NCCL

NCCL, or NVIDIA Collective Communications Library, is a library from NVIDIA that provides high-performance collective communication primitives for multi-GPU and multi-node workloads. It is designed to optimize data movement for parallel applications, especially in distributed machine learning and high-performance computing. Key operations include all-reduce, reduce, all-gather, all-to-all, broadcast, and reduce-scatter, along with basic point-to-point communication. The library is designed to operate directly on GPU memory to minimize host involvement and latency.

NCCL emphasizes topology-aware, scalable performance. It automatically selects efficient algorithms and transports to exploit available interconnects, such as NVLink, NVSwitch, and PCIe within a node, as well as high-speed networks across nodes. It provides a backend abstraction, the NCCL Net API, to support multiple transport mechanisms, and is commonly used in conjunction with distributed runtimes like MPI or framework-specific systems for multi-GPU training and inference.

Integration and usage are widespread in the deep learning ecosystem. NCCL is frequently employed by frameworks such as PyTorch, TensorFlow, and MXNet to implement data-parallel training and other distributed workloads, benefiting from optimized GPU-to-GPU communication. It is maintained by NVIDIA and released under a BSD-style license.

Platforms and scope are primarily Linux-based systems with CUDA-enabled GPUs. Windows support is limited relative to Linux, and deployment typically occurs in data centers and HPC clusters that rely on NVIDIA accelerators and high-bandwidth interconnects.