Alea.cuBase allows to create GPU accelerated applications at all levels of sophistication, from simple GPU kernels up to complex GPU algorithms using textures, shared memory and other advanced GPU programming techniques, fully integrated into .NET. The GPU kernels are developed in functional language F# and are callable from any other .NET language. No additional wrappers or assembly translation processes are required. Alea.cuBase allows dynamic creation of GPU code at run time, thereby opening completely new dimensions for GPU accelerated applications. Trial versions are available at http://www.quantalea.net/products.
TidePowerd has released Version 2 of their GPU computing solution for the .NET framework, GPU.NET. Their platform allows developers to quickly and easily write GPU-accelerated applications completely in .NET-based languages. Some key benefits include:
- Stay in C# and treat kernel methods like any regular method
- “Boilerplate” GPU programming tasks such as memory transfer and GPU scheduling are abstracted from the developer
- Cross-platform and cross-hardware with a single binary
- Systems seamlessly adapt to new hardware without rewriting code
- Speed on par with native code
New version 2 features:
- Visual Studio Error list and IntelliSense integration
- On-device random number generation
- Double precision support
From a recent press release:
CUDAfy is a .NET SDK that allows you to write, debug and emulate CUDA GPU applications in any .NET language including C# or Visual Basic. The aim is to bring the power of GPGPU to the large number of .NET developers out there. Features include:
- .NET object orientated CUDA model (GThread)
- Write .NET code marking methods, structures and constants that should be translated to CUDA (“Cudafying”)
- An add-in for Red Gate’s .NET Reflector tool that translates to CUDA C
- Built in emulation of GPU kernel functions
- 1D, 2D and 3D array support including access to Array class’s Length, GetLength and Rank members
- Use all standard .NET value types. No new types even for managing data allocated on GPU
- Simple .NET wrapper for CUFFT and CUBLAS
During our work with the European Space Agency, Astrium and NLR we saw how GPUs could significantly improve performance of the emulation of algorithms targeted on FPGAs and ASICs. The SDEs and SDKs produced were .NET based and CUDAfy is the result of efforts to more tightly integrate the GPU and CPU code development. There are user guides and sample projects. Many of the samples in the book CUDA by Example have been ported to .NET. See www.hybriddsp.com for downloads and more information.
A webinar by Jack Pappas, CEO and Co-Founder of Tidepowerd is being hosted by NVIDIA this coming Wednesday at 9am PST.
Tidepowerd have created GPU.NET, a software tool which allows developers to write GPU-accelerated code in managed languages like C# and VB.NET.
The “Beta 2” version of GPU.NET, a new product by TidePowerd, has recently been released. It allows developers to write GPU-based code in C# or other .NET-supported languages. GPU.NET beta is available for public download, and the full documentation and several example projects are available online.
CUDA.NET 2.1 has been released with support for the NVIDIA CUDA 2.1 API. This version supports DirectX 10 interoperability and the new JIT compilation API. The library is supported on Windows and Linux operating systems. (CUDA.NET)
CUDA.NET version 2.0 is now available for download. Changes from CUDA.NET 1.1 include full support for the CUDA 2.0 API, support for double precision data types, the latest BLAS routines from CUDA 2.0, and some minor bug fixes. (CUDA.NET)
CUDA.NET is an effort by GASS to provide access to NVIDIA CUDA functionality through .NET applications. The library currently provides .NET bindings for CUDA functions, allowing programmers to use existing .NET applications as hosts for CUDA enabled devices, this way exposing a strong co-processor that can be used with .NET. The current distribution contains a .NET library that can be used from any .NET application and language, along with examples in C# and Python showing how to use the library. The API is very straightforward and compatible with the NVIDIA CUDA API available for C applications with few modifications to ease development and align with .NET standards. See the CUDA.NET home page for more details.