XCCL: Scaling PyTorch Collectives to Exascale on Intel GPUs with TorchComms#
PyTorch Conference 2026 digest Program Adjacent pick 20–21 October 2026
Panagiotis Kourdis, Tanima Dey — Intel
- Why: A rare, detailed account of building and validating a new accelerator's collective backend at exascale — including the CI and testing strategy — useful as the reference playbook for any multi-vendor comm-stack work.
- What: XCCL, an in-tree Intel-GPU backend for TorchComms built on oneCCL, validated on Aurora (Argonne's exascale supercomputer): over 90% scaling efficiency across thousands of nodes running TorchTitan and other AI workloads. Covers bridging PyTorch collective semantics to oneCCL, a stream-ordered asynchronous execution model that overlaps compute and communication, CI combining mock-based validation with real Intel hardware, and comparisons against NCCL and RCCL. (from abstract only)
- How: Backend integration plus semantic bridging between PyTorch's collective API and oneCCL's execution model.
- Where applicable: Intel GPUs on Aurora — a specific supercomputer deployment; the transferable content is the backend-building and validation methodology, not the silicon.