97 citations · 625 across the 24 of their papers we have counts for
4 papers · 1 filter
PipeDream: Fast and Efficient Pipeline Parallel DNN Training
Aaron Harlap, Deepak Narayanan, Amar Phanishayee +4
PipeDream is a Deep Neural Network(DNN) training system for GPUs that parallelizes computation by pipelining execution across multiple machines. Its pipeline parallel computing mod…
Parameter Hub: a Rack-Scale Parameter Server for Distributed Deep Neural Network Training
Liang Luo, Jacob Nelson, Luis Ceze +2
Distributed deep neural network (DDNN) training constitutes an increasingly important workload that frequently runs in the cloud. Larger DNN models and faster compute engines are s…
TBD: Benchmarking and Analyzing Deep Neural Network Training
Hongyu Zhu, Mohamed Akrout, Bojian Zheng +4
The recent popularity of deep neural networks (DNNs) has generated a lot of research interest in performing DNN-related computation efficiently. However, the primary focus is usual…
Parameter Box: High Performance Parameter Servers for Efficient Distributed Deep Neural Network Training
Liang Luo, Jacob Nelson, Luis Ceze +2
Most work in the deep learning systems community has focused on faster inference, but arriving at a trained model requires lengthy experiments. Accelerating training lets developer…