Error Xid 31 when Running Concurrent CudaGraph ExecutionContexts on Nvidia Jetson Orin Nano

Issue Overview

Users are experiencing an Xid 31 error when running two CudaGraph captured ExecutionContexts concurrently on the Nvidia Jetson Orin Nano Dev board. This issue arises specifically during the execution of TensorRT plans compiled from ONNX, where users successfully capture ExecutionContexts and launch them on Streams. However, after a significant number of loop iterations, the error manifests, disrupting the expected program flow.

Symptoms and Context

• Symptoms: The error appears as an Xid 31 error, which is indicative of a problem related to GPU resource management or memory access.

• Context: The issue occurs during the execution of TensorRT plans in a loop, particularly when certain conditions are met that lead to concurrent execution of multiple CudaGraph captured contexts.

• Hardware/Software Specifications: Users have reported this issue while utilizing TensorRT version 8.6.1.6 on systems with RTX 4070 or RTX A4500 GPUs. The context is relevant to the Jetson Orin Nano but primarily affects CI servers used for testing.

• Frequency: The error does not occur consistently; it happens after an arbitrary number of iterations, suggesting a potential memory leak or resource contention.

• Impact: This problem significantly hampers the ability to perform concurrent GPU computations, which is crucial for testing and deploying applications on the Jetson platform.

Possible Causes

• Concurrent Memory Access: The Jetson platform does not support concurrent memory access effectively, which may lead to the Xid 31 error when multiple ExecutionContexts attempt to access GPU resources simultaneously.

• Software Bugs or Conflicts: There may be underlying bugs in the CUDA or TensorRT software that result in improper handling of concurrent execution contexts.

• Configuration Errors: Incorrect configurations in how TensorRT plans are set up or executed could lead to resource contention issues.

• Driver Issues: Outdated or incompatible GPU drivers might contribute to problems with memory management and execution contexts.

• User Errors or Misconfigurations: Users may inadvertently misconfigure their execution environments, leading to unexpected behavior during concurrent executions.

Troubleshooting Steps, Solutions & Fixes

1. Verify Compatibility:

   • Ensure that TensorRT plans are compiled specifically for the Orin Nano using JetPack 6.0.
   • Check that all software components (CUDA, TensorRT) are up-to-date and compatible with your hardware.

2. Isolate the Issue:

   • Run tests with only one ExecutionContext at a time to confirm that the issue arises only during concurrent executions.
   • Utilize logging to capture detailed output before the error occurs for further analysis.

3. Test Different Configurations:

   • Experiment with different configurations of your TensorRT plans to identify if specific settings trigger the error.
   • Try running your application on a different setup (e.g., using a desktop system) to see if the issue persists.

4. Memory Management Practices:

   • Implement proper memory management techniques in your code to avoid potential leaks or contention.
   • Review documentation on NVIDIA Tegra memory architecture for best practices regarding memory usage on Jetson platforms.

5. Seek Workarounds:

   • If possible, modify your application logic to avoid concurrent execution of multiple CudaGraph contexts until a more permanent fix is identified.
   • Engage with NVIDIA support forums or customer support for potential patches or updates addressing this specific issue.

6. Documentation and Resources:

   • Refer to NVIDIA’s official documentation on CUDA for Tegra for insights into memory architecture and usage considerations.
   • Keep an eye on updates from NVIDIA regarding improvements or fixes related to this issue in future releases of TensorRT or CUDA.

Code Snippet Example

To gather system information related to CUDA usage, you can run:

nvidia-smi

This command provides details about GPU utilization and can help diagnose if resources are being overused or mismanaged during execution.

Unresolved Aspects

While several users have reported similar issues, no definitive solution has been established yet. Further investigation may be required into specific configurations that lead to the Xid 31 error under concurrent executions. Users are encouraged to share reproducible examples with NVIDIA support for more targeted assistance.