Difference between revisions of "Performance & Benchmark"

From CUVI Wiki
Line 24: Line 24:
| 461.36 fps
| 461.36 fps
|-
|-
| [[Function:Dehaze|dehaze]]
| [[Function:Lowlight|lowlight]]
| 1707.94 fps
| 2143.02 fps
| 389.68 fps
| 525.16 fps
| 145.52 fps
|-
|-
| [[Function:DemosaicDFPD|demosaicDFPD]]
| [[Function:DemosaicDFPD|demosaicDFPD]]
| 1707.94 fps
| 1707.94 fps
| 389.68 fps
| 389.68 fps
|
|-
|-
| [[Function:Demosaic|demosaic]]
| [[Function:Demosaic|demosaic]]
| 1707.94 fps
| 1707.94 fps
| 389.68 fps
| 389.68 fps
|


|}
|}

Revision as of 19:14, 26 June 2018

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 4k 8k
channelMix 1707.94 fps 389.68 fps
autoColor 7088.83 fps 1850.02 fps 461.36 fps
lowlight 2143.02 fps 525.16 fps 145.52 fps
demosaicDFPD 1707.94 fps 389.68 fps
demosaic 1707.94 fps 389.68 fps