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If you can parallelize your code by harnessing the power of the GPU, I bow to you. GPU code is usually abstracted away by by the popular deep learning frameworks, but knowing how it works is really useful. CUDA is the most popular of the GPU frameworks so we're going to add two arrays together, then optimize that process using it. I love CUDA!
Code for this video:
https://github.com/llSourcell/An_Introduction_to_GPU_Programming
Alberto's Winning Code:
https://github.com/alberduris/SirajsCodingChallenges/tree/master/Stock%20Market%20Prediction
Hutauf's runner-up code:
https://github.com/hutauf/Stock_Market_Prediction
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More learning resources:
http://supercomputingblog.com/cuda-tutorials/
http://www.nvidia.com/docs/IO/116711/sc11-cuda-c-basics.pdf
https://devblogs.nvidia.com/parallelforall/even-easier-introduction-cuda/
https://developer.nvidia.com/cuda-education-training
https://llpanorama.wordpress.com/cuda-tutorial/
https://www.udacity.com/course/intro-to-parallel-programming--cs344
http://lorenabarba.com/gpuatbu/Program_files/Cruz_gpuComputing09.pdf
http://cuda-programming.blogspot.nl/p/tutorial.html
https://www.cc.gatech.edu/~vetter/keeneland/tutorial-2011-04-14/02-cuda-overview.pdf
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http://wizards.herokuapp.com/
No, Nvidia did not pay me to make this video lol. I just love CUDA.
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