developed by Marcel Köster
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.
A new release of the ILGPU compiler is available.
It includes many performance improvements (in terms of ILGPU and kernel runtime) and bug fixes. New support for .Net Standard 2.1 and OpenCL-compatible GPUs (beta) have been added. Furthermore, it offers generic warp functions and a new extension API to implement custom intrinsics for user-defined backends/intrinsics. Please note that math functions via the XMath class are no longer directly support by ILGPU. You can leverage the IntrinsicMath class to use math functions that are guaranteed to be available on all platforms. Use the new ILGPU.Algorithms library to access the full power of the XMath class that is supported on all platforms. The ILGPU.Algorithms library also includes a set of useful functions (like Reduce or Scan) that are also supported on group and/or warp level. All samples, the class reference, documentation and upgrade guide have been updated.
Special thanks to MoFtZ for contributing to this release. MoFtZ worked on several issues in the PTXBackend, the Cuda runtime and the internal IR.
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.
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.
The previously announced Google group is now obsolete. Please refer to Github or the Discord channel for communication and support purposes>
High performance kernel compilation, dispatch and execution times. Furthermore, type-safe kernel delegates avoid boxing.
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.
Single- or multi-threaded execution of kernels on the CPU. This is also useful for debugging or emulation of specific target platforms.
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.
Functions do not have to be annotated in order to use them in the scope of kernels.
Compile your applications for any cpu. ILGPU will automatically adjust everything else for X86 or X64 platforms.
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 index types simplify address computations and kernel writing.
No pointer arithmetic and dramatically simplified index computations due to views to memory regions.
Support for shared (scratch-pad) memory in kernels via array views. Static or dynamic allocation of shared memory is supported.
Easy access to atomic functions and low-level-intrinsics like warp shuffles. All functions are supported during CPU debugging.
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.
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.
Debug assertions are supported on all accelerators. Note that debug assertions are not available in Release mode.
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.
There is basic support for hardware-based kernel debugging and profiling. However, CPU-based kernel debugging is recommended in all cases due to the advanced debugging and testing capabilities.
The new ILGPU version supports .Net 4.7, .Net Standard 2.0 (e.g. .Net Core 2.0) and .Net Standard 2.1 (e.g. .Net Core 3.0).
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.