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. 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.

New Major Release

A new major version has been released


The new ILGPU version includes several important enhancements: dotnetcore support, convenient kernel loading and caching. Please refer to the updated ILGPU samples or the upgrade guide for more information. Note that all libraries contain required backwards compatibility that is marked with the Obsolete attribute.

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 currently no support for harware-based kernel debugging. This will be added in the future. However, CPU-based kernel debugging is recommended in all cases due to the advanced debugging and testing capabilities.

What about .Net Core support?

The current release supports .Net framework 4.6 or higher and .Net Core 2.0.

What about Linux and Mac support?

Once .Net Core support is available, the platform-dependent parts of ILGPU have to be ported to Linux and Mac. Support for Linux and Mac will be available in one of the next releases. This offers you the opportunity to compile your application (including GPU code) once for every target platform.

I am experiencing long compilation times in Debug mode...

The GPU-capable objects in the ILGPU library contain runtime assertions (e.g. bounds checks). Due to the nature of assertions, these are only available in Debug mode. Consequently, ILGPU is also in Debug mode when your application is in Debug mode. This will be changed in the future to provide fast compilation times in Debug mode.

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