Illegal Memory Access Error on CUDA with Nvidia Jetson Orin Nano

Issue Overview

Users have reported encountering a runtime error on the Nvidia Jetson Orin Nano when executing the cudaMalloc function to allocate GPU memory. The specific error message indicates an illegal memory access during the execution of cudaMemcpy, leading to a segmentation fault. This issue typically arises when running CUDA applications that require memory allocation for image processing tasks.

Symptoms:

• Runtime error: "illegal memory access" (error code 700) during cudaMemcpy.
• Segmentation fault (core dumped) after modifying memory allocation code.
• Internal Sanitizer errors related to mobile debugger interface initialization and device support.

Context:

The problem occurs while executing CUDA code intended for image processing, specifically when allocating memory for multiple images using a loop. The issue has been consistently reproducible across different attempts.

Hardware/Software Specifications:

• Device: Nvidia Jetson Orin Nano
• CUDA Version: Not specified in the discussion
• Operating System: Likely a Linux distribution, as indicated by the use of terminal commands.

Impact:

This illegal memory access significantly hampers the ability to run CUDA applications effectively, thereby affecting user experience and overall system functionality.

Possible Causes

1. Memory Management Issues:

   • Reusing pointers without proper deallocation can lead to illegal accesses. If temp1 is reused without freeing previously allocated memory, it may cause conflicts.

2. Buffer Allocation Errors:

   • Failing to allocate sufficient memory for each image or mismanaging pointers can lead to segmentation faults or illegal accesses.

3. Driver or Configuration Issues:

   • Incompatibilities with CUDA drivers or incorrect configurations in the development environment may trigger these errors.

4. Environmental Factors:

   • Insufficient power supply or overheating could affect the stability of operations, especially during intensive computations.

5. User Errors:

   • Misconfigurations in the code, such as incorrect pointer usage or improper handling of asynchronous operations, may lead to runtime errors.

Troubleshooting Steps, Solutions & Fixes

1. Verify Memory Allocation:

   • Ensure that each pointer used for cudaMalloc is unique and properly allocated.

   double **d_imgs;
   HANDLE_ERROR(cudaMalloc(&d_imgs, config.N * sizeof(double*)));
   for (int i = 0; i < config.N; ++i) {
       HANDLE_ERROR(cudaMalloc((void**)&d_imgs[i], nSize * sizeof(double)));
       HANDLE_ERROR(cudaMemcpyAsync(d_imgs[i], imgs[i].ptr<double>(0), nSize * sizeof(double), cudaMemcpyHostToDevice));
   }
   

2. Check Permissions:

   • Ensure that /dev/nvidia* nodes have the correct permissions set. This can be checked using:

   ls -l /dev/nvidia*
   

3. Use Compute Sanitizer:

   • Run your application with Compute Sanitizer to gather more detailed information about the illegal access.

   compute-sanitizer ./your_cuda_application
   

4. Inspect Backtraces:

   • Analyze the backtrace provided in the error message to identify where the illegal access occurs and adjust your code accordingly.

5. Test with Simplified Code:

   • Create a minimal version of your CUDA code that isolates the memory allocation and copying logic to identify if the issue persists.

6. Update Drivers and SDKs:

   • Ensure that you are using the latest version of CUDA and JetPack SDK compatible with your Jetson Orin Nano.

7. Monitor System Resources:

   • Use tools like nvidia-smi to monitor GPU usage and ensure that there are no resource constraints causing instability.

8. Best Practices:

   • Always free allocated GPU memory after use to prevent leaks and potential illegal accesses.

   for (int i = 0; i < config.N; ++i) {
       HANDLE_ERROR(cudaFree(d_imgs[i]));
   }
   HANDLE_ERROR(cudaFree(d_imgs));
   

9. Seek Community Support:

   • If issues persist, consider posting detailed logs and code snippets on forums like NVIDIA Developer Forums for community assistance.

Unresolved Aspects

Further investigation may be needed regarding specific environmental factors affecting performance, as well as potential bugs in the CUDA version being used. Users should also clarify if they are using any specific libraries that may conflict with CUDA operations.