# GPU * [Graphics Processing Unit](https://en.wikipedia.org/wiki/Graphics_processing_unit) * * GPUs offer many cores, but narrow instruction set and lower clock speed * VS video card: Video Card generated feed of output images; has a GPU in its core. Also usually has dedicated RAM (unlike integrated video cards that share RAM with the rest of the system). * GPUs offer many cores, but narrow instruction set and lower clock speed * Major brands: NVIDIA (GeeForce), AMD Radeon, Intel. ### CUDA * CUDA - NVIDIA's platform and API for accessing their GPU's instruction sets. CUDA-powered GPUs also support open standards like OpenMP or OpenCL. * Natively supports C, C++, Fortran. Third-party wrappers exist for Python, Ruby and many other languages. * Parallelize functions by making a function doing only a part of the job depending on its thread id. * Threads are grouped into blocks; blocks form a grid. * Lower-level [Driver API](https://docs.nvidia.com/cuda/cuda-driver-api/index.html) and higher-level [Runtime API.](https://docs.nvidia.com/cuda/cuda-runtime-api/index.html) * ### DL Thinks like convolution or even matrix multiplication can easily be parallelized to run much faster on GPU. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://wiki.ainzzorl.lol/tech/technologies/gpu.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.