https://github.com/mhutch/Mono.GameMath

Optimized math for games, based on XNA APIs
https://github.com/mhutch/Mono.GameMath

Last synced: 3 months ago
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Optimized math for games, based on XNA APIs

• Host: GitHub
• URL: https://github.com/mhutch/Mono.GameMath
• Owner: mhutch
• Created: 2011-01-03T23:24:25.000Z (about 14 years ago)
• Default Branch: main
• Last Pushed: 2021-03-04T19:12:47.000Z (almost 4 years ago)
• Last Synced: 2024-08-01T03:16:42.627Z (6 months ago)
• Language: C#
• Homepage:
• Size: 108 KB
• Stars: 100
• Watchers: 3
• Forks: 15
• Open Issues: 4
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-monogame - Mono.GameMath -  - Optimized math for games, based on XNA APIs. (Helpers)

README

        
Fast Managed Math for Games

===========================

Mono.GameMath is a project to develop a highly-performant math library

for games.

There will be three components:

*  the math library

*  a benchmark suite

*  an IL manipulation tool

The Library

-----------

The math library API is initially based on the XNA math API, in order to 

make it easy to port code, however it will likely be expanded. Non-math 

algorithms used in games may also be added if contributed.

The library will have the following ifdefed versions:

*  "safe" - as a baseline and for sandboxed platforms

*  unsafe - for architectures without SIMD support

*  unsafe + Mono.Simd

Architecture-specific versions tuned for JIT & SIMD behaviour

may be added later if benchmarks indicate this is necessary.

The Benchmark Suite

-------------------

The benchmark suite will consist of micro-benchmarks to measure 

individual functions, and macro-benchmarks to test the interaction of 

multiple functions. The purpose of the benchmark suite is to ensure 

that optimizations actually improve the performance, and to be able

to evaluate performance on different runtimes and CPU architectures

where JIT behaviour may differ.

The benchmark runner will be able to run the benchmarks against 

different versions of the library with different optimizations.

It will also be able to compare results against recorded baselines.

The IL Tool

-----------

The IL manipulation tool will be used to create differently tuned

versions of the library, and to optimize consuming code, based on

by attributes in the code. It may inline functions, rewrite pass-

by-value function calls to use pass-by-ref overloads (especially

for overloaded operators), and perhaps other optimizations such as 

profile-guided optimization (it would be great if we could use

attributes to annotate things for the JIT to inline or to heavily 

optimize).