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CUDA is a software layer that gives direct access to the GPU's virtual instruction set and parallel computational elements for the execution of compute kernels. [5] In addition to drivers and runtime kernels, the CUDA platform includes compilers, libraries and developer tools to help programmers accelerate their applications.
CUDA code runs on both the central processing unit (CPU) and graphics processing unit (GPU). NVCC separates these two parts and sends host code (the part of code which will be run on the CPU) to a C compiler like GNU Compiler Collection (GCC) or Intel C++ Compiler (ICC) or Microsoft Visual C++ Compiler, and sends the device code (the part which will run on the GPU) to the GPU.
Full machine code compatibility would here imply exactly the same layout of interrupt service routines, I/O-ports, hardware registers, counter/timers, external interfaces and so on. For a more complex embedded system using more abstraction layers (sometimes on the border to a general computer, such as a mobile phone), this may be different.
Julia is a high-level, general-purpose [17] dynamic programming language, still designed to be fast and productive, [18] for e.g. data science, artificial intelligence, machine learning, modeling and simulation, most commonly used for numerical analysis and computational science.