🎹 Unified implementation of SIMD intrinsic functions and a fallback on hardware which doesn't natively support them.
Caveats:
Warning. Not meant to be used in production – created for learning purposes!
SIMDX provides a unified implementation for built-in vector and matrix
intrinsics, such as SSE/AVX on x86 and Neon on Arm, in C and exposes
them to Swift as generic types. Furthermore, SIMDX provides a fast and
portable implementation of SIMD like intrinsics on hardware that does not
natively support them or has a limited feature set, making SIMDX independent
of the target hardware – without additional external dependencies.
The raison d’être of the package is to provide a simple, unified and generic API
for vector and matrix types and operations that chooses the fastest calculation
path available to the target hardware automatically.
Further details on the C implementation can be found within the documentation
and source code of the CSIMDX module, while more details on the Swift
implementation can be found within the documentation and source of the SIMDX
module.
Given a color that should be modified in brightness on each channel
separately and then multiplied by a scale factor on each lanes equally.
Without SIMDX
Example 1.1:
// A color to be modify
let color: [Float] = [0.11, 0.2, 0.64, 1.0] // RGBA
// Add a modification value to each channel seperatly
let brightness: [Float] = [0.25, 0.3, -0.35, 0.0]
// Scale the resulting color on each channel equally
let scale: Float = 0.8
var newColor: [Float] = .init(repeating: 0.0, count: 4)
for index in 0...3 {
// operation on each element
newColor[index] = (color[index] + brightness[index]) * scale
}
print(newColor)
// [0.288, 0.4, 0,232, 1.0]
The SIMDX library allows to rewrite the example:
Example 1.2:
import SIMDX
// A color to be modify
let color: SIMDX4<Float> = [0.11, 0.2, 0.64, 1.0]
// Add a modification value to each channel seperatly
let brightness: SIMDX4<Float> = [0.25, 0.3, -0.35, 0.0]
// Scale the resulting color on each channel equally
let scale: Float = 0.8
// Do all operations on SIMD in parallel using SIMDX
let newColor = (color + brightness) * scale
print(newColor)
// [0.288, 0.4, 0,232, 1.0]
Example 1.2 does the same as example 1.1, but more efficiently because it utilises SIMD instructions that do four
additions and four multiplications in a single instruction. Today, modern CPU’s have these instructions which may give
you a throughput of four floating point additions and four multiplications per clock cycle. A good compiler may
actually convert example 1.1 automatically to use the SIMD instructions, but in more complicated cases you cannot be
sure that the compiler is able to vectorise your code in an optimal way.
The type SIMDX4<Float> in example 1.2 is a struct that encapsulates a 128-bit intrinsic type holding 4 floating point
numbers of 32 bits each. The operators + and * represent the SIMD instruction for adding and multiplying the
intrinsic types. These operators are inlined so that no extra code is generated other than the SIMD instructions. More
specifically, the type SIMDX4<Float> masks a __m128 intrinsic type on x86 with SSE2 or a float32x4_t intrinsic
type on Arm with Neon. If neither of both are available, the module instructs the compiler to optimise the vector code.
If this is not possible on the target hardware, the library provides a fallback to a C-array of float type and fixed
length, i.e. float array[4].
Numeric protocols functions (see upcoming blog post #link )| Int8 | UInt8 | Int16 | UInt16 | Float16 | Int32 | UInt32 | Float32 | Int64 | UInt64 | Float64 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 64 bit | |||||||||||
| 128 bit | |||||||||||
| 256 bit | |||||||||||
| 512 bit |
Move TODOs to Issues and/or a Project at some point
count on SIMDX staticinit(SIMDX) that uses native intrinsics storeNumeric protocols
I started with almost zero knowledge of SIMD/Intrinsics or builtin clang functions and was DuckDuckGoing (is that a
thing?) alot prior to started writing this lib. The following references contain some of the most useful instructions
I found across the internet. I gathered them while writing this library, and I am pretty sure I will need them and
re-visit them quite a lot so I leave them here.
The SIMDX library is licensed under the Apache License, version 2.0.
You may not use the files except in compliance with this License.
You may obtain a copy of the license at www.apache.org/licenses/LICENSE-2.0