[ENH] Implement batched GPU interpolation with CUDA streams for parallel stack processing | GEN-14003#42
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[ENH] Implement batched GPU interpolation with CUDA streams for parallel stack processing | GEN-14003#42Leguark wants to merge 4 commits intooctree_improvementfrom
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- Updated tensor creation to use pinned memory and non-blocking transfer for improved GPU performance. - Introduced `_zeros`, `_ones`, and `_eye` wrapper functions for better consistency in tensor initialization on the specified device. - Refined the `_wrap_pytorch_functions` method to streamline tensor operations and ensure compatibility with the device settings. - Enabled stricter CUDA checks by updating conditions for GPU availability.
…ularity and enhance batch processing support - Introduced the `keops_enabled` parameter across various modules to enable conditional usage of PyKeOps for optimized computations. - Added `_interpolate_stack_batched.py` for GPU-accelerated batched interpolation with CUDA streams, minimizing memory overhead and improving throughput. - Updated tensor creation logic in `backend_tensor.py` to include `pykeops_eval_enabled` for enhanced flexibility in method selection. - Refactored multiple constructor methods to propagate `keops_enabled`, ensuring consistent conditional logic for tensor handling and backend compatibility. - Improved fault data initialization and dependency handling in interpolation pipelines for better parallel computation.
This was referenced Jan 23, 2026
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[ENH] Improve tensor handling and device management in backend_tensor.py
_zeros,_ones, and_eyewrapper functions for better consistency in tensor initialization on the specified device._wrap_pytorch_functionsmethod to streamline tensor operations and ensure compatibility with the device settings.[ENH] Add
keops_enabledparameter to improve kernel constructor modularity and enhance batch processing supportkeops_enabledparameter across various modules to enable conditional usage of PyKeOps for optimized computations._interpolate_stack_batched.pyfor GPU-accelerated batched interpolation with CUDA streams, minimizing memory overhead and improving throughput.backend_tensor.pyto includepykeops_eval_enabledfor enhanced flexibility in method selection.keops_enabled, ensuring consistent conditional logic for tensor handling and backend compatibility.[WIP] Towards batching
[ENH] JIT-compiled kernel functions for improved GPU performance
torch.jit.scriptfor better GPU execution