1 citations · 1 across the 8 of their papers we have counts for
8 papers
ScaleAcross Explorer: Exploring Communication Optimization for Scale-Across AI Model Training
Minghao Li, Alicia Golden, Samuel Hsia +14
The rapid scaling of large language model training requires distributing GPU resources across multiple data center buildings and regions. We refer to such paradigm as "scale-across…
The xPU-athalon: Quantifying the Competition of AI Acceleration
Alicia Golden, Carole-Jean Wu, Gu-Yeon Wei +1
The push for greater efficiency in AI computation has given rise to an array of accelerator architectures that increasingly challenge the GPU's long-standing dominance. In this wor…
KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta
Gang Liao, Hongsen Qin, Ying Wang +36
Making deep learning recommendation model (DLRM) training and inference fast and efficient is important. However, this presents three key system challenges - model architecture div…
PRISM: Probabilistic Runtime Insights and Scalable Performance Modeling for Large-Scale Distributed Training
Alicia Golden, Michael Kuchnik, Samuel Hsia +4
Large model training beyond tens of thousands of GPUs is an uncharted territory. At such scales, disruptions to the training process are not a matter of if, but a matter of when --…
Composer: A Search Framework for Hybrid Neural Architecture Design
Bilge Acun, Prasoon Sinha, Newsha Ardalani +7
Hybrid model architectures that combine computational primitives (e.g., Attention, MLP) in different ratios have shown promising performance beyond Transformers. Some studies have…
Is Flash Attention Stable?
Alicia Golden, Samuel Hsia, Fei Sun +8
Training large-scale machine learning models poses distinct system challenges, given both the size and complexity of today's workloads. Recently, many organizations training state-…
Generative AI Beyond LLMs: System Implications of Multi-Modal Generation
Alicia Golden, Samuel Hsia, Fei Sun +8
As the development of large-scale Generative AI models evolve beyond text (1D) generation to include image (2D) and video (3D) generation, processing spatial and temporal informati…
MAD Max Beyond Single-Node: Enabling Large Machine Learning Model Acceleration on Distributed Systems
Samuel Hsia, Alicia Golden, Bilge Acun +5
Training and deploying large-scale machine learning models is time-consuming, requires significant distributed computing infrastructures, and incurs high operational costs. Our ana…