CUDA ISP for NVIDIA Jetson/Performance/Library: Difference between revisions
No edit summary |
No edit summary |
||
Line 22: | Line 22: | ||
* On the Jetson AGX Orin, we used Jetpack 5.0.2 and MAXN Power Mode (NVP model 0) | * On the Jetson AGX Orin, we used Jetpack 5.0.2 and MAXN Power Mode (NVP model 0) | ||
The following table summarizes CUDA ISP's performance results | 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. | ||
<center> | <center> | ||
{| class="wikitable" style="text-align:center;" | {| class="wikitable" style="text-align:center;" | ||
Line 44: | Line 44: | ||
|- | |- | ||
| colspan="9" style="font-weight:bold; text-align:center;" | Processing time by algorithm (microseconds) | | colspan="9" style="font-weight:bold; text-align:center;" | Processing time by algorithm (microseconds, framerate in Hz) | ||
|- style="text-align:right;" | |- style="text-align:right;" |
Revision as of 14:48, 31 March 2023
CUDA ISP for NVIDIA Jetson |
---|
CUDA ISP for NVIDIA Jetson Basics |
Getting Started |
User Manual |
GStreamer |
Examples |
Performance |
Contact Us |
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 thechrono
library. - We used the
sys/times.h
library to obtain the CPU usage. - 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 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 MAXN Power Mode (NVP model 0)
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.
Platform | Jetson AGX Orin | Jetson Xavier AGX | Jetson Xavier NX | Jetson Nano | ||||
---|---|---|---|---|---|---|---|---|
Buffer size | 1080p | 4K | 1080p | 4K | 1080p | 4K | 1080p | 4K |
Processing time by algorithm (microseconds, framerate in Hz) | ||||||||
CudaShift | 60 (16.7K) | 51 (19.6K) | 135 (7.4K) | 131 (7.6K) | 93 (10.8K) | 93 (10.8K) | 135 (7.4K) | 147 (6.8K) |
CudaDebayer | 22 (45.5K) | 20 (50.0K) | 48 (20.8K) | 39 (25.6K) | 39 (25.6K) | 31 (32.3K) | 53 (18.9K) | 55 (18.2K) |
CudaWhiteBalancer | 4056 (247) | 5966 (168) | 4844 (206) | 8091 (124) | 1360 (735) | 4249 (235) | 5071 (197) | 18903 (53) |
CudaColorSpaceConverter | 20 (50.0K) | 17 (58.8K) | 45 (22.2K) | 52 (19.2K) | 35 (28.6K) | 34 (29.4K) | 55 (18.2K) | 57 (17.5K) |
Resource consumption profile | ||||||||
CPU usage (%) | 0.211 | 0.129 | 0.491 | 0.458 | 0.524 | 0.477 | 0.836 | 0.820 |
CPU RAM (MB) | 160.3 | 157.6 | 173.6 | 173.5 | 173.5 | 172.0 | 146.3 | 147.6 |
GPU usage (%) | 20.68 | 27.06 | 13.22 | 50.16 | 5.48 | 17.91 | 25.12 | 94.60 |
GPU RAM (MB) | 86.7 | 135.9 | 105.2 | 107.6 | 100.4 | 106.3 | 91.7 | 116.8 |