avx

Loading 8 chars from memory into an __m256 variable as packed single precision floats

由 本秂侑毒 提交于 2019-11-26 11:29:32
问题 I am optimizing an algorithm for Gaussian blur on an image and I want to replace the usage of a float buffer[8] in the code below with an __m256 intrinsic variable. What series of instructions is best suited for this task? // unsigned char *new_image is loaded with data ... float buffer[8]; buffer[x ] = new_image[x]; buffer[x + 1] = new_image[x + 1]; buffer[x + 2] = new_image[x + 2]; buffer[x + 3] = new_image[x + 3]; buffer[x + 4] = new_image[x + 4]; buffer[x + 5] = new_image[x + 5]; buffer[x

Per-element atomicity of vector load/store and gather/scatter?

由 放肆的年华 提交于 2019-11-26 11:27:25
问题 Consider an array like atomic<int32_t> shared_array[] . What if you want to SIMD vectorize for(...) sum += shared_array[i].load(memory_order_relaxed) ?. Or to search an array for the first non-zero element, or zero a range of it? It\'s probably rare, but consider any use-case where tearing within an element is not allowed, but reordering between elements is fine. (Perhaps a search to find a candidate for a CAS). I think x86 aligned vector loads/stores would be safe in practice to use on for

cpu dispatcher for visual studio for AVX and SSE

由 会有一股神秘感。 提交于 2019-11-26 11:15:01
问题 I work with two computers. One without AVX support and one with AVX. It would be convenient to have my code find the instruction set supported by my CPU at run-time and choose the appropriate code path. I\'ve follow the suggestions by Agner Fog to make a CPU dispatcher (http://www.agner.org/optimize/#vectorclass). However, on my maching ithout AVX compiling and linking with visual studio the code with AVX enabled causes the code to crash when I run it. I mean for example I have two source

practical BigNum AVX/SSE possible?

由 。_饼干妹妹 提交于 2019-11-26 09:12:45
问题 SSE/AVX registers could be viewed as integer or floating point BigNums. That is, one could neglect that there exist lanes at all. Does there exist an easy way to exploit this point of view and use these registers as BigNums either singly or combined? I ask because from what little I\'ve seen of BigNum libraries, they almost universally store and do arithmetic on arrays, not on SSE/AVX registers. Portability? Example: Say you store the contents of a SSE register as a key in a std::set , you

Why is this SSE code 6 times slower without VZEROUPPER on Skylake?

由 孤街醉人 提交于 2019-11-26 08:06:43
I've been trying to figure out a performance problem in an application and have finally narrowed it down to a really weird problem. The following piece of code runs 6 times slower on a Skylake CPU (i5-6500) if the VZEROUPPER instruction is commented out. I've tested Sandy Bridge and Ivy Bridge CPUs and both versions run at the same speed, with or without VZEROUPPER . Now I have a fairly good idea of what VZEROUPPER does and I think it should not matter at all to this code when there are no VEX coded instructions and no calls to any function which might contain them. The fact that it does not

How to solve the 32-byte-alignment issue for AVX load/store operations?

由 谁说我不能喝 提交于 2019-11-26 07:46:12
问题 I am having alignment issue while using ymm registers, with some snippets of code that seems fine to me. Here is a minimal working example: #include <iostream> #include <immintrin.h> inline void ones(float *a) { __m256 out_aligned = _mm256_set1_ps(1.0f); _mm256_store_ps(a,out_aligned); } int main() { size_t ss = 8; float *a = new float[ss]; ones(a); delete [] a; std::cout << \"All Good!\" << std::endl; return 0; } Certainly, sizeof(float) is 4 on my architecture (Intel(R) Xeon(R) CPU E5-2650

How to use Fused Multiply-Add (FMA) instructions with SSE/AVX

由 我的梦境 提交于 2019-11-26 07:28:19
问题 I have learned that some Intel/AMD CPUs can do simultanous multiply and add with SSE/AVX: FLOPS per cycle for sandy-bridge and haswell SSE2/AVX/AVX2. I like to know how to do this best in code and I also want to know how it\'s done internally in the CPU. I mean with the super-scalar architecture. Let\'s say I want to do a long sum such as the following in SSE: //sum = a1*b1 + a2*b2 + a3*b3 +... where a is a scalar and b is a SIMD vector (e.g. from matrix multiplication) sum = _mm_set1_ps(0.0f

Measuring memory bandwidth from the dot product of two arrays

由 こ雲淡風輕ζ 提交于 2019-11-26 06:45:48
问题 The dot product of two arrays for(int i=0; i<n; i++) { sum += x[i]*y[i]; } does not reuse data so it should be a memory bound operation. Therefore, I should be able to measure the memory bandwidth from the dot product. Using the code at why-vectorizing-the-loop-does-not-have-performance-improvement I get a bandwidth of 9.3 GB/s for my system . However, when I attempt to calculate the bandwidth using the dot product I get over twice the rate for a single thread and over three time the rate

Fast vectorized rsqrt and reciprocal with SSE/AVX depending on precision

由 我的梦境 提交于 2019-11-26 06:42:11
问题 Suppose that it is necessary to compute reciprocal or reciprocal square root for packed floating point data. Both can easily be done by: __m128 recip_float4_ieee(__m128 x) { return _mm_div_ps(_mm_set1_ps(1.0f), x); } __m128 rsqrt_float4_ieee(__m128 x) { return _mm_div_ps(_mm_set1_ps(1.0f), _mm_sqrt_ps(x)); } This works perfectly well but slow: according to the guide, they take 14 and 28 cycles on Sandy Bridge (throughput). Corresponding AVX versions take almost the same time on Haswell. On

is there an inverse instruction to the movemask instruction in intel avx2?

由 纵然是瞬间 提交于 2019-11-26 04:00:11
问题 The movemask instruction(s) take an __m256i and return an int32 where each bit (either the first 4, 8 or all 32 bits depending on the input vector element type) is the most significant bit of the corresponding vector element. I would like to do the inverse: take a 32 (where only the 4, 8 or 32 least significant bits are meaningful), and get a __m256i where the most significant bit of each int8, int32 or int64 sized block is set to the original bit. Basically, I want to go from a compressed
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