common high-performance computing concepts. Newer, hardware-vendor-independent offerings include Microsoft's DirectCompute and Apple/Khronos Group's OpenCL. This means that modern GPGPU pipelines can leverage the speed of a GPU without requiring full and explicit conversion of the data to a graphical form. Mark Harris, the founder of GPGPU.org, coined the term GPGPU. Implementations Any language that allows the code running on the CPU to poll a GPU shader for return values, can create a GPGPU framework. Programming standards for parallel computing include OpenCL (vendor-independent), OpenACC, OpenMP and OpenHMPP. , OpenCL is the dominant open general-purpose GPU computing language, and is an open standard defined by the Khronos Group. OpenCL provides a cross-platform GPGPU platform that additionally supports data parallel compute on CPUs. OpenCL is actively supported on Intel, AMD, Nvidia, and ARM platforms. The Khronos Group has also standardised and implemented SYCL, a higher-level programming model for OpenCL as a single-source domain specific embedded language based on pure C++11. The dominant proprietary framework is Nvidia CUDA. Nvidia launched CUDA in 2006, a software development kit (SDK) and application programming interface (API) that allows using the programming language C to code algorithms for execution on GeForce 8 series and later GPUs. ROCm, launched in 2016, is AMD's open-source response to CUDA. It is, as of 2022, on par with CUDA with regards to features, and still lacking in consumer support. OpenVIDIA was developed at University of Toronto between 2003–2005, in collaboration with Nvidia. Altimesh Hybridizer created by Altimesh compiles Common Intermediate Language to CUDA binaries. It supports generics and virtual functions. Debugging and profiling is integrated with Visual Studio and Nsight. It is available as a Visual Studio extension on Visual Studio Marketplace. Microsoft introduced the DirectCompute GPU computing API, released with the DirectX 11 API. , created by QuantAlea, introduces native GPU computing capabilities for the Microsoft .NET languages F# and C#. Alea GPU also provides a simplified GPU programming model based on GPU parallel-for and parallel aggregate using delegates and automatic memory management. MATLAB supports GPGPU acceleration using the Parallel Computing Toolbox and MATLAB Distributed Computing Server, and third-party packages like Jacket. GPGPU processing is also used to simulate Newtonian physics by physics engines, and commercial implementations include Havok Physics, FX and PhysX, both of which are typically used for computer and video games. C++ Accelerated Massive Parallelism (C++ AMP) is a library that accelerates execution of C++ code by exploiting