M Wang, C Meng, G Long, C Wu… - … on workload …, 2019 - ieeexplore.ieee.org
… of these workloads, and more importantly, the training performance … , we characterize deep learningtrainingworkloads from … of various workloads using different training architectures, to …
Q Zhang, LT Yang, Z Yan, Z Chen… - IEEE transactions on …, 2018 - ieeexplore.ieee.org
… learning model often includes a great number of parameters. In this paper, an efficient deeplearning … is proposed to predict the cloud workload for industry informatics. In the proposed …
… In this paper we present a detailed workload characterization … cluster utilization for DNN trainingworkloads on multi-tenant … , and (3) failures during training. Based on our experience …
Load imbalance pervasively exists in distributed deeplearningtraining systems, either caused by the inherent imbalance in learned tasks or by the system itself. Traditional …
W Gao, X Zhang, S Huang, S Guo, P Sun… - Proceedings of the 38th …, 2024 - dl.acm.org
… -arrival workloads. In detail, we analyze the historical workloads in the workload repository … , and then adopt FFT to extract the periodic workload submission. To generate future-arrival …
… However, many multi-model workloads are unable to … optimize multi-model deeplearning workloads. HiveMind optimizes a “… tuning and multi-model inference workloads by up to 10× on …
P Gu, X Xie, S Li, D Niu, H Zheng… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
… Second, many of these DNN trainingworkloads are … of these workloads, we conduct a case study on a Tesla V100 GPU [4] using six representative DNN training data center workloads […
… increase the diversity of job workloads to enable more general … to benefit the community of deeplearning systems. Based on the … Then we describe the DL workloads running in this …
… on allocating heterogeneous resources for DNN trainingworkloads, we believe that Gavel can be used for nonDNN workloads as well. Other workloads that are amenable to GPU …