SimpleSIMD 3.3.0

Suggested Alternatives

SimpleSIMD 3.3.1

Additional Details

Major bug in subtraction operator.

There is a newer version of this package available.
See the version list below for details.
dotnet add package SimpleSIMD --version 3.3.0                
NuGet\Install-Package SimpleSIMD -Version 3.3.0                
This command is intended to be used within the Package Manager Console in Visual Studio, as it uses the NuGet module's version of Install-Package.
<PackageReference Include="SimpleSIMD" Version="3.3.0" />                
For projects that support PackageReference, copy this XML node into the project file to reference the package.
paket add SimpleSIMD --version 3.3.0                
#r "nuget: SimpleSIMD, 3.3.0"                
#r directive can be used in F# Interactive and Polyglot Notebooks. Copy this into the interactive tool or source code of the script to reference the package.
// Install SimpleSIMD as a Cake Addin
#addin nuget:?package=SimpleSIMD&version=3.3.0

// Install SimpleSIMD as a Cake Tool
#tool nuget:?package=SimpleSIMD&version=3.3.0                

SimpleSIMD

NuGet version (SimpleSIMD)

What is SIMD?

Single Instruction, Multiple Data (SIMD) units refer to hardware components that perform the same operation on multiple data operands concurrently. The concurrency is performed on a single thread, while utilizing the full size of the processor register to perform several operations at one.
This approach could be combined with standard multithreading for massive performence boosts in numeric computations.

Goals And Purpose

  • Single API to unify SIMD for All supported types
  • Gain performence boost for mathematical computations using a simple API
  • Simplifies SIMD usage, and to make it easy to integrate it into an already existing solutions
  • Helps generalize several methemathical functions for supported types
  • Performs less allocations compared to standard LINQ implementations

Available Functions

Comparison:
  • Equal
  • Greater
  • GreaterOrEqual
  • Less
  • LessOrEqual
Elementwise:
  • Negate
  • Abs
  • Add
  • Divide
  • Multiply
  • Subtract
  • And
  • Or
  • Xor
  • Not
  • Select
  • Ternary (Conditional Select)
  • Concat
  • Sqrt
Reduction:
  • Aggregate
  • Sum
  • Average
  • Max
  • Min
  • Dot
General Purpose:
  • All
  • Any
  • Contains
  • IndexOf
  • Fill
  • Foreach

Auto-Generated Functions

For any of the Elementwise functions, an auto-generated overload is generated, which doesn't accept Span<T> result, and instead creates T[] internally and returns the result within this array.

For any of the functions with the Value Delagate pattern, an auto-generated overload is generated, which accepts regular delegates. Note that using this overload results in performence losses. Check Value Delegates - Benchmark section for more info.

Performance Benefits

A simple benchmark to demonstrate performance gains of using SIMD.
Benchmarked method was a Sum over an int[].

Method Length Mean Error StdDev Ratio
LINQ 10 58.428 ns 1.1658 ns 1.4743 ns 9.65
Naive 10 6.138 ns 0.1226 ns 0.1087 ns 1.00
SIMD 10 5.739 ns 0.1397 ns 0.1372 ns 0.93
LINQ 100 475.290 ns 9.3530 ns 17.7951 ns 7.36
Naive 100 65.447 ns 0.8545 ns 0.7575 ns 1.00
SIMD 100 12.879 ns 0.2039 ns 0.1592 ns 0.20
LINQ 1000 4,620.020 ns 80.4166 ns 71.2872 ns 7.47
Naive 1000 617.992 ns 7.6832 ns 7.1869 ns 1.00
SIMD 1000 78.865 ns 0.7991 ns 0.6673 ns 0.13
LINQ 10000 43,103.800 ns 700.6532 ns 655.3915 ns 6.99
Naive 10000 6,164.725 ns 51.9217 ns 48.5676 ns 1.00
SIMD 10000 738.459 ns 14.7266 ns 32.3252 ns 0.13
LINQ 100000 393,739.178 ns 755.6571 ns 631.0079 ns 6.73
Naive 100000 58,510.310 ns 58.0928 ns 54.3400 ns 1.00
SIMD 100000 8,897.370 ns 102.2559 ns 95.6502 ns 0.15

Value Delegates

This library extensively uses the value delegate pattern. This pattern is used as a replacement for delegates.
Calling functions using this patten may feel unusual since it requires creation of structs to pass as arguments instead of delegates, but it is very beneficial performance-wise. The performance difference makes using this pattern worthwhile in performance critical places.
Since the focus of this library is pure performance, we use this pattern wherever possible.

