no code implementations • 17 Apr 2024 • Feiwen Zhu, Arkadiusz Nowaczynski, Rundong Li, Jie Xin, Yifei Song, Michal Marcinkiewicz, Sukru Burc Eryilmaz, Jun Yang, Michael Andersch
In this work, we conducted a comprehensive analysis on the AlphaFold training procedure based on Openfold, identified that inefficient communications and overhead-dominated computations were the key factors that prevented the AlphaFold training from effective scaling.
no code implementations • 16 Jul 2021 • Jiandong Mu, Mengdi Wang, Feiwen Zhu, Jun Yang, Wei Lin, Wei zhang
Reinforcement learning (RL)-based auto-pruning has been further proposed to automate the DNN pruning process to avoid expensive hand-crafted work.
no code implementations • 23 Sep 2020 • Zhen Zheng, Pengzhan Zhao, Guoping Long, Feiwen Zhu, Kai Zhu, Wenyi Zhao, Lansong Diao, Jun Yang, Wei. Lin
We show in this work that memory intensive computations can result in severe performance problems due to off-chip memory access and CPU-GPU context switch overheads in a wide range of deep learning models.
no code implementations • ICLR 2018 • Feiwen Zhu, Jeff Pool, Michael Andersch, Jeremy Appleyard, Fung Xie
Recurrent Neural Networks (RNNs) are powerful tools for solving sequence-based problems, but their efficacy and execution time are dependent on the size of the network.