Author

Hongyi Zeng

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#machine learning Preprint Oct 2026

Architecting for Hardware Diversity: Meta's End-to-End Co-Design for High-Performance AI Training on AMD GPUs

Meta deploys a heterogeneous fleet comprising NVIDIA GPUs, AMD GPUs, and proprietary MTIA accelerators to reduce costs and mitigate vendor lock-in. However, the extensive history of NVIDIA-specific optimizations hinders performance portability to other hardware. This paper presents our methods for optimizing alternativ...

Hao-Yu Zhang, Arshad Rahman, Adnan Aziz et al. · 0 citations

Preprint Aug 2026

HCCL: Collective Communication for Meta Training and Inference Accelerators

HCCL, a collective communication library co-designed with Meta's MTIA 300 accelerator, the first Meta chip to integrate backend networking directly on chip package, and describes collective designs that improve compute-communication pipelining for latency-sensitive workloads are presented.

W. Bland, Tiago Antunes, Lars Paul Huse et al. · 0 citations

Book Open access Aug 2026

Connecting 100K+ GPUs: Building the Communication Stack for Large-Scale LLM Training

This work presents Meta's network architecture and software stack designed to support one of the world's largest RoCE fabrics, currently connecting over 100,000 GPUs across multiple datacenter buildings, and introduces a scalable initialization strategy that reduces startup times by 11× via eager process group creation...

Hongyi Zeng, Min Si, Pavan Balaji et al. · Conference on Applications,... · 0 citations