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MathSharp

Note: As of 2022 MathSharp is not recommended for general use

Since its inception, the .NET intrinsics and vectors have increased in performance and functionality massively, and they are recommended for any new projects

Configuration	Windows x86	Windows x64	Ubuntu 1604 x64	Mac OS x64
Debug
Release

MathSharp is a vector and matrix library written in C# using hardware intrinsics. Thanks to hardware acceleration, MathSharp is significantly faster than most mathematics libraries out there, but only supports .NET Core 3.0 and up.

MathSharp beats out all alternative for speed. Comparing it to the System.Numerics types and the OpenTk.Math types shows just how fast it is:

[All benchmarks were taken using BenchmarkDotNet on an i3-8350k at stock speeds with minimal background activity, with .NET Core 3.0.100]

Matrix Equality

Operation

Matrix4x4 == Matrix4x4

Results

<img src="assets/Benchmarks/MatrixEqualityBenchmark-barplot.png" width="500" height="500">

Method	Mean	Error	StdDev
OpenTk	10.984 ns	0.1087 ns	0.1017 ns
SystemNumerics	3.895 ns	0.0185 ns	0.0164 ns
MathSharp	1.622 ns	0.0109 ns	0.0102 ns

Matrix Transposition

Operation

Transpose(Matrix4x4)

Results

<img src="assets/Benchmarks/MatrixTransposeBenchmark-barplot.png" width="500" height="500">

Method	Mean	Error	StdDev
OpenTk	10.984 ns	0.1087 ns	0.1017 ns
SystemNumerics	3.895 ns	0.0185 ns	0.0164 ns
MathSharp	1.622 ns	0.0109 ns	0.0102 ns

Sine Wave Generation

Operation

const int SampleRate = 44100;
const float Tau = MathF.PI * 2;
const float Frequency = 440;

for (var i = 0; i < waveInputs.Length; i++)
{
    waveInputs[i] = Sin(Tau * Frequency * ((float)i / SampleRate));
}

Results

<img src="assets/Benchmarks/SineWaveBenchmark-barplot.png" width="500" height="500">

Method	Mean	Error	StdDev
SystemMathF	412.30 us	1.6319 us	1.5265 us
MathSharp	80.37 us	0.6465 us	0.6048 us

Anchored Scale Operation

Operation

const Vector2 Translation = new Vector2(1.7f, 2.3f);
const Vector2 Anchor = new Vector2(1.0f, 0.0f);
const Vector2 Scale = new Vector2(7.0f, 3.6f);
const Vector2 Amount = new Vector2(0.5f, 0.25f);

Vector2 newScale = Scale * Amount;
Vector2 deltaT = Scale * (1 - Amount);
deltaT *= Anchor;
(Translation + deltaT) * newScale;

Results

<img src="assets/Benchmarks/AnchoredScaleBenchmark-barplot.png" width="500" height="500">

Method	Mean	Error	StdDev	Rank
MathSharp	0.8542 ns	0.0084 ns	0.0079 ns	1
SystemNumerics	2.0281 ns	0.0123 ns	0.0115 ns	2
OpenTk	37.4250 ns	0.1585 ns	0.1483 ns	3

Vector addition

Operation

Vector4 + Vector4;

Results (within margin of error between MathSharp and System.Numerics)

<img src="assets/Benchmarks/VectorAdditionBenchmark-barplot.png" width="500" height="500">

Method	Mean	Error	StdDev
OpenTk	6.5341 ns	0.0392 ns	0.0367 ns
SystemNumerics	0.0510 ns	0.0080 ns	0.0075 ns
MathSharp	0.0426 ns	0.0043 ns	0.0040 ns

Authors

MathSharp is a library written, managed, and maintained by John Kelly (@john-h-k)

How to use

TODO

Contributing

MathSharp uses and encourages Early Pull Requests. Please don't wait until you're done to open a PR!

Install Git and the .Net Core SDK
Fork MathSharp
Create a branch on your fork.
Add an empty commit to start your work off (and let you open a PR): git commit --allow-empty -m "start of [thing you're working on]"
Open a Pull request with [WIP] in the title. Do this before you actually start working.
Make your commits in small, incremental steps with clear descriptions.
Tag a maintainer when you're done and ask for a review!

Click here for good first issues.

Click here for everything we need help with.

License & Copyright

MathSharp is licensed under the MIT license. Full copyright belongs to @john-h-k.