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

All supported intrinsics here.

SIMD intrinsics with _mm_ prefix

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

The intrinsics implemented follow the syntax and semantics at:

• https://www.intel.com/content/www/us/en/docs/intrinsics-guide/index.htm
• https://www.officedaytime.com/simd512e/

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

__m128i A;

// set a single SIMD element (here, in an int4)
A[0] = 42; 

// get a single SIMD element (here, in an int4)
int elem = A[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://www.intel.com/content/www/us/en/docs/intrinsics-guide/ Without that Intel documentation, it's impractical to write sizeable SIMD code.

Recommended for maximum reach on consumer machines

If you'd like to distribute software to consumers, it's safest to target SSE3 with dflags: ["-mattr=+sse3"].

• Apple Rosetta support up to AVX2.
• Microsoft Prism supports up to SSE4.2.

Hence it's reach-limiting for consumer target to target above SSE4.2.

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