Difference between revisions of "Performance & Benchmark"
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|+ style="caption-side:bottom; color:#e76700;"|''Kernel Time in milliseconds (ms) with CUVI v1.8.0 on GTX 1080 | <!--|+ style="caption-side:bottom; color:#e76700;"|''Kernel Time in milliseconds (ms) with CUVI v1.8.0 on GTX 1080--> | ||
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|colspan="5" |Kernel Time in milliseconds (ms) with CUVI v1.8.0 on GTX 1080 having '''2,560''' CUDA Cores. | |||
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! Algorithm / Image Size | ! Algorithm / Image Size |
Revision as of 16:08, 30 October 2022
Measured with NVIDIA's Performance tools for Windows and Linux. Timing figure represents time of kernel/function in milliseconds (rounded) on a single GPU. The benchmarks are performed on color images with 8-bits per channel except where mentioned otherwise. The list below is a small subset of 100+ features in CUVI.
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Color Pipeline
Let's take a typical color pipeline and measure its performance on one of the entry level GPUs. Any color pipeline almost always starts with the Raw image. Before converting to RGB, you might want to do some processing on the raw which may include applying LUTs (look up tables), FPN (fixed point noise) removal and fixing white balance. Next comes demosaic/debayer followed by several further enhancement functions and a color space conversion into the desired format. This pipeline can perform in real-time on a decent entry level GPU on an 8k images and at over 100 FPS on a 2k image:
Performance
- Image size: 8k
- Debayer method: DFPD
- RAW Size: 59.9 MB
- Codec: JPEG2000
- Sharpening: 7x7
- GPU: GTX 1080
- FPS: 26 FPS