intel-intrinsics

intel-intrinsics is the SIMD library for D.

intel-intrinsics lets you use SIMD in D with support for LDC / DMD / GDC with a single syntax and API: the x86 Intel Intrinsics API that is also used within the C, C++, and Rust communities.

intel-intrinsics is most similar to simd-everywhere, it can target AArch64 for full-speed with Apple Silicon without code change.

"dependencies":
{
    "intel-intrinsics": "~>1.0"
}

Features

SIMD intrinsics with _mm_ prefix

	DMD x86/x86_64	LDC x86/x86_64	LDC arm64	GDC x86_64
MMX	Yes but (#42)	Yes	Yes	Yes
SSE	Yes	Yes	Yes	Yes
SSE2	Yes but (#42)	Yes	Yes	Yes
SSE3	Yes but (#42)	Yes (-mattr=+sse3)	Yes	Yes (-msse3)
SSSE3	Yes (-mcpu)	Yes (-mattr=+ssse3)	Yes	Yes (-mssse3)
SSE4.1	Yes but (#42)	Yes (-mattr=+sse4.1)	Yes	Yes (-msse4.1)
SSE4.2	Yes but (#42)	Yes (-mattr=+sse4.2)	Yes (-mattr=+crc)	Yes (-msse4.2)
BMI2	Yes but (#42)	Yes (-mattr=+bmi2)	Yes	Yes (-mbmi2)
AVX	Yes but (#42)	Yes (-mattr=+avx)	Yes	Yes (-mavx)
F16C	WIP, (#42)	WIP (-mattr=+f16c)	WIP	WIP (-mf16c)
AVX2	WIP and (#42)	WIP (-mattr=+avx2)	WIP	WIP (-mavx2)

The intrinsics implemented follow the syntax and semantics at: https://software.intel.com/sites/landingpage/IntrinsicsGuide/

The philosophy (and guarantee) of intel-intrinsics is:

• intel-intrinsics generates optimal code else it's a bug.
• No promise that the exact instruction is generated, because it's often not the fastest thing to do.
• Guarantee that the semantics of the intrinsic is preserved, above all other consideration (even at the cost of speed). See image below.

SIMD types

intel-intrinsics define the following types whatever the compiler and target:

long1, int2, short4, byte8, float2,
long2, int4, short8, byte16, float4, double2
long4, int8, short16, byte32, float8, double4

though most of the time you will deal with:

alias __m128 = float4; 
alias __m128i = int4;
alias __m128d = double2;
alias __m64 = long1;
alias __m256 = float8; 
alias __m256i = long4;
alias __m256d = double4;

This type erasure of integers vectors is a defining point of the Intel API.

Vector Operators for all

intel-intrinsics implements Vector Operators for compilers that don't have __vector support (DMD with 32-bit x86 target, 256-bit vectors with GDC without -mavx...). It doesn't provide unsigned vectors though.

Example:

__m128 add_4x_floats(__m128 a, __m128 b)
{
    return a + b;
}

is the same as:

__m128 add_4x_floats(__m128 a, __m128 b)
{
    return _mm_add_ps(a, b);
}

See available operators...

One exception to this is int4 * int4. Older GDC and current DMD do not have this operator. Instead, do use _mm_mullo_epi32 from inteli.smmintrin module.

Individual element access

It is recommended to do it in that way for maximum portability:

__m128i A;

// recommended portable way to set a single SIMD element
A.ptr[0] = 42; 

// recommended portable way to get a single SIMD element
int elem = A.array[0];

Why intel-intrinsics?

• Portability It just works the same for DMD, LDC, and GDC. When using LDC, intel-intrinsics allows to target AArch64 and 32-bit ARM with the same semantics.

• Capabilities Some instructions just aren't accessible using core.simd and ldc.simd capabilities. For example: pmaddwd which is so important in digital video. Some instructions need an almost exact sequence of LLVM IR to get generated. ldc.intrinsics is a moving target and you need a layer on top of it.

• Familiarity Intel intrinsic syntax is more familiar to C and C++ programmers. The Intel intrinsics names aren't good, but they are known identifiers. The problem with introducing new names is that you need hundreds of new identifiers.

• Documentation There is a convenient online guide provided by Intel: https://software.intel.com/sites/landingpage/IntrinsicsGuide/ Without that Intel documentation, it's impractical to write sizeable SIMD code.

Who is using it?

• dg2d is a very fast 2D renderer, twice as fast as Cairo
• 18x faster SHA-256 vs Phobos with intel-intrinsics
• Auburn Sounds audio products
• Cut Through Recordings audio products
• PixelPerfectEngine
• SMAOLAB audio products

Notable differences between x86 and ARM targets

• AArch64 and 32-bit ARM respects floating-point rounding through MXCSR emulation. This works using FPCR as thread-local store for rounding mode.

  Some features of MXCSR are absent:

  • Getting floating-point exception status
  • Setting floating-point exception masks
  • Separate control for denormals-are-zero and flush-to-zero (ARM has one bit for both)

• 32-bit ARM has a different nearest rounding mode as compared to AArch64 and x86. Numbers with a 0.5 fractional part (such as -4.5) may not round in the same direction. This shouldn't affect you.

• Some ARM architecture do not represent the sign bit for NaN. Just writing -float.nan or -double.nan will loose the sign bit! This isn't related to intel-intrinsics.

Notable differences between x86 instruction semantics and intel-intrinsics semantics

• Masked load/store MUST address fully addressable memory, even if their mask is zero. Pad your buffers.
• Some AVX float comparisons have an option to signal quiet NaN. This is not followed by intel-intrinsics.

Video introduction

In this DConf 2019 talk, Auburn Sounds:

• introduces how intel-intrinsicscame to be,
• demonstrates a 3.5x speed-up for some particular loops,
• reminds that normal D code can be really fast and intrinsics might harm performance

See the talk: intel-intrinsics: Not intrinsically about intrinsics