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Found 4 papers, 0 papers with code
ScaleFold: Reducing AlphaFold Initial Training Time to 10 Hours
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.
Boosting the Convergence of Reinforcement Learning-based Auto-pruning Using Historical Data
Reinforcement learning (RL)-based auto-pruning has been further proposed to automate the DNN pruning process to avoid expensive hand-crafted work.
FusionStitching: Boosting Memory Intensive Computations for Deep Learning Workloads
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.
Sparse Persistent RNNs: Squeezing Large Recurrent Networks On-Chip
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.
