n, k; m = n = k = 1 << 30; auto *a = static_cast<double*>(aligned_alloc(512, m*sizeof(double))); auto *b = static_cast<double*>(aligned_alloc(512, n*sizeof(double))); auto *c = static_cast<double*>(aligned_alloc(512, k*sizeof(double)));

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 have a square boolean matrix M of size N, stored by rows and I want to count the number of bits set to 1 for each column. For instance for n=4: 1101 0101 0001 1001 M stored as { { 1,1,0,1}, {0,1,0,1}, {0,0,0,1}, {1,0,0,1} }; result = { 2, 2, 0, 4}; I can obviously transpose the matrix M into a matrix M' popcount each row of M'. Good algorithms exist for matrix transposition and popcounting through bit manipulation. My question is: would it be possible to "merge" such algorithms into a