3 papers
cs.DC2025
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,…
cs.DC2025
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…
cs.LG2024
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…