CUDA Notes

This page summarizes CUDA-specific setup and failure modes.

Compatibility Checklist

1. NVIDIA driver installed and visible in nvidia-smi.
2. PyTorch build has CUDA support.
3. CUDA toolkit available for native build workflows.
4. CMake can detect nvcc during csrc build.

Typical Setup

• Ensure CUDA Toolkit is installed and on PATH.
• Verify nvcc --version.
• Confirm your PyTorch build has CUDA enabled.

Useful Python checks:

import torch
print(torch.cuda.is_available())
print(torch.version.cuda)

Troubleshooting

1. torch.cuda.is_available() is False:

   • install matching CUDA-enabled torch wheel
   • verify driver installation

2. Native backend loads but CUDA symbols are unavailable:

   • rebuild csrc with a valid CUDA toolkit and visible nvcc
   • check CMake output for CUDA detection messages

3. Runtime illegal memory access or launch failures:

   • validate tensor shapes and bounds for source/receiver indices
   • reduce workload size and reproduce with one shot for isolation

4. Performance lower than expected:

   • test storage_mode=device first
   • profile with realistic n_shots and nt
   • verify kernels are not falling back to Python backend