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CUDA - Wikipedia CUDA is written in the C programming language but is designed to work with a wide array of other programming languages including C++, Fortran, Python and Julia
CuPy - Wikipedia CuPy is an open source library for GPU-accelerated computing with Python programming language, providing support for multi-dimensional arrays, sparse matrices, and a variety of numerical algorithms implemented on top of them [3] CuPy shares the same API set as NumPy and SciPy, allowing it to be a drop-in replacement to run NumPy SciPy code on GPU CuPy supports Nvidia CUDA GPU platform, and
Numba - Wikipedia Numba is an open-source JIT compiler that translates a subset of Python and NumPy into fast machine code using LLVM, via the llvmlite Python package It offers a range of options for parallelising Python code for CPUs and GPUs, often with only minor code changes
Thread block (CUDA programming) - Wikipedia Thread block (CUDA programming) A thread block is a programming abstraction that represents a group of threads that can be executed serially or in parallel For better process and data mapping, threads are grouped into thread blocks
General-purpose computing on graphics processing units The dominant proprietary framework is Nvidia CUDA [14] 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
PyTorch - Wikipedia PyTorch Tensors are similar to NumPy Arrays, but can also be operated on by a CUDA -capable NVIDIA GPU PyTorch has also been developing support for other GPU platforms, for example, AMD's ROCm [21] and Apple's Metal Framework
Nvidia CUDA Compiler - Wikipedia Nvidia CUDA Compiler (NVCC) is a compiler by Nvidia intended for use with CUDA It is proprietary software
Parallel Thread Execution - Wikipedia Parallel Thread Execution Parallel Thread Execution (PTX or NVPTX[1]) is a low-level parallel thread execution virtual machine and instruction set architecture used in Nvidia 's Compute Unified Device Architecture (CUDA) programming environment
Single instruction, multiple threads - Wikipedia Single instruction, multiple threads (SIMT) is an execution model used in parallel computing where a single central "control unit" broadcasts an instruction to multiple "processing units" for them to all optionally perform simultaneous synchronous and fully-independent parallel execution of that one instruction Each PU has its own independent data and address registers, its own independent