{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/2"},{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/2"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics"}],"enrichment":{"capability":"NVSHMEM provides a global address space for GPU cluster communication, enabling fine-grained GPU and CPU-initiated operations across multiple GPU memories using OpenSHMEM-based primitives.","skillfed_tags":["gpu-computing","distributed-systems","cuda"],"use_cases":["Implement collective operations (allreduce, broadcast) across GPUs in a cluster without explicit message passing.","Build distributed training loops where multiple GPUs access shared parameter buffers via global memory semantics.","Develop scientific simulations that require fine-grained synchronization and data movement between GPU memories.","Optimize communication in multi-GPU inference pipelines by leveraging GPU-initiated remote memory access."],"what_it_does":"NVSHMEM is NVIDIA's implementation of the OpenSHMEM parallel programming standard, adapted for GPU clusters. It creates a unified memory address space spanning multiple GPUs, allowing kernels and CPU code to read and write remote GPU memory with fine-grained control. The package is built on top of CUDA 12 and is intended for researchers and engineers building distributed machine learning and scientific computing workloads that need efficient inter-GPU communication.\n\nThe package is in Beta status and actively maintained. It targets modern Python versions (3.5 through 3.11) and runs on Linux (both aarch64 and x86_64 architectures). Installation requires the nvidia-cuda-cccl-cu12 runtime dependency, and the platform-specific wheels mean you must match your system architecture exactly.","worth_installing":"Yes, if you are building multi-GPU CUDA applications on Linux and need efficient inter-GPU communication. The package is actively maintained, has no known vulnerabilities, and targets a real use case in distributed GPU computing. However, verify the unclear license terms and confirm Windows support is not actually available despite classifier claims. Medium install friction is acceptable for a specialized GPU library."},"id":"nvidia-nvshmem-cu12","links":{"html":"https://skillfed.io/packages/nvidia-nvshmem-cu12","md":"https://skillfed.io/packages/nvidia-nvshmem-cu12.md","pypi":"https://pypi.org/project/nvidia-nvshmem-cu12/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-17","license_spdx":null,"license_treatment":"unclear","name":"nvidia-nvshmem-cu12","python_support":"supports_current","summary":"NVSHMEM creates a global address space that provides efficient and scalable communication for NVIDIA GPU clusters."},"popularity":{"monthly_downloads":7604088,"position":1715,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"3.7.2"}