examples.rst

Examples

This page links to the cuda.bindings examples shipped in the :cuda-bindings-examples:`cuda-python repository </>`. Use it as a quick index when you want a runnable sample for a specific API area or CUDA feature.

Introduction

• :cuda-bindings-example:`clock_nvrtc.py <0_Introduction/clock_nvrtc.py>` uses NVRTC-compiled CUDA code and the device clock to time a reduction kernel.
• :cuda-bindings-example:`simple_cubemap_texture.py <0_Introduction/simple_cubemap_texture.py>` demonstrates cubemap texture sampling and transformation.
• :cuda-bindings-example:`simple_p2p.py <0_Introduction/simple_p2p.py>` shows peer-to-peer memory access and transfers between multiple GPUs.
• :cuda-bindings-example:`simple_zero_copy.py <0_Introduction/simple_zero_copy.py>` uses zero-copy mapped host memory for vector addition.
• :cuda-bindings-example:`system_wide_atomics.py <0_Introduction/system_wide_atomics.py>` demonstrates system-wide atomic operations on managed memory.
• :cuda-bindings-example:`vector_add_drv.py <0_Introduction/vector_add_drv.py>` uses the CUDA Driver API and unified virtual addressing for vector addition.
• :cuda-bindings-example:`vector_add_mmap.py <0_Introduction/vector_add_mmap.py>` uses virtual memory management APIs such as cuMemCreate and cuMemMap for vector addition.

Concepts and techniques

• :cuda-bindings-example:`stream_ordered_allocation.py <2_Concepts_and_Techniques/stream_ordered_allocation.py>` demonstrates cudaMallocAsync and cudaFreeAsync together with memory-pool release thresholds.

CUDA features

• :cuda-bindings-example:`global_to_shmem_async_copy.py <3_CUDA_Features/global_to_shmem_async_copy.py>` compares asynchronous global-to-shared-memory copy strategies in matrix multiplication kernels.
• :cuda-bindings-example:`simple_cuda_graphs.py <3_CUDA_Features/simple_cuda_graphs.py>` shows both manual CUDA graph construction and stream-capture-based replay.

Libraries and tools

• :cuda-bindings-example:`conjugate_gradient_multi_block_cg.py <4_CUDA_Libraries/conjugate_gradient_multi_block_cg.py>` implements a conjugate-gradient solver with cooperative groups and multi-block synchronization.
• :cuda-bindings-example:`nvidia_smi.py <4_CUDA_Libraries/nvidia_smi.py>` uses NVML to implement a Python subset of nvidia-smi.

Advanced and interoperability

• :cuda-bindings-example:`iso_fd_modelling.py <extra/iso_fd_modelling.py>` runs isotropic finite-difference wave propagation across multiple GPUs with peer-to-peer halo exchange.
• :cuda-bindings-example:`jit_program.py <extra/jit_program.py>` JIT-compiles a SAXPY kernel with NVRTC and launches it through the Driver API.