NVIDIA/nvshmem

NVIDIA 资料

最近记录的来源活动: 2026年10月3日 18:37

SkillsMP 收录数据更新: 2026年10月26日 11:37

已收集 skills: 1

GitHub 星标: 597
GitHub Forks: 114

仓库 README

仓库 README 来源：README.md

查看 README 源文件

展开完整 README

# NVSHMEM GPU-initiated communication for scalable NVIDIA GPU clusters. ## Introduction NVSHMEM is an OpenSHMEM-based parallel programming interface that provides a partitioned global address space across NVIDIA GPUs. Applications use symmetric memory for one-sided transfers, atomics, signaling, synchronization, and collective operations. NVSHMEM provides host, CUDA kernel, and CUDA stream interfaces, allowing CPU or GPU threads to initiate communication. [![license](https://img.shields.io/badge/License-Apache_2.0-blue)](License.txt) [![docs](https://img.shields.io/badge/Documentation-latest-green)](https://docs.nvidia.com/nvshmem/api/) [![contributions](https://img.shields.io/badge/Contributions-Accepted-green)](CONTRIBUTING.md) ## Quick Start ### Prerequisites - A Linux system, NVIDIA driver, and CUDA Toolkit supported by the current [NVSHMEM release][release-notes]. - Two NVIDIA data center GPUs. - A compatible process launcher, such as Open MPI, Hydra, or Slurm. ### Install NVSHMEM Download a package from the [NVSHMEM product page][product-page] and follow the [installation guide][install-guide]. Set `NVSHMEM_PREFIX` to the installation directory: ```bash export NVSHMEM_PREFIX=/path/to/nvshmem export LD_LIBRARY_PATH="$NVSHMEM_PREFIX/lib${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}" ``` ### Verify the Installation Launch two NVSHMEM processing elements (PEs), one per GPU, using a process launcher available on your system: ```bash # Open MPI NVSHMEM_BOOTSTRAP=MPI \ mpirun -np 2 \ "$NVSHMEM_PREFIX/bin/perftest/device/pt-to-pt/shmem_put_bw" \ --min_size 8 --max_size 1M --use_smem 0 # Hydra (PMI-1) NVSHMEM_BOOTSTRAP_PMI=PMI \ mpiexec.hydra -n 2 -ppn 2 \ "$NVSHMEM_PREFIX/bin/perftest/device/pt-to-pt/shmem_put_bw" \ --min_size 8 --max_size 1M --use_smem 0 # Slurm (PMI-2) NVSHMEM_BOOTSTRAP_PMI=PMI-2 \ srun --mpi=pmi2 --nodes=1 --ntasks=2 --gpus-per-task=1 \ "$NVSHMEM_PREFIX/bin/perftest/device/pt-to-pt/shmem_put_bw" \ --min_size 8 --max_size 1M --use_smem 0 ``` A successful run reports both GPUs, prints put bandwidth for a range of message sizes, and exits with status zero. If initialization fails, rerun with `NVSHMEM_DEBUG=INFO` and consult the [launching guide][launching-guide]. For further help, use the channels in [Support and Community](#support-and-community). ## Build from Source See the [installation guide][install-guide] for source prerequisites and build options. A standard CMake build is: ```bash git clone https://github.com/NVIDIA/nvshmem.git cd nvshmem export NVSHMEM_PREFIX="$PWD/install" cmake -S . -B build \ -DNVSHMEM_PREFIX="$NVSHMEM_PREFIX" \ -DCMAKE_INSTALL_PREFIX="$NVSHMEM_PREFIX" cmake --build build --parallel cmake --install build ``` The development branch may be newer than the latest published release and its documentation. For a release-aligned build, check out the corresponding branch or tag before running CMake. ## Use NVSHMEM Installed packages provide imported CMake targets for the host and device libraries: ```cmake find_package(NVSHMEM REQUIRED CONFIG) target_link_libraries(my_target PRIVATE nvshmem::nvshmem_host nvshmem::nvshmem_device ) ``` Applications that use only host-side or stream-ordered APIs can link only the host target. See the [Using NVSHMEM guide](https://docs.nvidia.com/nvshmem/api/using.html) for the programming model, application lifecycle, compilation, and launch requirements. The [put-block example](examples/put-block.cu) provides a complete CUDA program. Python users should continue with the [NVSHMEM4Py quick start](nvshmem4py/README.md#quick-start). ## Documentation - [API reference](https://docs.nvidia.com/nvshmem/api/) and source [examples](examples/) - [Installation guide][install-guide] and [launching guide][launching-guide] - [Release notes][release-notes] for supported platforms, compatibility, and known issues - [Environment variables](https://docs.nvidia.com/nvshmem/api/gen/env.html) and [best practices][best-practices] ## Support and Community - Report reproducible bugs, request features, or ask questions through [GitHub Issues](https://github.com/NVIDIA/nvshmem/issues). - Contact the maintainers at [nvshmem@nvidia.com](mailto:nvshmem@nvidia.com). ## Contributing See the [Contributing Guide](CONTRIBUTING.md) before opening a pull request. Commits require a Developer Certificate of Origin (DCO) sign-off (`git commit -s`). ## License NVSHMEM is licensed under the [Apache License 2.0](License.txt). [best-practices]: https://docs.nvidia.com/nvshmem/release-notes-install-guide/best-practice-guide/index.html [install-guide]: https://docs.nvidia.com/nvshmem/release-notes-install-guide/install-guide/nvshmem-install-proc.html [launching-guide]: https://docs.nvidia.com/nvshmem/release-notes-install-guide/install-guide/nvshmem-install-proc.html#launching-nvshmem-programs [product-page]: https://developer.nvidia.com/nvshmem [release-notes]: https://docs.nvidia.com/nvshmem/release-notes-install-guide/release-notes/index.html

正在加载仓库 README…

这个仓库中的 skills

职业分类待补全

skill

职业分类

描述

更新

nvshmem-select-remote-transport

未分类

Select an NVSHMEM remote transport from target system and kernel evidence. Use for inter-node selection, compatibility checks, or configuration.

原文语言：英语

2026-10-03

已展示 1 / 1 个已收集 Skill。

用 AI 助手安装

复制这段 Prompt，发给你正在使用的 AI 助手。

Follow https://skillsmp.com/skill-install/prompt.md to install Agent Skills from https://github.com/NVIDIA/nvshmem.