Difference between revisions of "CUDA ISP for NVIDIA Jetson/Performance/Library"

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For each system, we also used <code>jetson_clocks</code> to maximize the device clock frequency and thus the performance.
 
For each system, we also used <code>jetson_clocks</code> to maximize the device clock frequency and thus the performance.
  
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The following table summarizes CUDA ISP's performance results. The values in parenthesis next to each processing time is the corresponding theoretical framerate calculated as the inverse of the time.
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The following table summarizes CUDA ISP's performance results.
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Revision as of 12:24, 26 April 2023


  Index  


CUDA ISP for NVIDIA Jetson
Cudaisp-transparent.png
CUDA ISP for NVIDIA Jetson Basics
Getting Started
User Manual
GStreamer
Examples
Performance
Contact Us
 





This page is still under development. Please, do not take the performance data as definitive.

Library API performance

To measure the CUDA ISP API performance, we built a simple example (provided upon request) that iterates over the Apply methods for each algorithm and records performance metrics for each iteration. We measured the duration of each algorithm's Apply method. We also measured CPU, CPU RAM, GPU, and GPU RAM usage for the complete processing pipeline iterating at 30fps. We ran the experiments on both 1080p and 4K buffers. We also ran the experiments on the Jetson Nano, Jetson Xavier NX, Jetson Xavier AGX, and Jetson AGX Orin.

  • We measured the duration of each Apply method separately using the chrono library.
  • We mainly used sys/times.h library to obtain the CPU usage. However, we used the proc/status to create a secondary measure in order to verify.
  • We read the /proc/self/status file to obtain the CPU RAM usage.
  • We used jtop to measure GPU usage on the Jetson Nano and Jetson Xavier NX. We use jetson-stats to measure GPU usage on the Jetson Xavier AGX and the Jetson AGX Orin.
  • We used cudaMemGetInfo from CUDA to measure GPU RAM usage.

This is the hardware setup we used:

  • On the Jetson Nano, we used Jetpack 4.5.3 and 10W 4 Core MAXN Power Mode (NVP model 0)
  • On the Jetson Xavier NX, we used Jetpack 4.5.3 and 20W 6 Core Power Mode (NVP model 8)
  • On the Jetson Xavier AGX, we used Jetpack 4.5.1 and 30W 8 Core Power Mode (NVP model 3)
  • On the Jetson AGX Orin, we used Jetpack 5.0.2 and 50W 12 Core Power Mode (NVP model 3)

For each system, we also used jetson_clocks to maximize the device clock frequency and thus the performance.

The following table summarizes CUDA ISP's performance results.

Algorithm Jetson AGX Orin Jetson Xavier AGX Jetson Xavier NX Jetson Nano
Buffer size 1080p 4K 1080p 4K 1080p 4K 1080p 4K
Duration (ms)
CudaShift 0.82 1.52 1.56 4.18 0.71 1.82 2.10 7.52
CudaDebayer 0.68 1.30 1.93 5.74 0.79 2.13 2.40 8.75
CudaWhiteBalancer (Gray World Algorithm) 0.84 1.66 1.94 5.21 0.99 2215 2.51 8.51
CudaWhiteBalancer (Histogram Stretch Algorithm) 1.24 1.91 2.33 6.87 1.11 2.71 3.18 11.06
CudaColorSpaceConverter 0.91 1.60 1.23 3.13 0.59 1.31 2.05 7.70
Framerate (fps)
CudaShift 1216 660 641 239 1408 550 475 132
CudaDebayer 1479 771 519 174 1259 469 415 114
CudaWhiteBalancer (Gray World Algorithm) 1197 603 515 191 1011 451 398 117
CudaWhiteBalancer (Histogram Stretch Algorithm) 807 522 429 145 902 368 314 90
CudaColorSpaceConverter 1104 623 814 319 1697 761 487 129
CPU usage (%)
CudaShift 0.198 0.218 0.285 0.255 0.287 0.356 0.800 0.817
CudaDebayer 0.121 0.161 0.238 0.237 0.263 0.280 0.873 0.665
CudaWhiteBalancer (Gray World Algorithm) 0.201 0.277 0.338 0.316 0.443 0.471 1.286 1.299
CudaWhiteBalancer (Histogram Stretch Algorithm) 0.260 0.280 0.351 0.341 0.527 0.442 1.569 1.454
CudaColorSpaceConverter 0.172 0.201 0.246 0.237 0.278 0.251 0.647 0.680
CPU RAM (MB)
CudaShift 89.7 90.8 90.0 91.5 89.9 90.0 77.5 88.7
CudaDebayer 44.4 44.5 41.8 42.2 41.7 42.9 33.0 33.4
CudaWhiteBalancer (Gray World Algorithm) 99.8 99.1 108.5 107.4 107.6 108.1 40.6 77.5
CudaWhiteBalancer (Histogram Stretch Algorithm) 99.2 99.3 107.7 108.6 108.1 107.5 52.1 33.0
CudaColorSpaceConverter 44.1 44.3 42.0 42.1 42.1 42.0 33.2 89.1
GPU usage (%)
CudaShift 12.29 7.81 18.03 13.00 11.59 4.23 85.32 25.96
CudaDebayer 11.60 13.04 28.42 29.00 7.28 10.38 67.27 42.53
CudaWhiteBalancer (Gray World Algorithm) 9.75 19.27 17.22 25.54 4.70 15.15 20.24 75.14
CudaWhiteBalancer (Histogram Stretch Algorithm) 8.89 24.42 17.00 24.56 5.36 17.11 26.84 83.35
CudaColorSpaceConverter 4.85 9.14 13.47 24.68 4.64 15.36 20.11 81.19
GPU RAM (MB)
CudaShift 26.8 28.4 43.0 85.7 42.9 63.7 41.6 43.3
CudaDebayer 6.0 6.1 10.7 10.3 9.8 11.2 11.9 11.8
CudaWhiteBalancer (Gray World Algorithm) 33.7 32.7 57.5 61.6 56.8 59.6 40.6 31.5
CudaWhiteBalancer (Histogram Stretch Algorithm) 33.2 33.4 56.8 66.3 57.1 62.2 52.1 52.4
CudaColorSpaceConverter 5.4 6.0 9.9 10.4 10.0 10.3 11.2 11.8



  Index  



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