4 papers
CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training
Rezaul Karim, Austin Wen, Wang Zongzuo +3
The rapid growth in the size of large language models has necessitated the partitioning of computational workloads across accelerators such as GPUs, TPUs, and NPUs. However, these…
Distributed Hybrid Parallelism for Large Language Models: Comparative Study and System Design Guide
Hossam Amer, Rezaul Karim, Ali Pourranjbar +3
With the rapid growth of large language models (LLMs), a wide range of methods have been developed to distribute computation and memory across hardware devices for efficient traini…
EPAS: Efficient Training with Progressive Activation Sharing
Rezaul Karim, Maryam Dialameh, Yang Liu +2
We present a novel method for Efficient training with Progressive Activation Sharing (EPAS). This method bridges progressive training paradigm with the phenomenon of redundant QK (…
ECHO-LLaMA: Efficient Caching for High-Performance LLaMA Training
Maryam Dialameh, Rezaul Karim, Hossein Rajabzadeh +5
This paper introduces ECHO-LLaMA, an efficient LLaMA architecture designed to improve both the training speed and inference throughput of LLaMA architectures while maintaining its…