2 citations · 6 across the 16 of their papers we have counts for
17 papers
Accelerating Data Preprocessing for Efficient Vision Model Inference on Jetson Edge Device
Tian Chen, Nawras Alnaasan, Jinghan Yao +4
Data preprocessing is a crucial part of deep learning workflows on edge devices. However, decoding data saved in JPEG format is very compute-intensive and occupies a major portion…
NCCL EP: Towards a Unified Expert Parallel Communication API for NCCL
Amos Goldman, Nimrod Boker, Maayan Sheraizin +15
Mixture-of-Experts (MoE) architectures have become essential for scaling large language models, driving the development of specialized device-initiated communication libraries such…
GPU-Initiated Networking for NCCL
Khaled Hamidouche, John Bachan, Pak Markthub +6
Modern AI workloads, especially Mixture-of-Experts (MoE) architectures, increasingly demand low-latency, fine-grained GPU-to-GPU communication with device-side control. Traditional…
Scaling Large Language Model Training on Frontier with Low-Bandwidth Partitioning
Lang Xu, Quentin Anthony, Jacob Hatef +4
Scaling up Large Language Model(LLM) training involves fitting a tremendous amount of training parameters across a limited number of workers. However, methods like ZeRO-3 that dras…
Accelerating Large Language Model Training with Hybrid GPU-based Compression
Lang Xu, Quentin Anthony, Qinghua Zhou +5
Data Parallelism (DP), Tensor Parallelism (TP), and Pipeline Parallelism (PP) are the three strategies widely adopted to enable fast and efficient Large Language Model (LLM) traini…
Demystifying the Communication Characteristics for Distributed Transformer Models
Quentin Anthony, Benjamin Michalowicz, Jacob Hatef +5
Deep learning (DL) models based on the transformer architecture have revolutionized many DL applications such as large language models (LLMs), vision transformers, audio generation…