oneCCL Enables Distributed Machine Learning with oneAPI | Intel Software
7The Intel® oneAPI Collective Communications Library (oneCCL) allows you to efficiently train deep neural networks in a distributed computing environment. In addition to supporting SYCL* and several communication interfaces including MPI, it provides a set of collective communication operations for effective synchronization among the nodes. In this video, you will learn how to leverage the library for scaling out compute-intensive machine learning models across heterogeneous architectures from diverse vendors.
oneCCL enables adding data parallelism to deep neural networks for efficient training and scaling out the models in a multiarchitecture environment. It lets you harness the potential of the latest hardware including CPUs, GPUs, and other AI accelerators for distributed model training. It supports a variety of communication interfaces such as MPI, Libfacbrics/ Open Fabrics Interfaces (OFI), Ethernet, Cornelis Networks and InfiniBand. The library is a part of the open source oneAPI specification and the UXL Foundation. With a support for the SYCL programming framework, it also provides several collective communication options ensuring effective transfer and synchronization of model weights/gradients among the nodes involved in distributed training. Check out this video to get an overview of oneCCL and how you can take its advantage in a wide range of real-world AI application domains such as healthcare, robotics, aerospace, and a lot more.
You can download standalone version of the library or install it as a part of the Intel® oneAPI Base Toolkit. You can also test it on the Intel® Tiber™ AI Cloud platform.
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oneCCL Enables Distributed Machine Learning with oneAPI