**Altimesh Hybridizer Essentials** Hybridizer Essentials is a compiler targeting CUDA-enabled GPUS from .Net. Using parallelization patterns, such as Parallel.For, or ditributing parallel work by hand, the user can benefit from the compute power of GPUS without entering the learning curve of CUDA, all within Visual Studio. **Linux version** Is available on our [release page](https://github.com/altimesh/hybridizer-basic-samples/releases/tag/1.1.9554) **Warning** Installer has **changed**. Uninstall previous version before installing this one. **New in this version** - Improved installer -- complete rewrite. Able to properly handle updates - Add Reference to CUDAImports from visual studio - OpenGL interop (with mandelbulb sample) - Simple recursion (CUDA only) -- no stackalloc in recursive functions, no parallel for - Improved linux support (same licensing model as Windows) - Jupyter notebooks - improved builtins integration (intellisense in the xml) - Major internal refactoring (remove third party dlls...) - Redistributable license (upon request only) - couple of bug fixes (hybarrays) **Disclaimer** We don't support the entire C# language or .Net Framework. Main known limitations are - Heap allocation (new A()) from device thread (except for arrays) - System.Collection is not supported - string type is not supported **Known Issues** - Visual studio latest C++ toolset (v141) and CUDA 9.0/9.1/9.2 are not compatible. To compile CUDA code with Visual studio 2017, you need to install and use v140 toolset (visual 2015). This is enforced in our project templates. - SharedMatrix and cublas samples can fail on windows 8.1 for unknown reason.


	Debugging and profiling If debug information is available, the debugging can be performed from within the development environment, while running the optimized code on the target hardware; for example, a program written in C#, with a breakpoint in the cs file within Visual Studio can have that breakpoint hit and its local variables and object data explored while execution occurs on an NVIDIA GPU (see snapshot).
	Customization While providing automated default behavior, Hybridizer leaves full control to the developer at each phase, allowing the reuse of existing device-specific code, use of existing external libraries or custom handmade code snippets.
	Performance All of this flexibility does not come at the expense of performances loss. As our benchmark illustrate, code generated by the Hybridizer can perform as well as hand-written code.