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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.
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:
In some cases, the elimination of false sharing can result in order-of-magnitude performance improvements. [2] False sharing is an inherent artifact of automatically synchronized cache protocols and can also exist in environments such as distributed file systems or databases, but current prevalence is limited to RAM caches.
Once the space has been made available, the OS can read the data for the new page into memory, add an entry to its location in the memory management unit, and indicate that the page is loaded. Thus major faults are more expensive than minor faults and add storage access latency to the interrupted program's execution.
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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]
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