examples.rst

Examples

This page links to the cuda.core examples shipped in the :cuda-core-examples:`cuda-python repository </>`. Use it as a quick index when you want a runnable starting point for a specific workflow.

Compilation and kernel launch

• :cuda-core-example:`vector_add.py` compiles and launches a simple vector-add kernel with CuPy arrays.
• :cuda-core-example:`saxpy.py` JIT-compiles a templated SAXPY kernel and launches both float and double instantiations.
• :cuda-core-example:`pytorch_example.py` launches a CUDA kernel with PyTorch tensors and a wrapped PyTorch stream.

Multi-device and advanced launch configuration

• :cuda-core-example:`simple_multi_gpu_example.py` compiles and launches kernels across multiple GPUs.
• :cuda-core-example:`thread_block_cluster.py` demonstrates thread block cluster launch configuration on Hopper-class GPUs.
• :cuda-core-example:`tma_tensor_map.py` demonstrates Tensor Memory Accelerator descriptors and TMA-based bulk copies.

Linking and graphs

• :cuda-core-example:`jit_lto_fractal.py` uses JIT link-time optimization to link user-provided device code into a fractal workflow at runtime.
• :cuda-core-example:`cuda_graphs.py` captures and replays a multi-kernel CUDA graph to reduce launch overhead.

Interoperability and memory access

• :cuda-core-example:`memory_ops.py` covers memory resources, pinned memory, device transfers, and DLPack interop.
• :cuda-core-example:`strided_memory_view_cpu.py` uses StridedMemoryView with JIT-compiled CPU code via cffi.
• :cuda-core-example:`strided_memory_view_gpu.py` uses StridedMemoryView with JIT-compiled GPU code and foreign GPU buffers.
• :cuda-core-example:`gl_interop_plasma.py` renders a CUDA-generated plasma effect through OpenGL interop without CPU copies.

System inspection

• :cuda-core-example:`show_device_properties.py` prints a detailed report of the CUDA devices available on the system.