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Forward compatibility or upward compatibility is a design characteristic that allows a system to accept input intended for a later version of itself. The concept can be applied to entire systems, electrical interfaces , telecommunication signals , data communication protocols , file formats , and programming languages .
In computing, CUDA (Compute Unified Device Architecture) is a proprietary [2] parallel computing platform and application programming interface (API) that allows software to use certain types of graphics processing units (GPUs) for accelerated general-purpose processing, an approach called general-purpose computing on GPUs.
Binary-code compatibility (binary compatible or object-code compatible) is a property of a computer system, meaning that it can run the same executable code, typically machine code for a general-purpose computer central processing unit (CPU), that another computer system can run.
A simple example of both backward and forward compatibility is the introduction of FM radio in stereo. FM radio was initially mono, with only one audio channel represented by one signal. With the introduction of two-channel stereo FM radio, many listeners had only mono FM receivers.
Even on similar systems, the details of implementing a compatibility layer can be quite intricate and troublesome; a good example is the IRIX binary compatibility layer in the MIPS architecture version of NetBSD. [25] A compatibility layer requires the host system's CPU to be (upwardly) compatible to that of the foreign
Software compatibility can refer to the compatibility that a particular software has running on a particular CPU architecture such as Intel or PowerPC. [1] Software compatibility can also refer to ability for the software to run on a particular operating system. Very rarely is a compiled software compatible with multiple different CPU ...
rCUDA, which stands for Remote CUDA, is a type of middleware software framework for remote GPU virtualization. Fully compatible with the CUDA application programming interface ( API ), it allows the allocation of one or more CUDA-enabled GPUs to a single application.
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.