Intel® oneAPI Collective Communications Library

Scalable & Efficient Distributed Training for Deep Neural Networks

Implement Multi-Node Communication Patterns

The Intel® oneAPI Collective Communications Library (oneCCL) enables developers and researchers to more quickly train newer and deeper models. This is done by using optimized communication patterns to distribute model training across multiple nodes.

The library is designed for easy integration into deep learning frameworks, whether you are implementing them from scratch or customizing existing ones.

  • Built on top of lower-level communication middleware. Message passing interface (MPI) and libfabrics transparently support many interconnects, such as Cornelis Networks*, InfiniBand*, and Ethernet.
  • Optimized for high performance on Intel CPUs and GPUs. 
  • Allows the tradeoff of compute for communication performance to drive scalability of communication patterns.
  • Enables efficient implementations of collectives that are heavily used for neural network training, including all-gather, all-reduce, and reduce-scatter.
Download as Part of the Toolkit

oneCCL is included as part of the Intel® oneAPI Base Toolkit, which is a core set of tools and libraries for developing high-performance, data-centric applications across diverse architectures.

Download the Stand-Alone Version

A stand-alone download of oneCCL is available. You can download binaries from Intel or choose your preferred repository.

Runtime Version

Develop in the Cloud

Build and optimize oneAPI multiarchitecture applications using the latest optimized Intel® oneAPI and AI tools, and test your workloads across Intel® CPUs and GPUs. No hardware installations, software downloads, or configuration necessary. Free for 120 days with extensions possible.

 

Help oneCCL Evolve

oneCCL is part of the oneAPI industry standards initiative. We welcome you to participate.

 

Specification

Open Source Version (GitHub*)

Features

Common APIs to Support Deep Learning Frameworks

oneCCL exposes a collective API that supports:

  • Commonly used collective operations found in deep learning and machine learning workloads
  • Interoperability with SYCL* from the Khronos* Group

 

Deep Learning Optimizations

The runtime implementation enables several optimizations, including: 

  • Asynchronous progress for compute communication overlap
  • Dedication of one or more cores to ensure optimal network use
  • Message prioritization, persistence, and out-of-order execution
  • Collectives in low-precision data types

Specifications

Processors:
  • Intel® Xeon® processors
GPUs:
  • Intel® Processor Graphics Gen9 and higher
  • Xe Architecture
Memory
  • Dynamic RAM
  • Intel® Optane™ DC persistent memory
Host operating system:
  • Linux*
Target operating system:
  • Linux
Languages:
  • SYCL
  • C and C++

For more information, see the system requirements.

Compilers:
  • GNU Compiler Collection (GCC)*
Distributed environments:
  • MPI (MPICH-based)

Get Help

Your success is our success. Access these forum and GitHub resources when you need assistance.

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