GPU

  • 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 and higher-level Runtime API.

DL

Thinks like convolution or even matrix multiplication can easily be parallelized to run much faster on GPU.

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