Performance difference with compute capability jetson orin nano vs gtx 2070

Issue Overview

Users have reported significant performance discrepancies when running object detection scripts on the Nvidia Jetson Orin Nano compared to a notebook equipped with a GTX 2070 graphics card. The specific symptoms include:

• Performance Lag: The Jetson Orin Nano is underperforming in object detection tasks using YOLOv8, despite both systems being configured similarly.
• Setup Context: The issue arises during the execution of a script for object detection, where the Jetson Orin Nano is configured with JetPack 5.1 and CUDA 11.4.
• Hardware Specifications: The GTX 2070 has a compute capability of 7.5, while the Jetson Orin Nano boasts a compute capability of 8.7.
• Frequency of Issue: This performance difference has been consistently observed by users comparing the two setups.
• Impact on User Experience: The poor performance on the Jetson Orin Nano affects the overall functionality and usability for tasks that require real-time object detection.

The context surrounding this issue includes questions about expected performance based on compute capabilities and whether optimizations have been fully utilized on the Jetson platform.

Possible Causes

Several potential causes for the performance issues have been identified:

• Hardware Incompatibilities: Differences in hardware architecture between the Jetson Orin Nano and GTX 2070 may lead to varying performance levels in specific tasks.

• Software Bugs or Conflicts: Incompatibilities between JetPack, CUDA versions, or TensorRT configurations may affect performance.

• Configuration Errors: Incorrect settings in the object detection script or CUDA configurations could hinder optimal performance.

• Driver Issues: Outdated or improperly configured drivers could lead to suboptimal GPU utilization.

• Environmental Factors: Power supply issues or thermal throttling could affect the performance of the Jetson Orin Nano during intensive tasks.

• User Errors or Misconfigurations: Users may not have maximized device performance settings, leading to lower-than-expected output.

Troubleshooting Steps, Solutions & Fixes

To address the performance issues observed with the Nvidia Jetson Orin Nano, follow these detailed troubleshooting steps and potential solutions:

1. Verify Device Performance Settings:

   • Ensure that all performance settings are optimized for the Jetson Orin Nano. This can include checking power modes and GPU settings.

2. Monitor GPU Utilization:

   • Use the following command to monitor GPU usage:

     sudo tegrastats
     

   • This command will provide real-time statistics about GPU utilization, memory usage, and thermal status.

3. Check Software Configuration:

   • Ensure that your YOLOv8 model is correctly configured for TensorRT with the parameters half=True and simplify=True. Verify that these settings are applied correctly in your script.

4. Update Software Components:

   • Make sure you are using the latest versions of JetPack, CUDA, and TensorRT. Check for any available updates that might enhance performance.

5. Test with Different Configurations:

   • Experiment with different configurations of your object detection script to see if certain settings yield better performance.
   • Test running simpler models to assess if complexity affects execution time.

6. Isolate Hardware Issues:

   • If possible, test your Jetson Orin Nano with different power supplies or cooling solutions to rule out thermal throttling or power delivery issues.

7. Consult Documentation:

   • Refer to Nvidia’s official documentation for best practices on optimizing performance for the Jetson platform, including recommended configurations and usage patterns.

8. Community Feedback:

   • Engage with community forums to see if others have encountered similar issues and what solutions they found effective.

9. Consider Alternative Approaches:

   • If persistent issues remain unresolved, consider leveraging cloud-based solutions or more powerful external GPUs for resource-intensive tasks until local optimization is achieved.

10. Document Findings:

    • Keep a log of changes made and their effects on performance to identify which adjustments yield positive results.

By following these steps, users can systematically diagnose and potentially resolve the performance issues faced when using the Nvidia Jetson Orin Nano compared to other GPUs like the GTX 2070.