sse

Does anyone know an open-source C++ x86 SIMD intrinsics library? Intel supplies exactly what I need in their integrated performance primitives library, but I can't use that because of the copyrights all over the place. EDIT I already know the intrinsics provided by the compilers. What I need is a convenient interface to use them. p12 Take a look at libsimdpp header-only C++ SIMD wrapper library. The library supports several instruction sets via single interface: SSE2, SSE3, SSSE3, SSE4.1, AVX, AVX2, AVX512F, XOP, FMA3/4, NEON, NEONv2, Altivec. All of Clang, GCC, MSVC and ICC are suported. Any

I've been using Intel's SSE intrinsics for quite some time with good performance gains. Hence, I expected the AVX intrinsics to further speed-up my programs. This, unfortunately, was not the case until now. Probably I am doing a stupid mistake, so I would be very grateful if somebody could help me out. I use Ubuntu 11.10 with g++ 4.6.1. I compiled my program (see below) with g++ simpleExample.cpp -O3 -march=native -o simpleExample The test system has a Intel i7-2600 CPU. Here is the code which exemplifies my problem. On my system, I get the output 98.715 ms, b[42] = 0.900038 // Naive 24.457 ms

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.. For the visually inclined SIMD programmer, Stefano Tommesani's site is the best introduction to x86 SIMD programming. http://www.tommesani.com/index.php/simd/46-sse-arithmetic.html The diagrams are only provided for MMX and SSE2, but once a learner gets proficient with SSE2, it is relatively easy to move on and read the formal specifications. Intel IA-32 Instructions beginning with A to M http://www

I have a C program where I have a few arrays of unsigned ints. I'm using this declaration uint32_t . I want to use SIMD to perform some operations on the data stored in each of the arrays. This is where I'm stuck because it looks like most of the SSE and SSE2 functions only support float and double. What's the best way for me to load data of type uint32_t ? For any integer SSE type you typically use _mm_load_si128 / _mm_loadu_si128 : uint32_t a[N]; __m128i v = _mm_loadu_si128((__m128i *)a); 来源： https://stackoverflow.com/questions/30286685/how-to-load-unsigned-ints-into-simd

I am trying to figure out why g++ does not generate a SIMD code. Info GCC / OS / CPU: $ gcc -v gcc version 4.8.2 (Ubuntu 4.8.2-19ubuntu1) $ cat /proc/cpuinfo ... model name : Intel(R) Core(TM)2 Duo CPU P8600 @ 2.40GHz ... and here is my C++ code: #include <iostream> #include <cstdlib> //function that fills an array with random numbers template<class T> void fillArray(T *array, int n){ srand(1); for (int i = 0; i < n; i++) { array[i] = (float) (rand() % 10); } } // function that computes the dotprod of two vectors (loop unrolled) float dotCPP(float *src1, float *src2, int n){ float dest = 0;

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. 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, unpack to 32 bits (requires two operations: 8 bit to 16 bit, then 16 bit to 32 bit), then convert to float.

As you know we have below Shift instructions in SIMD SSE: PSLL (W-D-Q) and PSRL (W-D-Q) There's no PSLLB instruction, so how can we shift vectors of 8bit values (single bytes)? In the special-case of left-shift-by-one, you can use paddb xmm0, xmm0 . As Jester points out in comments, the best option to emulate the non-existent psrlb and psllb is to use a wider shift and then mask off any bits that crossed element boundaries. e.g. psrlw xmm0, 2 ; doesn't matter what size (w/d/q): performance is the same for all sizes on all CPUs pand xmm0, [mask_right2] section .rodata align 16 ;; required mask