4 papers
NEST: Network- and Memory-Aware Device Placement For Distributed Deep Learning
Irene Wang, Vishnu Varma Venkata, Arvind Krishnamurthy +1
The growing scale of deep learning demands distributed training frameworks that jointly reason about parallelism, memory, and network topology. Prior works often rely on heuristic…
Flashlight: PyTorch Compiler Extensions to Accelerate Attention Variants
Bozhi You, Irene Wang, Zelal Su Mustafaoglu +5
Attention is a fundamental building block of large language models (LLMs), so there have been many efforts to implement it efficiently. For example, FlashAttention leverages tiling…
CATransformers: Carbon Aware Transformers Through Joint Model-Hardware Optimization
Irene Wang, Newsha Ardalani, Mostafa Elhoushi +6
Machine learning solutions are rapidly adopted to enable a variety of key use cases, from conversational AI assistants to scientific discovery. This growing adoption is expected to…
Characterizing the Efficiency of Distributed Training: A Power, Performance, and Thermal Perspective
Seokjin Go, Joongun Park, Spandan More +5
The rapid scaling of Large Language Models (LLMs) has pushed training workloads far beyond the limits of single-node analysis, demanding a deeper understanding of how these models…