sse

问题 I am looking for efficient AVX (AVX512) implementation of // Given float u[8]; float v[8]; // Compute float a[8]; float b[8]; // Such that for ( int i = 0; i < 8; ++i ) { a[i] = fabs(u[i]) >= fabs(v[i]) ? u[i] : v[i]; b[i] = fabs(u[i]) < fabs(v[i]) ? u[i] : v[i]; } I.e., I need to select element-wise into a from u and v based on mask , and into b based on !mask , where mask = (fabs(u) >= fabs(v)) element-wise. 回答1: I had this exact same problem just the other day. The solution I came up with

问题 I am looking for efficient AVX (AVX512) implementation of // Given float u[8]; float v[8]; // Compute float a[8]; float b[8]; // Such that for ( int i = 0; i < 8; ++i ) { a[i] = fabs(u[i]) >= fabs(v[i]) ? u[i] : v[i]; b[i] = fabs(u[i]) < fabs(v[i]) ? u[i] : v[i]; } I.e., I need to select element-wise into a from u and v based on mask , and into b based on !mask , where mask = (fabs(u) >= fabs(v)) element-wise. 回答1: I had this exact same problem just the other day. The solution I came up with

问题 This question is very similar to: SIMD instructions for floating point equality comparison (with NaN == NaN) Although that question focused on 128 bit vectors and had requirements about identifying +0 and -0. I had a feeling I might be able to get this one myself but the intel intrinsics guide page seems to be down :/ My goal is to take an array of doubles and to return whether a NaN is present in the array. I am expecting that the majority of the time that there won't be one, and would like

问题 I had a mysterious bus error that occurred, on a x86 (32-bit) platform, when running code compiled with gcc-4.8.1 with -march=pentium4 . I traced the problem to an SSE instruction: movdqa %xmm5,0x50(%esp) with esp = 0xbfffedac. movdqa requires the address to be 16-byte aligned, which is not the case here, thus the bus error. The problem does not occur if compiling with -march=native (this is a Core-i3 processor). As far as I know, the only stack alignment guaranteed on Linux/x86 is 4-byte.

问题 I current run BOINC across a number of servers which have GPUs. The servers run both GPU and CPU BOINC apps. As AVX and SSE slow down the CPU freq when being used within a CPU app, I have to be selective which CPU/GPU I run together, as some GPU apps get bottle necked (slower run time completion) where as others do not. At present some CPU apps are named so it is clear to see if they use AVX but most are not. Therefore is there any command I can run, and some way of viewing, to see if any of

问题 1) Is there a way to efficiently implement sign function using SSE3 (no SSE4) with the following characteristics? the input is a float vector __m128 . the output should be also __m128 with [-1.0f, 0.0f, 1.0f] as its values I tried this, but it didn't work (though I think it should): inputVal = _mm_set_ps(-0.5, 0.5, 0.0, 3.0); comp1 = _mm_cmpgt_ps(_mm_setzero_ps(), inputVal); comp2 = _mm_cmpgt_ps(inputVal, _mm_setzero_ps()); comp1 = _mm_castsi128_ps(_mm_castps_si128(comp1)); comp2 = _mm

问题 I want sum up all elements of a big vector ary . My idea was to do it with a horizontal sum. const int simd_width = 16/sizeof(float); float helper[simd_width]; //take the first 4 elements const __m128 a4 = _mm_load_ps(ary); for(int i=0; i<N-simd_width; i+=simd_width){ const __m128 b4 = _mm_load_ps(ary+i+simd_width); //save temporary result in helper array _mm_store_ps(helper, _mm_hadd_ps(a4,b4)); //C const __m128 a4 = _mm_load_ps(helper); } I looked for a method, with which i can assign the

问题 I want sum up all elements of a big vector ary . My idea was to do it with a horizontal sum. const int simd_width = 16/sizeof(float); float helper[simd_width]; //take the first 4 elements const __m128 a4 = _mm_load_ps(ary); for(int i=0; i<N-simd_width; i+=simd_width){ const __m128 b4 = _mm_load_ps(ary+i+simd_width); //save temporary result in helper array _mm_store_ps(helper, _mm_hadd_ps(a4,b4)); //C const __m128 a4 = _mm_load_ps(helper); } I looked for a method, with which i can assign the

问题 There are two ways of implementation of accumulation in sse intrinsic. But one of them gets the wrong result. #include <smmintrin.h> int main(int argc, const char * argv[]) { int32_t A[4] = {10, 20, 30, 40}; int32_t B[8] = {-1, 2, -3, -4, -5, -6, -7, -8}; int32_t C[4] = {0, 0, 0, 0}; int32_t D[4] = {0, 0, 0, 0}; __m128i lv = _mm_load_si128((__m128i *)A); __m128i rv = _mm_load_si128((__m128i *)B); // way 1 unexpected rv += lv; _mm_store_si128((__m128i *)C, rv); // way 2 expected rv = _mm_load

问题 There are two ways of implementation of accumulation in sse intrinsic. But one of them gets the wrong result. #include <smmintrin.h> int main(int argc, const char * argv[]) { int32_t A[4] = {10, 20, 30, 40}; int32_t B[8] = {-1, 2, -3, -4, -5, -6, -7, -8}; int32_t C[4] = {0, 0, 0, 0}; int32_t D[4] = {0, 0, 0, 0}; __m128i lv = _mm_load_si128((__m128i *)A); __m128i rv = _mm_load_si128((__m128i *)B); // way 1 unexpected rv += lv; _mm_store_si128((__m128i *)C, rv); // way 2 expected rv = _mm_load