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CuPy is a part of the NumPy ecosystem array libraries [7] and is widely adopted to utilize GPU with Python, [8] especially in high-performance computing environments such as Summit, [9] Perlmutter, [10] EULER, [11] and ABCI.
In computing, CUDA 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.
PyTorch is a machine learning library based on the Torch library, [4] [5] [6] used for applications such as computer vision and natural language processing, [7] originally developed by Meta AI and now part of the Linux Foundation umbrella.
shared, read-only memory.global global memory, shared by all threads.local local memory, private to each thread.param parameters passed to the kernel.shared memory shared between threads in a block.tex global texture memory (deprecated) Shared memory is declared in the PTX file via lines at the start of the form:
[1] [2] As in the multi-threaded context where a program executes several threads simultaneously in a shared address space and each of those threads has access to every other thread's memory, thread-safe functions need to ensure that all those threads behave properly and fulfill their design specifications without unintended interaction.
Torch is an open-source machine learning library, a scientific computing framework, and a scripting language based on Lua. [3] It provides LuaJIT interfaces to deep learning algorithms implemented in C. It was created by the Idiap Research Institute at EPFL. Torch development moved in 2017 to PyTorch, a port of the library to Python. [4] [5] [6]
The number of threads in a block is limited, but grids can be used for computations that require a large number of thread blocks to operate in parallel and to use all available multiprocessors. CUDA is a parallel computing platform and programming model that higher level languages can use to exploit parallelism.
The use of shared memory and an atomic test-and-set instruction provide the mutual exclusion. A process can test-and-set on a location in shared memory, and since the operation is atomic, only one process can set the flag at a time. Any process that is unsuccessful in setting the flag can either go on to do other tasks and try again later ...