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Fast Video Multi-Style Transfer

Published in , 2020

We achieve as many as 120 stylization effects in a single model and show results on long-term videos that consist of thousands of frames.

Recommended citation: Wei Gao, Yijun Li, Yihang Yin, Ming-Hsuan Yang; WACV 2020.

Privacy-preserving collaborative learning with automatic transformation search

Published in , 2021

We design a novel search method to automatically discover qualified policies, which can significantly protect collaborative learning.

Recommended citation: Wei Gao, Shangwei Guo, Tianwei Zhang, Han Qiu, Yonggang Wen, Yang Liu; CVPR 2021 (oral).

CHRONUS: A Novel Deadline-aware Scheduler for Deep Learning Training Jobs

Published in , 2021

We present Chronus, an end-to-end scheduling system to provide deadline guarantee for SLO jobs and maximize the performance of best-effort jobs.

Recommended citation: Wei Gao, Zhisheng Ye, Peng Sun, Yonggang Wen, Tianwei Zhang; ACM SoCC 2021.

Titan: A Scheduler for Foundation Model Fine-tuning Workloads

Published in , 2022

We present Titan, an elastic end-to-end scheduling system for foundation model fine-tuning workloads in GPU datacenters.

Recommended citation: Wei Gao, Peng Sun, Yonggang Wen, Tianwei Zhang; ACM SoCC 2022.

Automatic Transformation Search Against Deep Leakage From Gradients

Published in , 2023

We first design two new metrics to quantify the impacts of transformations on data privacy and model usability. With the two metrics, we design a novel search method to automatically discover qualified policies from a given data augmentation library.

Recommended citation: Wei Gao, Xu Zhang, Shangwei Guo, Tianwei Zhang, Tao Xiang, Han Qiu, Yonggang Wen, Yang Liu; TPAMI 2023.

Deep Learning Workload Scheduling in GPU Datacenters: A Survey

Published in , 2024

This article surveys existing research efforts for both training and inference workloads. We primarily present how existing schedulers facilitate the respective workloads from the scheduling objectives and resource utilization manner. Finally, we discuss several promising future research directions including emerging DL workloads, advanced scheduling decision making, and underlying hardware resources.

Recommended citation: Zhisheng Ye, Wei Gao, Qinghao Hu, Peng Sun, Xiaolin Wang, Yingwei Luo, Tianwei Zhang, Yonggang Wen; ACM Computing Surveys 2024.

UniSched: A Unified Scheduler for Deep Learning Training Jobs with Different User Demands

Published in , 2024

In this work, we present UniSched, a unified scheduler to optimize different types of scheduling objectives (e.g., guaranteeing the deadlines of SLO jobs, minimizing the latency of best-effort jobs). Meanwhile, UniSched supports different job stopping criteria (e.g., iteration-based, performance-based).

Recommended citation: Wei Gao, Zhisheng Ye, Peng Sun, Tianwei Zhang, Yonggang Wen; Transactions on Computers 2024.

AutoSched: An Adaptive Self-configured Framework for Scheduling Deep Learning Training Workloads

Published in , 2024

We design AutoSched, a framework that can automatically, efficiently, and dynamically adjust the configuration parameters of DLT schedulers.

Recommended citation: Wei Gao, Xu Zhang, Shan Huang, Shangwei Guo, Peng Sun, Yonggang Wen, Tianwei Zhang; ACM International Conference on Supercomputing (ICS) 2024.

Ymir: A Scheduler for Foundation Model Fine-tuning Workloads in Datacenters

Published in , 2024

We propose Ymir, a scheduler to leverage the shared FM backbone architecture to expedite FMF workloads in GPU datacenters.

Recommended citation: Wei Gao, Weiming Zhuang, Minghao Li, Peng Sun, Yonggang Wen, Tianwei Zhang; ACM International Conference on Supercomputing (ICS) 2024.