1 code implementation • 31 Jan 2021 • Geoffrey X. Yu, Yubo Gao, Pavel Golikov, Gennady Pekhimenko
Our technique exploits the observation that, because DNN training consists of repetitive compute steps, predicting the execution time of a single iteration is usually enough to characterize the performance of an entire training process.
1 code implementation • 15 Aug 2020 • Geoffrey X. Yu, Tovi Grossman, Gennady Pekhimenko
Training a state-of-the-art deep neural network (DNN) is a computationally-expensive and time-consuming process, which incentivizes deep learning developers to debug their DNNs for computational performance.
