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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]
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.
Scattered reads – code can read from arbitrary addresses in memory. Unified virtual memory (CUDA 4.0 and above) Unified memory (CUDA 6.0 and above) Shared memory – CUDA exposes a fast shared memory region that can be shared among threads. This can be used as a user-managed cache, enabling higher bandwidth than is possible using texture lookups.
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]
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:
Hopper allows CUDA compute kernels to utilize automatic inline compression, including in individual memory allocation, which allows accessing memory at higher bandwidth. This feature does not increase the amount of memory available to the application, because the data (and thus its compressibility) may be changed at any time. The compressor ...
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.
Predication's primary drawback is in increased encoding space. In typical implementations, every instruction reserves a bitfield for the predicate specifying under what conditions that instruction should have an effect. When available memory is limited, as on embedded devices, this space cost can be prohibitive.