4 papers · 1 filter
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…
Accelerating the Low-Rank Decomposed Models
Habib Hajimolahoseini, Walid Ahmed, Austin Wen +1
Tensor decomposition is a mathematically supported technique for data compression. It consists of applying some kind of a Low Rank Decomposition technique on the tensors or matrice…
GQKVA: Efficient Pre-training of Transformers by Grouping Queries, Keys, and Values
Farnoosh Javadi, Walid Ahmed, Habib Hajimolahoseini +7
Massive transformer-based models face several challenges, including slow and computationally intensive pre-training and over-parametrization. This paper addresses these challenges…
SwiftLearn: A Data-Efficient Training Method of Deep Learning Models using Importance Sampling
Habib Hajimolahoseini, Omar Mohamed Awad, Walid Ahmed +8
In this paper, we present SwiftLearn, a data-efficient approach to accelerate training of deep learning models using a subset of data samples selected during the warm-up stages of…