306 citations · 564 across the 35 of their papers we have counts for
4 papers · 1 filter
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…
EasyScale: Accuracy-consistent Elastic Training for Deep Learning
Mingzhen Li, Wencong Xiao, Biao Sun +9
Distributed synchronized GPU training is commonly used for deep learning. The resource constraint of using a fixed number of GPUs makes large-scale training jobs suffer from long q…
FPGA Stream-Monitoring of Real-time Properties
Jan Baumeister, Bernd Finkbeiner, Maximilian Schwenger +1
An essential part of cyber-physical systems is the online evaluation of real-time data streams. Especially in systems that are intrinsically safety-critical, a dedicated monitoring…
BigDL: A Distributed Deep Learning Framework for Big Data
Jason Dai, Yiheng Wang, Xin Qiu +17
This paper presents BigDL (a distributed deep learning framework for Apache Spark), which has been used by a variety of users in the industry for building deep learning application…