BOOM: Use your Desktop to Accurately Predict the Performance of Large Deep Neural Networks

The intensive computational requirements of training deep neural networks (DNNs) have significantly driven the adoption of DNN accelerators like Graph Processing Units (GPU). However, selecting the most suitable GPU from all candidates with drastically different specifications and prices is still a challenging problem. While directly measuring the performance of DNN training tasks on every candidate is prohibitive, and not always available due to hardware shortage, an accurate performance predictor can assist in the decision-making. However, most existing performance predictors cannot predict the GPU memory footprint in an accurate, generalizable, and interpretable manner, which is crucial to the feasibility and performance of running the DNN model on real GPUs. Moreover, many optimizations for DNN training, such as mixed precision training and checkpointing, can significantly impact performance. However, such hardware-dependent optimizations are not considered by existing performance predictors.