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https://github.com/m4rs-mt/ilgpu
ILGPU JIT Compiler for high-performance .Net GPU programs
https://github.com/m4rs-mt/ilgpu
amd cil compiler cpu cuda dotnet gpgpu gpgpu-computing gpu ilgpu intel jit kernels msil nvidia opencl parallel ptx
Last synced: 6 days ago
JSON representation
ILGPU JIT Compiler for high-performance .Net GPU programs
- Host: GitHub
- URL: https://github.com/m4rs-mt/ilgpu
- Owner: m4rs-mt
- License: other
- Created: 2017-01-08T16:49:11.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2024-09-06T04:50:47.000Z (2 months ago)
- Last Synced: 2024-09-06T05:59:01.019Z (2 months ago)
- Topics: amd, cil, compiler, cpu, cuda, dotnet, gpgpu, gpgpu-computing, gpu, ilgpu, intel, jit, kernels, msil, nvidia, opencl, parallel, ptx
- Language: C#
- Homepage: http://www.ilgpu.net
- Size: 11.1 MB
- Stars: 1,343
- Watchers: 35
- Forks: 115
- Open Issues: 37
-
Metadata Files:
- Readme: Docs/README.md
- License: LICENSE-3RD-PARTY.txt
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
# ILGPU Tutorials
## Primers (How a GPU works)
This series introduces how a GPU works and what ILGPU does. If you have programmed with CUDA or OpenCL
before you can probably skip 01 and 02.
00 [Setting up ILGPU](01_Primers/01_Setting-Up-ILGPU.md) (ILGPU version 1.0.0)
01 [A GPU is not a CPU](01_Primers/01_Setting-Up-ILGPU.md) (ILGPU version 1.0.0)
> This page will provide a quick rundown the basics of how kernels (think GPU programs) run.
02 [Memory and bandwidth and threads. Oh my!](01_Primers/02_A-GPU-Is-Not-A-CPU.md)
> This will hopefully give you a better understanding of how memory works in hardware and the performance
> implications.
## Beginner (How ILGPU works)
This series is meant to be a brief overview of ILGPU and how to use it. It assumes you have at least a little knowledge
of how Cuda or OpenCL work.
If you need a primer look to something like [this for Cuda](https://developer.nvidia.com/about-cuda)
or [this for OpenCL](https://www.khronos.org/opencl/)
01 [Context and Accelerators](02_Beginner/01_Context-and-Accelerators.md)
> This tutorial covers creating the Context and Accelerator objects which setup ILGPU for use.
> It's mostly boiler plate and does no computation but it does print info about your GPU if you have one.
> There is some advice about ILGPU in here that makes it worth the quick read.
>
> See Also:
>
> [Device Info Sample](https://github.com/m4rs-mt/ILGPU/tree/master/Samples/DeviceInfo)
02 [MemoryBuffers and ArrayViews](02_Beginner/02_MemoryBuffers-and-ArrayViews.md)
> This tutorial covers the basics for Host / Device memory management.
>
> See Also:
>
> [Simple Allocation Sample](https://github.com/m4rs-mt/ILGPU/tree/master/Samples/SimpleAlloc)
03 [Kernels and Simple Programs](02_Beginner/03_Kernels-and-Simple-Programs.md)
> This is where it all comes together. This covers actual code, on the actual GPU (or the CPU if you are testing / dont
> have a GPU).
>
> See Also:
>
> [Simple Kernel Sample](https://github.com/m4rs-mt/ILGPU/tree/master/Samples/SimpleKernel)
>
> [Simple Math Sample](https://github.com/m4rs-mt/ILGPU/tree/master/Samples/SimpleMath)
04 [Structs and the N-body problem](02_Beginner/04_Structs.md)
> This tutorial actually does something! We use computing the N-body problem as a sample of how to better manage Host /
> Device memory.
## Beginner II (Something more interesting)
Well at least I think. This is where I will put ILGPUView bitmap shader things I (or other people if they want to)
eventually write. Below are the few I have planned / think would be easy.
1. Ray Tracing in One Weekend based raytracer
2. Cloud Simulation
3. 2D Physics Simulation
4. Other things I see on shadertoy
# Advanced Resources
## Samples
They cover a wide swath of uses for ILGPU including much of the more complex things that ILGPU is capable of.
[There are too many to list out so I will just link to the repository.](https://github.com/m4rs-mt/ILGPU/tree/master/Samples)
## Overview
[Memory Buffers & Views](03_Advanced/01_Memory-Buffers-and-Views.md)
[Kernels](03_Advanced/02_Kernels.md)
[Shared Memory](03_Advanced/03_Shared-Memory.md)
[Math Functions](03_Advanced/04_Math-Functions.md)
[Dynamically Specialized Kernels](03_Advanced/05_Dynamically-Specialized-Kernels.md)
[Debugging & Profiling](03_Advanced/06_Debugging-and-Profiling.md)
[Inside ILGPU](03_Advanced/07_Inside-ILGPU.md)
## Upgrade Guides
[Upgrade v0.1.X to v0.2.X](04_Upgrade-Guides/06_v0.1.X-to-v0.2.X.md)
[Upgrade v0.3.X to v0.5.X](04_Upgrade-Guides/05_v0.3.X-to-v0.5.X.md)
[Upgrade v0.6.X to v0.7.X](04_Upgrade-Guides/04_v0.6.X-to-v0.7.X.md)
[Upgrade v0.7.X to v0.8.X](04_Upgrade-Guides/03_v0.7.X-to-v0.8.X.md)
[Upgrade v0.8.0 to v0.8.1](04_Upgrade-Guides/02_v0.8.0-to-v0.8.1.md)
[Upgrade v0.8.X to v0.9.X](04_Upgrade-Guides/01_v0.8.X-to-v0.9.X.md)