sse4

Make a Dockerfile that compiles a Tensorflow binary to use: SSE4.1, SSE4.2 and AVX instructions

由 大憨熊 提交于 2019-12-12 08:41:34
问题 So, one of the porpuses of docker is to easily deploy an environment to test software right? Can anybody tell me how to compile a Tensorflow binary to use: SSE4.1, SSE4.2 on a docker file?. Can anybody point me to a docker file that does that? if it is possible at all? In summary, two questions: Is it possible to have a docker file that compiles a Tensorflow binary to use: SSE4.1, SSE4.2 (and GPU, I have only found one or the other) Can you tell me where I can found a docker file that does

SSE multiplication 16 x uint8_t

由 帅比萌擦擦* 提交于 2019-12-09 04:55:09
问题 I want to multiply with SSE4 a __m128i object with 16 unsigned 8 bit integers, but I could only find an intrinsic for multiplying 16 bit integers. Is there nothing such as _mm_mult_epi8 ? 回答1: There is no 8-bit multiplication in MMX/SSE/AVX. However, you can emulate 8-bit multiplication intrinsic using 16-bit multiplication as follows: inline __m128i _mm_mullo_epi8(__m128i a, __m128i b) { __m128i zero = _mm_setzero_si128(); __m128i Alo = _mm_cvtepu8_epi16(a); __m128i Ahi = _mm_unpackhi_epi8(a

Does .NET Framework 4.5 provide SSE4/AVX support?

由 …衆ロ難τιáo~ 提交于 2019-12-07 06:30:35
问题 I think, I heard about that, but don't know where. upd: I told about JiT 回答1: it seem that it is coming. (I just found out an hour ago) here a few links The JIT finally proposed. JIT and SIMD are getting married. Update to SIMD Support you need the latest version of RyuJIT and Microsoft SIMD-enabled Vector Types (Nuget) 回答2: No, there's no scenario in .NET where you can write machine code yourself. Code generation is entirely up to the just-in-time compiler. It is certainly capable of

What is the fastest way to do a SIMD gather without AVX(2)?

由 我的未来我决定 提交于 2019-12-07 03:11:03
问题 Assuming I have SSE to SSE4.1, but not AVX(2), what is the fastest way to load a packed memory layout like this (all 32-bit integers): a0 b0 c0 d0 a1 b1 c1 d1 a2 b2 c2 d2 a3 b3 c3 d3 Into four vectors a, b, c, d ? a: {a0, a1, a2, a3} b: {b0, b1, b2, b3} c: {c0, c1, c2, c3} d: {d0, d1, d2, d3} I'm not sure whether this is relevant or not, but in my actual application I have 16 vectors and as such a0 and a1 are 16*4 bytes apart in memory. 回答1: What you need here is 4 loads followed by a 4x4

Does .NET Framework 4.5 provide SSE4/AVX support?

由 纵然是瞬间 提交于 2019-12-05 12:41:10
I think, I heard about that, but don't know where. upd: I told about JiT it seem that it is coming. (I just found out an hour ago) here a few links The JIT finally proposed. JIT and SIMD are getting married. Update to SIMD Support you need the latest version of RyuJIT and Microsoft SIMD-enabled Vector Types (Nuget) No, there's no scenario in .NET where you can write machine code yourself. Code generation is entirely up to the just-in-time compiler. It is certainly capable of customizing its code generation based on the capabilities of the machine's processor. One of the big reasons why ngen

What is the fastest way to do a SIMD gather without AVX(2)?

由 ℡╲_俬逩灬. 提交于 2019-12-05 08:14:27
Assuming I have SSE to SSE4.1, but not AVX(2), what is the fastest way to load a packed memory layout like this (all 32-bit integers): a0 b0 c0 d0 a1 b1 c1 d1 a2 b2 c2 d2 a3 b3 c3 d3 Into four vectors a, b, c, d ? a: {a0, a1, a2, a3} b: {b0, b1, b2, b3} c: {c0, c1, c2, c3} d: {d0, d1, d2, d3} I'm not sure whether this is relevant or not, but in my actual application I have 16 vectors and as such a0 and a1 are 16*4 bytes apart in memory. What you need here is 4 loads followed by a 4x4 transpose: #include "emmintrin.h" // SSE2 v0 = _mm_load_si128((__m128i *)&a[0]); // v0 = a0 b0 c0 d0 v1 = _mm

Optimal SSE unsigned 8 bit compare

由 こ雲淡風輕ζ 提交于 2019-12-04 11:50:32
问题 I'm trying to find the most way of performing 8 bit unsigned compares using SSE (up to SSE 4.2). The most common case I'm working on is comparing for > 0U, e.g. _mm_cmpgt_epu8(v, _mm_setzero_si128()) // #1 (which of course can also be considered to be a simple test for non-zero.) But I'm also somewhat interested in the more general case, e.g. _mm_cmpgt_epu8(v1, v2) // #2 The first case can be implemented with 2 instructions, using various different methods, e.g. compare with 0 and then invert

_mm_testc_ps and _mm_testc_pd vs _mm_testc_si128

由 喜你入骨 提交于 2019-12-04 05:29:08
问题 As you know, the first two are AVX-specific intrinsics and the second is a SSE4.1 intrinsic. Both sets of intrinsics can be used to check for equality of 2 floating-point vectors. My specific use case is: _mm_cmpeq_ps or _mm_cmpeq_pd , followed by _mm_testc_ps or _mm_testc_pd on the result, with an appropriate mask But AVX provides equivalents for "legacy" intrinsics, so I might be able to use _mm_testc_si128 , after a cast of the result to __m128i . My questions are, which of the two use

Optimal SSE unsigned 8 bit compare

由 风流意气都作罢 提交于 2019-12-03 07:18:17
I'm trying to find the most way of performing 8 bit unsigned compares using SSE (up to SSE 4.2). The most common case I'm working on is comparing for > 0U, e.g. _mm_cmpgt_epu8(v, _mm_setzero_si128()) // #1 (which of course can also be considered to be a simple test for non-zero.) But I'm also somewhat interested in the more general case, e.g. _mm_cmpgt_epu8(v1, v2) // #2 The first case can be implemented with 2 instructions, using various different methods, e.g. compare with 0 and then invert the result. The second case typically requires 3 instructions, e.g. subtract 128 from both operands

SSE multiplication 16 x uint8_t

由 余生颓废 提交于 2019-12-03 02:42:31
I want to multiply with SSE4 a __m128i object with 16 unsigned 8 bit integers, but I could only find an intrinsic for multiplying 16 bit integers. Is there nothing such as _mm_mult_epi8 ? Marat Dukhan There is no 8-bit multiplication in MMX/SSE/AVX. However, you can emulate 8-bit multiplication intrinsic using 16-bit multiplication as follows: inline __m128i _mm_mullo_epi8(__m128i a, __m128i b) { __m128i zero = _mm_setzero_si128(); __m128i Alo = _mm_cvtepu8_epi16(a); __m128i Ahi = _mm_unpackhi_epi8(a, zero); __m128i Blo = _mm_cvtepu8_epi16(b); __m128i Bhi = _mm_unpackhi_epi8(b, zero); __m128i
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