sse

问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 4 years ago . Is there any good C/C++ tutorials or examples for learning Intel SSE and AVX instructions? I found few on Microsoft MSDN and Intel sites, but it would be great to understand it from the basics.. 回答1: For the visually inclined SIMD programmer, Stefano Tommesani's site is the best introduction to x86 SIMD

问题 I tried to use SSE to do 4 pixels operation. I have problem in loading the image data to __m128. My image data is a char buffer. Let say my image is 1024 x1024. My filter is 16x16. __m128 IMG_VALUES, FIL_VALUES, NEW_VALUES; //ok: IMG_VALUES=_mm_load_ps(&pInput[0]); //hang below: IMG_VALUES=_mm_load_ps(&pInput[1]); I dont know how to handle index 1,2,3... thanks. 回答1: If you really need to do this with floating point rather then integer/fixed point then you will need to load your 8 bit data,

问题 I have a version of OpenCV 2.4.10 Library which was built for Intel X64 on Windows. How can I know if the CV_SSE2 is active? I do not have the code. I just have the libs ,DLLs and headers. Thanks 回答1: You can check if SSE2 is enabled with the function checkHardwareSupport like: #include <opencv2/opencv.hpp> #include <iostream> int main() { cv::setUseOptimized(true); // Turn on optimization (if it was disabled) // Get other build information //std::cout << cv::getBuildInformation(); // Check

问题 I have an image drawing routine which is compiled multiple times for SSE, SSE2, SSE3, SSE4.1, SSE4.2, AVX and AVX2. My program dynamically dispatches one of these binary variations by checking CPUID flags. On Windows, I check the version of Windows and disable AVX/AVX2 dispatch if the OS doesn't support them. (For example, only Windows 7 SP1 or later supports AVX/AVX2.) I want to do the same thing on Mac OS X, but I'm not sure what version of OS X supports AVX/AVX2. Note that what I want to

问题 EDIT: As Cody Gray pointed out in his comment, profiling with disabled optimization is complete waste of time. How then should i approach this test? Microsoft in its XMVectorZero in case if defined _XM_SSE_INTRINSICS_ uses _mm_setzero_ps and {0.0f,0.0f,0.0f,0.0f} if don't. I decided to check how big is the win. So i used the following program in Release x86 and Configuration Properties>C/C++>Optimization>Optimization set to Disabled (/Od) . constexpr __int64 loops = 1e9; inline void fooSSE()

问题 I'd like to enable temporarily FTZ / DAZ modes to get a performance gain for some code where strict compliance with the IEEE 754 standard is not an issue, without changing the behaviour of other threads, which could be executing code, where that compliance is important. I've been reading this on how to enable/disable these modes and this on the performance impact of denormals handling, but unfortunately I've got a mixed code in a multithreaded environment and I cannot enable these modes once

问题 This question already has an answer here : what's the difference between _mm256_lddqu_si256 and _mm256_loadu_si256 (1 answer) Closed 2 years ago . I would like to know more about the _mm_lddqu_si128 intrinsic ( lddqu instruction since SSE3) particularly compared with the _mm_loadu_si128 intrinsic (movdqu instruction since SSE2) . I only discovered _mm_lddqu_si128 today. The intel intrinsic guide says this intrinsic may perform better than _mm_loadu_si128 when the data crosses a cache line

问题 Why do we need aligned memory for SSE/AVX? One of the answer I often get is aligned memory load is much faster than unaligned memory load. Then, why is this aligned memory load is much faster than unaligned memory load? 回答1: This is not just specific to SSE (or even x86). On most architectures loads and stores need to be naturally aligned otherwise they either (a) generate an exception or (b) need two or more cycles plus some fix up in order to handle the misaligned load/store transparently.

问题 I have manged to convert most of my SIMD code to us the vector extensions of GCC. However, I have not found a good solution for doing a broadcast as follows __m256 areg0 = _mm256_broadcast_ss(&a[i]); I want to do __m256 argeg0 = a[i]; If you see my answer at Mutiplying vector by constant using SSE I managed to get broadcasts working with another SIMD register. The following works: __m256 x,y; y = x + 3.14159f; // broadcast x + 3.14159 y = 3.14159f*x; // broadcast 3.14159*x but this won't work

问题 This question already has an answer here : inlining failed in call to always_inline ‘_mm_mullo_epi32’: target specific option mismatch (1 answer) Closed last year . I am trying to compile this project from github which is implemented in C++ with SIMD intrinsic (SSE4.1). The project in github is given as a Visual Studio solution, but I am trying to port it in Qtcreator with cmake. While I am trying to compile it I get the following error: /usr/lib/gcc/x86_64-unknown-linux-gnu/5.3.0/include