2 papers
cs.DC2024
Rubick: Exploiting Job Reconfigurability for Deep Learning Cluster Scheduling
Xinyi Zhang, Hanyu Zhao, Wencong Xiao +5
The era of large deep learning models has given rise to advanced training strategies such as 3D parallelism and the ZeRO series. These strategies enable various (re-)configurable e…
cs.DC2024
Opara: Exploiting Operator Parallelism for Expediting DNN Inference on GPUs
Aodong Chen, Fei Xu, Li Han +4
GPUs have become the \emph{defacto} hardware devices for accelerating Deep Neural Network (DNN) inference workloads. However, the conventional \emph{sequential execution mode of DN…