PipeSwitch: Fast Pipelined Context Switching for Deep Learning Applications

By Zhihao Bai, Zhen Zhang, Yibo Zhu, Xin Jin. 2020

156 citations recorded by the source corpus.

Subfields: Efficiency & Compression

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Efficiency & Compression

1. Ping Luo
2. Yonghui Wu
3. Joseph Dean
4. Ion Stoica
5. Aakanksha Chowdhery
6. Ruoming Pang
7. Hyeontaek Lim
8. Quoc V. Le

Citations in the corpus

• Pathways: Asynchronous Distributed Dataflow for ML (2022, background): “Concurrent (time-multiplexed or overlapping) ML task execution (Gupta et al., 2018; Xiao et al., 2020; Bai et al., 2020; Yu and Chowd-hury, 2020; Wang et al., 2021; Lim et al., 2021) helps harvest idle resources within accelerators.”
• SHEPHERD: Serving DNNs in the Wild (2023, methodology): “PipeSwitch [21] allows preempting a training tasks to execute an inference task.”
• vPipe: A Virtualized Acceleration System for Achieving Efficient and Scalable Pipeline Parallel DNN Training (2022, background): “, Unified Memory can slow down a DNN’s execution by more than 1x [5]).”