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We will share the latest updates to NCCL and how they can be used in PyTorch. We invite the community to share their feedback on challenges using NCCL at scale and ways to improve integration of NCCL with PyTorch applications.
Some of the important topics for community discussion include: - Symmetric memory support and GPU-initiated networking. - Copy-engine collectives and maximizing overlap of communication and computation for better end-to-end performance. - Profiling, debugging and tuning, as well as resilience (handling failed nodes without a restart).
Asma Farjallah is an AI Developer Technology Engineer at NVIDIA. Prior to her role as DevTech, she was part of the Solution Architect team at NVIDIA for 5 years and was part of the global energy team. Before joining NVIDIA, Asma worked for Intel for 4 years as an Application Engineer... Read More →
Gabrielle Talavera is the Product Manager for NCCL at NVIDIA, focused on shaping the product roadmap and improving the experience of teams building on GPU‑accelerated software. She joined NVIDIA in 2021 as a Solutions Architect, helping customers adopt NVIDIA software and debug... Read More →
Jeff Hammond is a Distinguished Engineer in the NCCL team at NVIDIA focused on user education and research outreach. His background is in parallel application and algorithm development, open-source software, and supercomputing architecture. Jeff has made significant contributions... Read More →
