GPGPU libraries

CUDA

CUDA (or Compute Unified Device Architecture) is a parallel computing platform and application programming interface (API) that allows software to use certain types of graphics processing units (GPUs) for general purpose processing, an approach called general-purpose computing on GPUs (GPGPU). 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. CUDA is designed to work with programming languages such as C, C++, and Fortran. This accessibility makes it easier for specialists in parallel programming to use GPU resources, in contrast to prior APIs like Direct3D and OpenGL, which required advanced skills in graphics programming. CUDA-powered GPUs also support programming frameworks such as OpenMP, OpenACC and OpenCL; and HIP by compiling such code to CUDA. CUDA was created by Nvidia. When it was first introduced, the name was an acronym for Compute Unified Device Architecture, but Nvidia later dropped the common use of the acronym. (Wikipedia).

CUDA
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Structure from motion | Single-precision floating-point format | Ray tracing (graphics) | MATLAB | Neural network | TensorFlow | Encryption | Lua (programming language) | OptiX | CuPy | Order of magnitude | Stream processing | LLVM | Cryptographic hash function | Cryptography | C++ AMP | BrookGPU | Space partitioning | Julia (programming language) | OpenCL | R (programming language) | DirectCompute | SYCL | Algorithm | Run-time type information | JPEG 2000