18 citations · 20 across the 4 of their papers we have counts for
4 papers
Strata: Hierarchical Context Caching for Long Context Language Model Serving
Zhiqiang Xie, Ziyi Xu, Mark Zhao +5
Large Language Models (LLMs) with expanding context windows face significant performance hurdles. While caching key-value (KV) states is critical for avoiding redundant computation…
SYMI: Efficient Mixture-of-Experts Training via Model and Optimizer State Decoupling
Athinagoras Skiadopoulos, Mark Zhao, Swapnil Gandhi +3
Mixture-of-Experts (MoE) models have become a widely-adopted solution to continue scaling model sizes without a corresponding linear increase in compute. During MoE model training,…
ReCycle: Resilient Training of Large DNNs using Pipeline Adaptation
Swapnil Gandhi, Mark Zhao, Athinagoras Skiadopoulos +1
Training large Deep Neural Network (DNN) models requires thousands of GPUs over the course of several days or weeks. At this scale, failures are frequent and can have a big impact…
cedar: Optimized and Unified Machine Learning Input Data Pipelines
Mark Zhao, Emanuel Adamiak, Christos Kozyrakis
The input data pipeline is an essential component of each machine learning (ML) training job. It is responsible for reading massive amounts of training data, processing batches of…