This NVIDIA Collective Communication Library (NCCL) Installation Guide provides a step-by-step instructions for downloading and installing NCCL 2.7.8.

For previously released NCCL installation documentation, see NCCL Archives.

1. Overview

The NVIDIA® Collective Communications Library ™ (NCCL) (pronounced “Nickel”) is a library of multi-GPU collective communication primitives that are topology-aware and can be easily integrated into applications.

Collective communication algorithms employ many processors working in concert to aggregate data. NCCL is not a full-blown parallel programming framework; rather, it is a library focused on accelerating collective communication primitives. The following collective operations are currently supported:
  • AllReduce
  • Broadcast
  • Reduce
  • AllGather
  • ReduceScatter

Tight synchronization between communicating processors is a key aspect of collective communication. CUDA® based collectives would traditionally be realized through a combination of CUDA memory copy operations and CUDA kernels for local reductions. NCCL, on the other hand, implements each collective in a single kernel handling both communication and computation operations. This allows for fast synchronization and minimizes the resources needed to reach peak bandwidth.

NCCL conveniently removes the need for developers to optimize their applications for specific machines. NCCL provides fast collectives over multiple GPUs both within and across nodes. It supports a variety of interconnect technologies including PCIe, NVLink™ , InfiniBand Verbs, and IP sockets. NCCL also automatically patterns its communication strategy to match the system’s underlying GPU interconnect topology.

Next to performance, ease of programming was the primary consideration in the design of NCCL. NCCL uses a simple C API, which can be easily accessed from a variety of programming languages. NCCL closely follows the popular collectives API defined by MPI (Message Passing Interface). Anyone familiar with MPI will thus find NCCL API very natural to use. In a minor departure from MPI, NCCL collectives take a “stream” argument which provides direct integration with the CUDA programming model. Finally, NCCL is compatible with virtually any multi-GPU parallelization model, for example:
  • single-threaded
  • multi-threaded, for example, using one thread per GPU
  • multi-process, for example, MPI combined with multi-threaded operation on GPUs

NCCL has found great application in deep learning frameworks, where the AllReduce collective is heavily used for neural network training. Efficient scaling of neural network training is possible with the multi-GPU and multi node communication provided by NCCL.

2. Prerequisites

2.1. Software Requirements

Ensure your environment meets the following software requirements:
  • glibc 2.17 or higher
  • CUDA 10.0 or higher

2.2. Hardware Requirements

NCCL supports all CUDA devices with a compute capability of 3.5 and higher. For the compute capability of all NVIDIA GPUs, check: CUDA GPUs.

3. Installing NCCL

In order to download NCCL, ensure you are registered for the NVIDIA Developer Program.

  1. Go to: NVIDIA NCCL home page.
  2. Click Download.
  3. Complete the short survey and click Submit.
  4. Accept the Terms and Conditions. A list of available download versions of NCCL displays.
  5. Select the NCCL version you want to install. A list of available resources displays. Refer to the following sections to choose the correct package depending on the Linux distribution you are using.

3.1. Ubuntu

Installing NCCL on Ubuntu requires you to first add a repository to the APT system containing the NCCL packages, then installing the NCCL packages through APT. There are two repositories available; a local repository and a network repository. Choosing the latter is recommended to easily retrieve upgrades when newer versions are posted.
  1. Install the repository.
    • For the local NCCL repository:
      sudo dpkg -i nccl-repo-<version>.deb
    • For the network repository:
      sudo dpkg -i nvidia-machine-learning-repo-<version>.deb
  2. Update the APT database:
    sudo apt update
  3. Install the libnccl2 package with APT. Additionally, if you need to compile applications with NCCL, you can install the libnccl-dev package as well:
    Note: If you are using the network repository, the following command will upgrade CUDA to the latest version.
    sudo apt install libnccl2 libnccl-dev
    If you prefer to keep an older version of CUDA, specify a specific version, for example:
    sudo apt install libnccl2=2.4.8-1+cuda10.0 libnccl-dev=2.4.8-1+cuda10.0
    Refer to the download page for exact package versions.

