Examples

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

Introduction

• clock_nvrtc.py uses NVRTC-compiled CUDA code and the device clock to time a reduction kernel.

• simple_cubemap_texture.py demonstrates cubemap texture sampling and transformation.

• simple_p2p.py shows peer-to-peer memory access and transfers between multiple GPUs.

• simple_zero_copy.py uses zero-copy mapped host memory for vector addition.

• system_wide_atomics.py demonstrates system-wide atomic operations on managed memory.

• vector_add_drv.py uses the CUDA Driver API and unified virtual addressing for vector addition.

• vector_add_mmap.py uses virtual memory management APIs such as cuMemCreate and cuMemMap for vector addition.

Concepts and techniques

• stream_ordered_allocation.py demonstrates cudaMallocAsync and cudaFreeAsync together with memory-pool release thresholds.

CUDA features

• global_to_shmem_async_copy.py compares asynchronous global-to-shared-memory copy strategies in matrix multiplication kernels.

• simple_cuda_graphs.py shows both manual CUDA graph construction and stream-capture-based replay.

Libraries and tools

• conjugate_gradient_multi_block_cg.py implements a conjugate-gradient solver with cooperative groups and multi-block synchronization.

• nvidia_smi.py uses NVML to implement a Python subset of nvidia-smi.

Advanced and interoperability

• iso_fd_modelling.py runs isotropic finite-difference wave propagation across multiple GPUs with peer-to-peer halo exchange.

• jit_program.py JIT-compiles a SAXPY kernel with NVRTC and launches it through the Driver API.

• numba_emm_plugin.py shows how to back Numba’s EMM interface with the NVIDIA CUDA Python Driver API.