Copy image arrays between CPU and GPU memory using the modern cuda.core API with optimal performance through pinned memory and asynchronous transfers.

• How to use pinned memory for faster CPU↔GPU transfers
• Using the cuda.core API for memory management
• Working with DLPack for zero-copy array views
• Performing asynchronous memory transfers with CUDA streams
• Interoperability between CUDA Core API and CuPy
• Proper CUDA resource management and cleanup

• cuda.core - Modern CUDA Python API
• numpy - Array operations and DLPack support
• cupy - GPU array operations and CUDA interoperability

• Device() - Initialize and access CUDA device
• Device.set_current() - Set the current device for API calls
• Device.create_stream() - Create CUDA stream for async operations
• Device.memory_resource - Access device memory allocator
• PinnedMemoryResource() - Allocate pinned host memory
• buffer.copy_to() - Copy data between memory spaces
• buffer.close() - Release allocated memory

• np.from_dlpack() - Create array view from DLPack capsule
• np.copyto() - Copy data between arrays

• cp.from_dlpack() - Create GPU array view from DLPack capsule
• cp.cuda.Stream.from_external() - Use external CUDA stream

• verify_array_result() - Verify computation results

• NVIDIA GPU with CUDA support
• Sufficient GPU memory for image data (sample uses ~200KB for 256×256×3 image)

• CUDA Toolkit 13.0 or newer
• Python 3.10 or newer
• NumPy 2.3.2 or newer (required for DLPack support)
• cuda-python package (>=13.0.0+)
• cuda-core package (>=1.0.0)
• cupy-cuda13x package (14.0.0+)

Install the required packages from requirements.txt:

cd /path/to/cuda-python/samples/cuda_core/copyImageArraytoGPU
pip install -r requirements.txt

The requirements.txt installs:

• numpy (2.3.2+, required for DLPack)
• cuda-python (>=13.0.0+)
• cuda-core (>=1.0.0)
• cupy-cuda13x (14.0.0+)

cd samples/cuda_core/copyImageArraytoGPU
python copyImageArraytoGPU.py

[Image Array Copy to GPU using CUDA Core API]
Device: NVIDIA GeForce RTX 4090
[Image array copy of 256x256x3 image]
Creating sample image...
Copying image to GPU...
Creating CuPy view of GPU data...
Mean pixel value (computed on GPU): 127.50
Copying image back from GPU...
Verifying result...
Test PASSED

Done

Note: Device name will vary based on your GPU.

• copyImageArraytoGPU.py - Python implementation using cuda.core API
• README.md - This file
• requirements.txt - Sample dependencies
• ../Utilities/cuda_samples_utils.py - Common utilities (imported by this sample)

• CUDA Python Documentation
• cuda.core API Guide
• DLPack Specification
• CuPy Documentation