3.2. RHEL/CentOS

Installing NCCL on RHEL or CentOS requires you to first add a repository to the YUM system containing the NCCL packages, then installing the NCCL packages through YUM. There are two repositories available; a local repository and a network repository. Choosing the latter is recommended to easily retrieve upgrades when newer versions are posted.
  1. Install the repository.
    • For the local NCCL repository:
      sudo rpm -i nccl-repo-<version>.rpm
    • For the network repository:
      sudo rpm -i nvidia-machine-learning-repo-<version>.rpm
  2. Update the YUM database:
    sudo yum update
  3. Install the libnccl2 package with YUM. Additionally, if you need to compile applications with NCCL, you can install the libnccl-devel package and optionally the libnccl-static package if you intend to link NCCL statically in your application:
    Note: If you are using the network repository, the following command will upgrade CUDA to the latest version.
    sudo yum install libnccl libnccl-devel libnccl-static
    If you prefer to keep an older version of CUDA, specify a specific version, for example:
    sudo yum install libnccl-2.4.8-1+cuda10.0 libnccl-devel-2.4.8-1+cuda10.0 libnccl-static-2.4.8-1+cuda10.0
    Refer to the download page for exact package versions.

3.3. Other Distributions

Download the tar file package. For more information, see Installing NCCL.
  1. Extract the NCCL package to your home directory or in /usr/local if installed as root for all users:
    # cd /usr/local 
    # tar xvf nccl-<version>.txz
  2. When compiling applications, specify the directory path to where you installed NCCL, for example /usr/local/nccl-<version>/.

4. Using NCCL

Using NCCL is similar to using any other library in your code. For example:

  1. Install the NCCL library onto your system. For more information, see Downloading NCCL.
  2. Modify your application to link to that library.
  3. Include the header file nccl.h in your application.
  4. Create a communicator. For more information, see Creating a Communicator in the NCCL Developer Guide.
  5. Familiarize yourself with the NCCL API documentation to maximize your usage performance.

5. Migrating From NCCL 1 To NCCL 2

If you are using NCCL 1.x and want to move to NCCL 2.x, be aware that the APIs have changed slightly. NCCL 2.x supports all of the collectives that NCCL 1.x supports, but with slight modifications to the API.

In addition, NCCL 2.x also requires the usage of the Group API when a single thread manages NCCL calls for multiple GPUs.

The following list summarizes the changes that may be required in usage of NCCL API when using an application has a single thread that manages NCCL calls for multiple GPUs, and is ported from NCCL 1.x to 2.x:
In 1.x, NCCL had to be initialized using ncclCommInitAll at a single thread or having one thread per GPU concurrently call ncclCommInitRank. NCCL 2.x retains these two modes of initialization. It adds a new mode with the Group API where ncclCommInitRank can be called in a loop, like a communication call, as shown below. The loop has to be guarded by the Group start and stop API.
 for (int i=0; i<ngpus; i++) {
   ncclCommInitRank(comms+i, ngpus, id, i);
In NCCL 2.x, the collective operation can be initiated for different devices by making calls in a loop, on a single thread. This is similar to the usage in NCCL 1.x. However, this loop has to be guarded by the Group API in 2.x. Unlike in 1.x, the application does not have to select the relevant CUDA device before making the communication API call. NCCL runtime internally selects the device associated with the NCCL communicator handle. For example:
 for (int i=0; i<nLocalDevs; i++) {
   ncclAllReduce(..., comm[i], stream[i];

When using only one device per thread or one device per process, the general usage of API remains unchanged from NCCL 1.x to 2.x. Group API is not required in this case.

Counts provided as arguments are now of type size_t instead of integer.

In-place usage for AllGather and ReduceScatter
For more information, see In-Place Operations in the NCCL Developer Guide.

