Difference between revisions of "CUDA ISP for NVIDIA Jetson/Performance/Library"
| Line 14: | Line 14: | ||
* 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 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 <code>cudaMemGetInfo</code> from CUDA to measure GPU RAM usage. | * We used <code>cudaMemGetInfo</code> 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 Power Mode | * On the Jetson Nano, we used Jetpack 4.5.3 and Power Mode | ||
Revision as of 14:39, 30 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
Applymethod separately using thechronolibrary. - We used the
sys/times.hlibrary to obtain the CPU usage. - We read the
/proc/self/statusfile 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
cudaMemGetInfofrom CUDA to measure GPU RAM usage.
This is the hardware setup we used:
- On the Jetson Nano, we used Jetpack 4.5.3 and Power Mode
- On the Jetson Xavier NX, we used Jetpack 4.5.3 and Power Mode
- On the Jetson Xavier AGX, we used Jetpack 4.5.1 and Power Mode
- On the Jetson AGX Orin, we used Jetpack 5.0.2 and Power Mode
The following table summarizes CUDA ISP's performance results:
| 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) | ||||||||
| 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 |
