5 citations · 8 across the 10 of their papers we have counts for
11 papers
Covenant-72B: Pre-Training a 72B LLM with Trustless Peers Over-the-Internet
Joel Lidin, Amir Sarfi, Erfan Miahi +6
Recently, there has been increased interest in globally distributed training, which has the promise to both reduce training costs and democratize participation in building large-sc…
Characterizing Communication Patterns in Distributed Large Language Model Inference
Lang Xu, Kaushik Kandadi Suresh, Quentin Anthony +2
Large Language Models (LLMs) built on transformer architectures have transformed natural language processing, achieving remarkable performance across diverse applications. While di…
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…
Comparative Study of Large Language Model Architectures on Frontier
Junqi Yin, Avishek Bose, Guojing Cong +2
Large language models (LLMs) have garnered significant attention in both the AI community and beyond. Among these, the Generative Pre-trained Transformer (GPT) has emerged as the d…
The Case for Co-Designing Model Architectures with Hardware
Quentin Anthony, Jacob Hatef, Deepak Narayanan +6
While GPUs are responsible for training the vast majority of state-of-the-art deep learning models, the implications of their architecture are often overlooked when designing new d…
Exploiting Inter-Layer Expert Affinity for Accelerating Mixture-of-Experts Model Inference
Jinghan Yao, Quentin Anthony, Aamir Shafi +3
In large language models like the Generative Pre-trained Transformer, the Mixture of Experts paradigm has emerged as a powerful technique for enhancing model expressiveness and acc…
MCR-DL: Mix-and-Match Communication Runtime for Deep Learning
Quentin Anthony, Ammar Ahmad Awan, Jeff Rasley +5
In recent years, the training requirements of many state-of-the-art Deep Learning (DL) models have scaled beyond the compute and memory capabilities of a single processor, and nece…
Cross-layer Visualization and Profiling of Network and I/O Communication for HPC Clusters
Pouya Kousha, Quentin Anthony, Hari Subramoni +1
Understanding and visualizing the full-stack performance trade-offs and interplay between HPC applications, MPI libraries, the communication fabric, and the file system is a challe…
HyPar-Flow: Exploiting MPI and Keras for Scalable Hybrid-Parallel DNN Training using TensorFlow
Ammar Ahmad Awan, Arpan Jain, Quentin Anthony +2
To reduce training time of large-scale DNNs, scientists have started to explore parallelization strategies like data-parallelism, model-parallelism, and hybrid-parallelism. While d…