Performance & Benchmark
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.
The following benchmark is performed on NVIDIA GTX 1080 via Nsight for Performance tool on Windows 10 (64-bit) and CUDA toolkit version 9.1. Timing figure represents frames per second (fps) based on only the processing time on the single GPU. The benchmarks are performed on 8-bit images except if mentioned otherwise. The benchmarks for 16-bit demosaicDFPD on 1080p, 4k and 8k image are 1550fps, 412fps and 94fps.
|1080p Full HD||4k Ultra HD||8k Ultra HD|
|Auto Color||7088.83 fps||1850.02 fps||461.36 fps|
|Demosaic (DFPD)||1707.94 fps||412.72 fps||101.86 fps|
|Demosaic (Linear)||4258.88 fps||1025.64 fps||234.66 fps|
|Low Light Enhancement||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|