sse

问题 How can I round a __m128 vector of floats up/down or to the nearest integer, like these functions? Round - roundf() Ceil - ceilf() or SSE4.1 _mm_ceil_ps . Floor - floorf() or SSE4.1 _mm_floor_ps . I need to do this without SSE4.1 roundps ( _mm_floor_ps / _mm_ceil_ps / _mm_round_ps(x, _MM_FROUND_TO_NEAREST_INT |_MM_FROUND_NO_EXC) . roundps can also truncate toward zero, but I don't need that for this application. I can use SSE3 and earlier. (No SSSE3 or SSE4) So the function declaration would

问题 How can I round a __m128 vector of floats up/down or to the nearest integer, like these functions? Round - roundf() Ceil - ceilf() or SSE4.1 _mm_ceil_ps . Floor - floorf() or SSE4.1 _mm_floor_ps . I need to do this without SSE4.1 roundps ( _mm_floor_ps / _mm_ceil_ps / _mm_round_ps(x, _MM_FROUND_TO_NEAREST_INT |_MM_FROUND_NO_EXC) . roundps can also truncate toward zero, but I don't need that for this application. I can use SSE3 and earlier. (No SSSE3 or SSE4) So the function declaration would

问题 How can I round a __m128 vector of floats up/down or to the nearest integer, like these functions? Round - roundf() Ceil - ceilf() or SSE4.1 _mm_ceil_ps . Floor - floorf() or SSE4.1 _mm_floor_ps . I need to do this without SSE4.1 roundps ( _mm_floor_ps / _mm_ceil_ps / _mm_round_ps(x, _MM_FROUND_TO_NEAREST_INT |_MM_FROUND_NO_EXC) . roundps can also truncate toward zero, but I don't need that for this application. I can use SSE3 and earlier. (No SSSE3 or SSE4) So the function declaration would

问题 How can I round a __m128 vector of floats up/down or to the nearest integer, like these functions? Round - roundf() Ceil - ceilf() or SSE4.1 _mm_ceil_ps . Floor - floorf() or SSE4.1 _mm_floor_ps . I need to do this without SSE4.1 roundps ( _mm_floor_ps / _mm_ceil_ps / _mm_round_ps(x, _MM_FROUND_TO_NEAREST_INT |_MM_FROUND_NO_EXC) . roundps can also truncate toward zero, but I don't need that for this application. I can use SSE3 and earlier. (No SSSE3 or SSE4) So the function declaration would

问题 If I have 2 denormal floating point numbers with different bit patterns and compare them for equality, can the result be affected by the Denormals-Are-Zero flag, the Flush-to-Zero flag, or other flags on commonly used processors? Or do these flags only affect computation and not equality checks? 回答1: DAZ (Denormals Are Zero) affects reading input, so DAZ affects compares . All denormals are literally treated as -0.0 or +0.0 , according to their sign. FTZ (Flush To Zero) affects only writing

问题 If I have 2 denormal floating point numbers with different bit patterns and compare them for equality, can the result be affected by the Denormals-Are-Zero flag, the Flush-to-Zero flag, or other flags on commonly used processors? Or do these flags only affect computation and not equality checks? 回答1: DAZ (Denormals Are Zero) affects reading input, so DAZ affects compares . All denormals are literally treated as -0.0 or +0.0 , according to their sign. FTZ (Flush To Zero) affects only writing

问题 I am trying to read and write values from registers in python using the LLDB API. For the General Purpose Registers, I have been using the frame.register['register name'].value to read and write register values, which works successfully for me. However, as I approach the Floating Point Registers, I found that this could not be done anymore, as some of the registers, such as the XMM registers do not have a value attribute e.g frame.register['xmm0'].value would return None . I have looked into

问题 I have disassembled a small C++ program compiled with MSVC v140 and am trying to estimate the cycles per instruction in order to better understand how code design impacts performance. I've been following Mike Acton's CppCon 2014 talk on "Data-Oriented Design and C++", specifically the portion I've linked to. In it, he points out these lines: movss 8(%rbx), %xmm1 movss 12(%rbx), %xmm0 He then claims that these 2 x 32-bit reads are probably on the same cache line therefore cost roughly ~200

问题 I have disassembled a small C++ program compiled with MSVC v140 and am trying to estimate the cycles per instruction in order to better understand how code design impacts performance. I've been following Mike Acton's CppCon 2014 talk on "Data-Oriented Design and C++", specifically the portion I've linked to. In it, he points out these lines: movss 8(%rbx), %xmm1 movss 12(%rbx), %xmm0 He then claims that these 2 x 32-bit reads are probably on the same cache line therefore cost roughly ~200

问题 I have a bunch of packed floats inside an XMM register (using SSE intrinsics): __m128 xmm = _mm_set_ps(4.0f, 3.0f, 2.0f, 1.0f); I'd like to convert all of these to integers in one go. I found an intrinsic, that does what I want ( _mm_cvtps_pi16() ), but it yields 4x16-bit short instead of full-blown int . An intrinsic called _mm_cvtps_pi32() yields int , but only for the two lower values in xmm . I can use it, extract the values, move things around and use it again, but is there a simpler way