Wrap extension methods are included (SimpleSimd.Wrapper.Wrap(delegate)) to wrap regular delegates as Value Delegates.
Note that wrapping a regular delegate results in a performance hit - prefer using Value Delegates directly as shown below.

Usage:
using System;
using System.Numerics;
using SimpleSimd;

namespace MyProgram
{
    class Program
    {
        static void Main()
        {
            // Creating the data
            // Can be int[], Span<int>, ReadOnlySpan<int>
            int[] Data = GetData()
            
            // We need to create 2 structs which will serve as a replacement for delegates
            SimdOps<int>.Sum(Data, new VecSelector(), new Selector());
        }
    }             
    
    // A struct which is used as Vector<int> selector
    // Inheritence from IFunc is according to Sum() signature
    struct VecSelector : IFunc<Vector<int>, Vector<int>>
    {
        public Vector<int> Invoke(Vector<int> param) => param * 2;
    }

    // A struct which is used as int selector
    // Inheritence from IFunc is according to Sum() signature
    struct Selector : IFunc<int, int>
    {
        public int Invoke(int param) => param * 2;
    }   
}
benchmark:

Both of the benchmarked methods have the exactly same code, both of them are accelerated using SIMD,
the only difference is the argument types.

// Delegate, baseline
public static T Sum(Span<T> span, Func<Vector<T>, Vector<T>> vSelector, Func<T, T> selector)

// ValueDelegate
public static T Sum<F1, F2>(in Span<T> span, F1 vSelector, F2 selector)
            where F1 : struct, IFunc<Vector<T>, Vector<T>>
            where F2 : struct, IFunc<T, T> 
Method Length Mean Error StdDev Ratio
Delegate 10 10.697 ns 0.0155 ns 0.0145 ns 1.00
ValueDelegate 10 5.069 ns 0.0206 ns 0.0182 ns 0.47
Delegate 100 40.812 ns 0.0977 ns 0.0913 ns 1.00
ValueDelegate 100 11.732 ns 0.0149 ns 0.0139 ns 0.29
Delegate 1000 302.164 ns 3.1291 ns 2.6130 ns 1.00
ValueDelegate 1000 66.808 ns 0.2692 ns 0.2518 ns 0.22
Delegate 10000 2,884.803 ns 8.9309 ns 7.4577 ns 1.00
ValueDelegate 10000 585.193 ns 0.8926 ns 0.6969 ns 0.20
Delegate 100000 28,920.414 ns 267.4154 ns 250.1406 ns 1.00
ValueDelegate 100000 8,519.340 ns 41.2833 ns 38.6164 ns 0.29
Delegate 1000000 304,228.749 ns 1,995.9951 ns 1,769.3976 ns 1.00
ValueDelegate 1000000 85,619.207 ns 316.5366 ns 280.6015 ns 0.28

Limitations

  • Methods are not lazily evaluated as IEnumerable
  • Old hardware might not support SIMD
  • Supported collection types:
    • T[] where T : unmanaged
    • Span<T> where T : unmanaged
    • ReadOnlySpan<T> where T : unmanaged
  • Supports only Primitive Numeric Types as array elements. Supported types are:
    • byte, sbyte
    • short, ushort
    • int, uint
    • long, ulong
    • float
    • double

Contributing

All ideas and suggestions are welcome. Feel free to open an issue if you have an idea or a suggestion that might improve this project. If you encounter a bug or have a feature request, please open a relevent issue.

