1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.DC2024★ 1 cited
Towards Universal Performance Modeling for Machine Learning Training on Multi-GPU Platforms
Zhongyi Lin, Ning Sun, Pallab Bhattacharya +3
Characterizing and predicting the training performance of modern machine learning (ML) workloads on compute systems with compute and communication spread between CPUs, GPUs, and ne…
cs.DC2023
Mystique: Enabling Accurate and Scalable Generation of Production AI Benchmarks
Mingyu Liang, Wenyin Fu, Louis Feng +5
Building large AI fleets to support the rapidly growing DL workloads is an active research topic for modern cloud providers. Generating accurate benchmarks plays an essential role…
cs.LG2022
Building a Performance Model for Deep Learning Recommendation Model Training on GPUs
Zhongyi Lin, Louis Feng, Ehsan K. Ardestani +5
We devise a performance model for GPU training of Deep Learning Recommendation Models (DLRM), whose GPU utilization is low compared to other well-optimized CV and NLP models. We sh…