Deep learning training on cloud platforms usually follows the tradition of the separation of storage and computing. The training executes on a compute cluster equipped with GPUs/TPUs while reading data from a separate cluster hosting the storage service. To alleviate the potential bottleneck, a training cluster usually leverages its local storage as a cache to reduce the remote IO from the storage cluster. However, existing deep learning schedulers do not manage storage resources thus fail to consider the diverse caching effects across different training jobs. This could degrade scheduling quality significantly. To address this issue, we present SiloD, a scheduling framework that co-designs the cluster scheduler and the cache subsystems for deep learning training. SiloD treats cache and remote IO as first-class resources and can integrate different state-of-the-art deep learning scheduling policies in a unified scheduling framework. To achieve this, SiloD develops an enhanced job performance estimator to help different schedulers to jointly consider the impact of storage and compute resource allocation while preserving their respective scheduling objectives. The SiloD-enhanced performance estimator leverages the unique data access pattern of deep learning training to develop a closed-form analytic model that captures the diverse cache / remote IO requirements from different training jobs. Evaluations show that SiloD improves the average job completion time, cluster utilization, and fairness by up to 7.4x, 2.57x, and 1.89x, respectively, compared to different combinations of cache systems and cluster schedulers where they operate independently.

Zhengyi Zhao, Shubo Zhang, Yuxi Zhang, Yanxi Zhao, Yifan Zhang, Zezhong Wang, Huimin Wang, Yutian Zhao, Bin Liang, Yefeng Zheng, Binyang Li, Kam-Fai Wong, Xian Wu. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

With the popularity of social media, there has been an increasing interest in user profiling and its applications nowadays. This paper presents our system named UIR-SIST for User Profiling Technology Evaluation Campaign in SMP CUP 2017. UIR-SIST aims to complete three tasks, including keywords extraction from blogs, user interests labeling and user growth value prediction. To this end, we first extract keywords from a user's blog, including the blog itself, blogs on the same topic and other blogs published by the same user. Then a unified neural network model is constructed based on a convolutional neural network (CNN) for user interests tagging. Finally, we adopt a stacking model for predicting user growth value. We eventually receive the sixth place with evaluation scores of 0.563, 0.378 and 0.751 on the three tasks, respectively.

Zhengyi Zhao, Shubo Zhang, Zezhong Wang, Huimin Wang, Yutian Zhao, Bin Liang, Yefeng Zheng, Binyang Li, Kam-Fai Wong, Xian Wu. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.