A Modern GPU Compiler for .Net Programs

developed by Marcel Köster

ILGPU

A modern, lightweight & fast GPU compiler for high-performance .Net programs


ILGPU is a new JIT (just-in-time) compiler for high-performance GPU programs (also known as kernels) written in .Net-based languages. ILGPU is completely written in C# without any native dependencies. It combines the convenience of C++ AMP with the high performance of CUDA. Functions in the scope of kernels do not have to be annotated (e.g. default C# functions) and are allowed to work on value types. All kernels (including all hardware features like shared memory, atomics and warp shuffles) can be executed and debugged on the CPU using the integrated multi-threaded CPU accelerator. And the best feature: it's free! ILGPU is released under the University of Illinois/NCSA Open Source License.

ILGPU is a free and non-sponsored project. It is being developed by a professional and passionate compiler, GPU and computer graphics developer. Support the project with contributions or some small donations in order to speed up the development process and to keep the project alive.

ILGPU Nuget Package   ILGPU.Lightning Nuget Package   ILGPU.SharpDX Nuget Package

New ILGPU Version (v0.6.0) released on July 22nd

A new release of the ILGPU compiler is available.

It includes many performance improvements (in terms of ILGPU and kernel runtime) and bug fixes. It also enhances the support for shared memory and includes initial support for array types in all GPU kernels. In addition, the released test framework allows users and developers to verify generated kernel code. Refer to the documentation for more information.

New ILGPU Version (v0.5.1) released on March 24th

A new release of the ILGPU compiler is available.

It includes many enhancements and bug fixes. Furthermore, it allows you to control all internal caches. Refer to the documentation for more information.

New ILGPU Version (v0.5.0) released on February 2nd

A new release of the ILGPU compiler is available.

It features a redesigned IR in order to significantly improve compilation time and memory consumption. It also brings implicit stream launchers, sub-warp shuffles and basic support for source-line based GPU debugging and profiling. All samples, the class reference, documentation and upgrade guide have been updated.

Public ILGPU Beta Version (v0.5.0-beta) released on December 24th

A significantly improved version of the ILGPU compiler has been released.

Thank you for your feedback on version 0.4-beta. Based on hundreds of emails and several bug reports, a much improved version of the compiler was released in December. All samples, the class reference, documentation and upgrade guide have been updated. Merry Christmas to all of you.

New Public Discussion Group

A new public Discord server has been created.


I've just created a new Discord server to simplify communication: https://discord.gg/KTvqYZP. For general questions or feature requests, I recommend using the new Discord server.

New Public Discussion Group

A new public Google group has been created.


Due to the large number of emails I usually receive, I have decided to set up a public discussion group. For general questions or feature requests, I recommend using the public discussion forum.

High Performance

High performance kernel compilation, dispatch and execution times. Furthermore, type-safe kernel delegates avoid boxing.

High Convenience

Use the power of C# or VB.Net to write high-level kernels and execute them on the GPU. No need to program C++, Cuda or OpenCL.

CPU Accelerator

Single- or multi-threaded execution of kernels on the CPU. This is also useful for debugging or emulation of specific target platforms.

Advanced Debugging

High-level kernel debugging using your favorite .Net debugger. Furthermore, the single-threaded execution feature allows to focus on the algorithm instead of the parallelism.

No Function Annotations

Functions do not have to be annotated in order to use them in the scope of kernels.

Any-CPU Builds

Compile your applications for any cpu. ILGPU will automatically adjust everything else for X86 or X64 platforms.

Implicitly Grouped Kernels

Focus on the algorithm and not on the details. Implicitly grouped kernels let you implement high-level kernels without paying attention to low-level index computations or tiling.

Multi-dimensional Indices

Multi-dimensional index types simplify address computations and kernel writing.

Array Views

No pointer arithmetic and dramatically simplified index computations due to views to memory regions.

Shared Memory

Support for shared (scratch-pad) memory in kernels via array views. Static or dynamic allocation of shared memory is supported.

Atomics and Low-Level Intrinsics

Easy access to atomic functions and low-level-intrinsics like warp shuffles. All functions are supported during CPU debugging.

High-Performance Math Functions

Default math functions and operations are mapped to high-performance math functions. Furthermore, there is support for fast math and forced 32bit math to avoid doubles.

Comparison to C++ AMP and Cuda

Features
.Net Code
C++ Code
Function Annotations Required
NVIDIA GPUs
AMD GPUs
High-Level Abstractions (Implicitly Grouped Kernels, ...)
Low-Level Intrinsics
High-Performance Math Functions
Cross-Platform Support
Single-Compilation Cross-Platform Support
Direct Multi-GPU Support
Convenient Algorithm Debugging
Debugging on GPU Hardware
Kernel Profiling
CPU Runtime
CPU Runtime with Shared Memory and Low-Level Intrinsics
SIMD CPU Runtime
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Yellow checkmarks indicate partial or limited support.
Features marked with a red checkmark will be available in the future. Check the Roadmap for details.

Comparison to other GPU compilers for .Net

Features
Function Annotations Required
NVIDIA GPUs
AMD GPUs
High-Level Abstractions (Implicitly Grouped Kernels, ...)
Low-Level Intrinsics
High-Performance Math Functions
Avoids Boxing
Cross-Platform Support
DotNetCore Support
Convenient Algorithm Debugging
Debugging on GPU Hardware
Kernel Profiling
CPU Runtime
CPU Runtime with Shared Memory and Low-Level Intrinsics
Debug Assertions
Classes
Lambda Functions
ILGPU
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Commertial Competitors
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Yellow checkmarks indicate partial or limited support.
Features marked with a red checkmark will be available in the future. Check the Roadmap for details.

Frequently Asked Questions

Are exceptions supported?

Exceptions require support for exception handlers and a limited support for reference types. Changes of the "intended" control flow (which can be caused by exceptions) are currently not supported. However, there might be a conversion phase in the future that converts several exceptions into debug assertions.

What about debug assertions?

Debug assertions are supported on all accelerators. Note that debug assertions are not available in Release mode.

Are class types supported? And what about lambda functions?

Reference types are currently not supported. However, a limited support for reference types will be added in the future. This will also allow the implementation of delegates.

Lambda functions (or delegates in general) are currently not supported since they require a limited support for reference types and custom code-transformation passes. Support for lambda functions will be added in the future.

Can I debug a kernel on the GPU?

There is basic support for harware-based kernel debugging and profiling. However, CPU-based kernel debugging is recommended in all cases due to the advanced debugging and testing capabilities.

What about .Net Standard support?

Refer to the current GitHub issue.

What about Linux and Mac support?

ILGPU supports .Net Core, which allows writing portable .Net applications. Since ILGPU is written in C# and does not rely on native libraries in the current version, kernels can be run on all .Net Core compatible platforms. This allows you to compile your application (including GPU code) only once.

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