AllGather arguments order
The AllGather function had its arguments reordered. The prototype changed from:
ncclResult_t  ncclAllGather(const void* sendbuff, int count, ncclDataType_t datatype,
    void* recvbuff, ncclComm_t comm, cudaStream_t stream);
ncclResult_t  ncclAllGather(const void* sendbuff, void* recvbuff, size_t sendcount,
    ncclDataType_t datatype, ncclComm_t comm, cudaStream_t stream);

The recvbuff argument has been moved after the sendbuff argument to be consistent with all the other operations.

New datatypes have been added in NCCL 2.x. The ones present in NCCL 1.x did not change and are still usable in NCCL 2.x.

Error codes
Error codes have been merged into the ncclInvalidArgument category and have been simplified. A new ncclInvalidUsage code has been created to cover new programming errors.

6. Troubleshooting

6.1. Support

Register for the NVIDIA Developer Program to report bugs, issues and make requests for feature enhancements. For more information, see: https://developer.nvidia.com/developer-program.

Refer to the NCCL open source documentation for additional support.


This document is provided for information purposes only and shall not be regarded as a warranty of a certain functionality, condition, or quality of a product. NVIDIA Corporation (“NVIDIA”) makes no representations or warranties, expressed or implied, as to the accuracy or completeness of the information contained in this document and assumes no responsibility for any errors contained herein. NVIDIA shall have no liability for the consequences or use of such information or for any infringement of patents or other rights of third parties that may result from its use. This document is not a commitment to develop, release, or deliver any Material (defined below), code, or functionality.

NVIDIA reserves the right to make corrections, modifications, enhancements, improvements, and any other changes to this document, at any time without notice.

Customer should obtain the latest relevant information before placing orders and should verify that such information is current and complete.

NVIDIA products are sold subject to the NVIDIA standard terms and conditions of sale supplied at the time of order acknowledgment, unless otherwise agreed in an individual sales agreement signed by authorized representatives of NVIDIA and customer (“Terms of Sale”). NVIDIA hereby expressly objects to applying any customer general terms and conditions with regards to the purchase of the NVIDIA product referenced in this document. No contractual obligations are formed either directly or indirectly by this document.

NVIDIA products are not designed, authorized, or warranted to be suitable for use in medical, military, aircraft, space, or life support equipment, nor in applications where failure or malfunction of the NVIDIA product can reasonably be expected to result in personal injury, death, or property or environmental damage. NVIDIA accepts no liability for inclusion and/or use of NVIDIA products in such equipment or applications and therefore such inclusion and/or use is at customer’s own risk.

NVIDIA makes no representation or warranty that products based on this document will be suitable for any specified use. Testing of all parameters of each product is not necessarily performed by NVIDIA. It is customer’s sole responsibility to evaluate and determine the applicability of any information contained in this document, ensure the product is suitable and fit for the application planned by customer, and perform the necessary testing for the application in order to avoid a default of the application or the product. Weaknesses in customer’s product designs may affect the quality and reliability of the NVIDIA product and may result in additional or different conditions and/or requirements beyond those contained in this document. NVIDIA accepts no liability related to any default, damage, costs, or problem which may be based on or attributable to: (i) the use of the NVIDIA product in any manner that is contrary to this document or (ii) customer product designs.


NVIDIA, the NVIDIA logo, and cuBLAS, CUDA, CUDA Toolkit, cuDNN, DALI, DIGITS, DGX, DGX-1, DGX-2, DGX Station, DLProf, GPU, Jetson, Kepler, Maxwell, NCCL, Nsight Compute, Nsight Systems, NVCaffe, NVIDIA Deep Learning SDK, NVIDIA Developer Program, NVIDIA GPU Cloud, NVLink, NVSHMEM, PerfWorks, Pascal, SDK Manager, Tegra, TensorRT, TensorRT Inference Server, Tesla, TF-TRT, Triton Inference Server, Turing, and Volta are trademarks and/or registered trademarks of NVIDIA Corporation in the United States and other countries. Other company and product names may be trademarks of the respective companies with which they are associated.