May 2019
Intermediate to advanced
452 pages
12h 16m
English
Content preview from Hands-On GPU Computing with Python
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Sixth generation
NVIDIA released their GeForce 8 series of GPUs as massively parallel processors in 2006. It was also the same year in July when AMD acquired ATI for $5.4 billion.
In 2007, CUDA was released as a freely available proprietary API by NVIDIA, only for NVIDIA GPUs. Through the years, CUDA became very popular in academia and industry for GPU computing. C/C++ programmers began their journey with the NVCC compiler from CUDA:
Image credit: Tosaka – own work, CC BY 3.0, https://commons.wikimedia.org/w/index.php?curid=5140417
The preceding diagram describes in a simple manner how intense computations are accelerated on a GPU with CUDA. ...
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