NVIDIA Deep Learning NCCL Documentation
Release Notes (PDF) - v2.20.3 - Last updated February 12, 2024

NCCL Release 2.1.15

Key Features and Enhancements

This NCCL release includes the following key features and enhancements.
  • Added a variable to control InfiniBand Traffic Class and Retry Count.

Using NCCL 2.1.15

Ensure you are familiar with the following notes when using this release.
  • The NCCL 2.x API is different from NCCL 1.x. Some porting may be needed for NCCL 1.x applications to work correctly. Refer to the migration documentation in the NCCL Developer Guide.
  • Starting in 2.2, NCCL supports collective aggregation. You can put multiple collectives in between ncclGroupStart() and ncclGroupEnd() to enable this feature.

Known Issues

  • If NCCL returns an error code, set the environment variable NCCL_DEBUG to WARN to receive an explicit error message.
  • Using multiple processes in conjunction with multiple threads to manage the different GPUs may in some cases cause ncclCommInitRank to fail while establishing IPCs (cudaIpcOpenMemHandle). This problem does not appear when using only processes or only threads.
  • NCCL uses CUDA 9 cooperative group launch by default, which may induce increased latencies in multi-threaded programs. See the NCCL_LAUNCH_MODE knob to restore the original behavior.

Fixed Issues

  • Fixed CPU usage and scheduling of NCCL network threads.
  • Fixed CUDA launch crash when mixing different types of GPUs in a node.
  • Fixed a performance problem on Skylake CPUs.
  • Fixed hanging issues with cudaFree and inter-node communication.
  • Restored library installation path to /usr/lib/x86_64-linux-gnu in debian packages.
  • Fixed RoCEv2 failure when using a non-zero GID.
  • No longer link to stdc++ library statically as this can cause issues with C++ applications.
  • Fixed PyTorch hanging issues when using multiple rings and many back-to-back broadcast operations.