HyperTune: Dynamic Hyperparameter Tuning For Efficient Distribution of DNN Training Over Heterogeneous Systems
This addresses performance and energy inefficiencies in distributed training for systems with heterogeneous processors or computational storage devices, offering a domain-specific incremental improvement.
The paper tackles the problem of inefficient distributed DNN training on heterogeneous systems by introducing Stannis, a framework that dynamically tunes hyperparameters, resulting in up to 3.1x performance improvement and 2.45x reduction in energy consumption.
Distributed training is a novel approach to accelerate Deep Neural Networks (DNN) training, but common training libraries fall short of addressing the distributed cases with heterogeneous processors or the cases where the processing nodes get interrupted by other workloads. This paper describes distributed training of DNN on computational storage devices (CSD), which are NAND flash-based, high capacity data storage with internal processing engines. A CSD-based distributed architecture incorporates the advantages of federated learning in terms of performance scalability, resiliency, and data privacy by eliminating the unnecessary data movement between the storage device and the host processor. The paper also describes Stannis, a DNN training framework that improves on the shortcomings of existing distributed training frameworks by dynamically tuning the training hyperparameters in heterogeneous systems to maintain the maximum overall processing speed in term of processed images per second and energy efficiency. Experimental results on image classification training benchmarks show up to 3.1x improvement in performance and 2.45x reduction in energy consumption when using Stannis plus CSD compare to the generic systems.