ScaMP: Scalable Meta-Parallelism for Deep Learning Search

Abstract

In this paper, we propose Scalable Meta-Parallelism for Deep Learning Search (ScaMP): a distributed Hyperparameter Optimization (HPO) and Neural Architecture Search (NAS) framework that supports out-of-core models with flexible parallelism schemes. SCaMP is integrated into the modern DL ecosystem, and enables both efficient parallel training of concurrent candidate architectures and aggregate device memory saturation via a powerful load balancing engine. SCaMP estimates the memory requirements of each candidate architecture and automatically applies the appropriate model-parallel degree and maximum batch size supported for the given candidate.We evaluate the benefits of our designs on synthetic training benchmarks and in training a state-of-the-art vision transformer model. We select transformers as a candidate DL model type and demonstrate a 29% improvement in end-to-end HPO time on 32 V100 GPUs on the Lassen and ThetaGPU HPC systems. Further, we demonstrate a reduction in the proportion of NAS time spent in communication from 28% to 15%. Finally, we thoroughly verify the correctness of SCaMP by training a state-of-the-art SwinIR model.