Performance & Benchmark

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Revision as of 19:11, 5 July 2018 by Jawad (talk | contribs)

If one thing CUVI gives you, it's performance boost over competitive libraries and solutions. Using GPGPU as the underlying hardware, Imaging and Vision modules get maximum benefit due to their inherent parallel algorithms. In addition to cost cutting on CPU-based clusters, CUVI gives up to 15x speedup over Intel IPP.

Applications using CUVI are generally ten times faster than their CPU counterpart. CUVI framework also gives the ease to scale the application on more than one GPU making it as fast as you want.

1080p Full HD 4k Ultra HD 8k Ultra HD
autoColor 7088.83 fps 1850.02 fps 461.36 fps
demosaicDFPD 1707.94 fps 412.72 fps 101.86 fps
lowlight 2143.02 fps 525.16 fps 145.52 fps
Resize (2x - Nearest Neighbor) 4169.51 fps 1048.44 fps 260.164 fps
Resize (2x - Linear) 2494.80 fps 613.65 fps 151.53 fps
Resize (2x - Cubic) 1778.42 fps 456.68 fps 108.44 fps
Resize (0.5x - Nearest Neighbor) 47,265.68 fps 12,396.48 fps 3145.28 fps
Resize (0.5x - Linear) 26,365.05 fps 6793.71 fps 1703.32 fps
Resize (0.5x - Cubic) 11,232.92 fps 3143.94 fps 799.00 fps