✨PyTorch Distributed: Experiences on Accelerating Data Parallel Training

📝 Summary:
This paper details PyTorch's distributed data parallel module, which accelerates large-scale model training. It uses techniques like gradient bucketing and computation-communication overlap to achieve near-linear scalability with 256 GPUs.

🔹 Publication Date: Published on Jun 28, 2020

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2006.15704
• PDF: https://arxiv.org/pdf/2006.15704
• Github: https://github.com/pytorch/pytorch/blob/master/torch/nn/parallel/distributed.py

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#PyTorch #DistributedTraining #DeepLearning #Scalability #HPC

✨RDMA Point-to-Point Communication for LLM Systems

📝 Summary:
TransferEngine provides a uniform interface for flexible point-to-point communication in LLM systems, overcoming NIC-specific limitations. It bridges different hardware, providing high throughput for disaggregated inference, RL, and MoE tasks. This solution avoids hardware lock-in and complements...

🔹 Publication Date: Published on Oct 31

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.27656
• PDF: https://arxiv.org/pdf/2510.27656
• Github: https://github.com/perplexityai/pplx-garden

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#RDMA #LLM #HPC #AIInfrastructure #DistributedSystems