Nvidia's CUDA 7.5

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CUDA 7.5

Both of the above pieces of software are based on the new CUDA 7.5 toolkit. The reason why CuDNN 3 supports 16-bit floating point data is because CUDA 7.5 now supports it. Most notably, it offers support for mixed floating point data, meaning that 16-bit vector data can be used where accuracy is less essential, and 32-bit data points will be used when higher accuracy is required.
Additional changes include new cuSPARSE GEMVI routines, along with instruction-level profiling, which can help you figure out which part of your code is limiting GPU performance.
The Preview Release version of the DIGTS 2 software is all available for free to registered CUDA developers, with final versions coming soon. More information is available here.

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  1. 제갈식2016년 6월 21일 PM 6:24

    16~32비트 부동소수점 연산, 그리고 GPU병목을 찾아내게 해주는 cuSPARSE

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파이썬으로 Homomorphic Filtering 하기

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import cv2 import numpy as np from matplotlib import pyplot as plt img = cv2 . imread ( 'messi5.jpg' , 0 ) f = np . fft . fft2 ( img ) fshift = np . fft . fftshift ( f ) magnitude_spectrum = 20 * np . log ( np . abs ( fshift )) plt . subplot ( 121 ), plt . imshow ( img , cmap = 'gray' ) plt . title ( 'Input Image' ), plt . xticks ([]), plt . yticks ([]) plt . subplot ( 122 ), plt . imshow ( magnitude_spectrum , cmap = 'gray' ) plt . title ( 'Magnitude Spectrum' ), plt . xticks ([]), plt . yticks ([]) plt . show () rows , cols = img . shape crow , ccol = rows / 2 , cols / 2 fshift [ crow - 30 : crow + 30 , ccol - 30 : ccol + 30 ] = 0 f_ishift = np . fft . ifftshift ( fshift ) img_back = np . fft . ifft2 ( f_ishift ) img_back = np . abs ( img_back ) plt . subplot ( 131 ), plt . imshow ( img , cmap = 'gray' ) plt . title ( 'Input Image' ), plt . xticks ([]), ...
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