License

This project is licensed under MIT license. For more info see the License File

Product Compatible and additional computed target framework versions.
.NET net5.0 is compatible.  net5.0-windows was computed.  net6.0 was computed.  net6.0-android was computed.  net6.0-ios was computed.  net6.0-maccatalyst was computed.  net6.0-macos was computed.  net6.0-tvos was computed.  net6.0-windows was computed.  net7.0 was computed.  net7.0-android was computed.  net7.0-ios was computed.  net7.0-maccatalyst was computed.  net7.0-macos was computed.  net7.0-tvos was computed.  net7.0-windows was computed.  net8.0 was computed.  net8.0-android was computed.  net8.0-browser was computed.  net8.0-ios was computed.  net8.0-maccatalyst was computed.  net8.0-macos was computed.  net8.0-tvos was computed.  net8.0-windows was computed. 
Compatible target framework(s)
Included target framework(s) (in package)
Learn more about Target Frameworks and .NET Standard.
  • net5.0

    • No dependencies.

NuGet packages (6)

Showing the top 5 NuGet packages that depend on SimpleSIMD:

Package Downloads
FaceAiSharp

FaceAiSharp allows you to work with face-related computer vision tasks easily. It currently provides face detection, face recognition, facial landmarks detection, and eye state detection functionalities. FaceAiSharp leverages publicly available pretrained ONNX models to deliver accurate and efficient results and offers a convenient way to integrate them into your .NET applications. Whether you need to find faces, recognize individuals, detect facial landmarks, or determine eye states, FaceAiSharp simplifies the process with its simple API. ONNXRuntime is used for model inference, enabling hardware acceleration were possible. All processing is done locally, with no reliance on cloud services. This package contains just FaceAiSharp's managed code and does not include any ONNX models. Take a look at FaceAiSharp.Bundle for a batteries-included package with everything you need to get started.

FaceAiSharp.Bundle

FaceAiSharp allows you to work with face-related computer vision tasks easily. It currently provides face detection, face recognition, facial landmarks detection, and eye state detection functionalities. FaceAiSharp leverages publicly available pretrained ONNX models to deliver accurate and efficient results and offers a convenient way to integrate them into your .NET applications. Whether you need to find faces, recognize individuals, detect facial landmarks, or determine eye states, FaceAiSharp simplifies the process with its simple API. ONNXRuntime is used for model inference, enabling hardware acceleration were possible. All processing is done locally, with no reliance on cloud services. This is a bundle package that installs FaceAiSharp's managed code and multiple AI models in the ONNX format.

STensor

SIMD-accelerated generic tensor library

PlatonAiPhoto

FaceAiSharp allows you to work with face-related computer vision tasks easily. It currently provides face detection, face recognition, facial landmarks detection, and eye state detection functionalities. FaceAiSharp leverages publicly available pretrained ONNX models to deliver accurate and efficient results and offers a convenient way to integrate them into your .NET applications. Whether you need to find faces, recognize individuals, detect facial landmarks, or determine eye states, FaceAiSharp simplifies the process with its simple API. ONNXRuntime is used for model inference, enabling hardware acceleration were possible. All processing is done locally, with no reliance on cloud services. This package contains just FaceAiSharp's managed code and does not include any ONNX models. Take a look at FaceAiSharp.Bundle for a batteries-included package with everything you need to get started.

PlatonAiPhoto.Bundle

FaceAiSharp allows you to work with face-related computer vision tasks easily. It currently provides face detection, face recognition, facial landmarks detection, and eye state detection functionalities. FaceAiSharp leverages publicly available pretrained ONNX models to deliver accurate and efficient results and offers a convenient way to integrate them into your .NET applications. Whether you need to find faces, recognize individuals, detect facial landmarks, or determine eye states, FaceAiSharp simplifies the process with its simple API. ONNXRuntime is used for model inference, enabling hardware acceleration were possible. All processing is done locally, with no reliance on cloud services. This is a bundle package that installs FaceAiSharp's managed code and multiple AI models in the ONNX format.

GitHub repositories

This package is not used by any popular GitHub repositories.

Added source generator to generate regular delegate overloads, for any method accepting value delegates.
       Removed wrappers as obsolete now - use generated methods instead.
       Added source generator to generate functions that return T[] as result for any method accepting a result Span.