A Deep Learning Dataloader with Shared Data Preparation

Executing a family of Deep Neural Networks (DNNs) training jobs on the same or similar datasets in parallel is typical in current deep learning scenarios. It is time-consuming and resource-intensive because each job repetitively prepares (i.e., loads and preprocesses) the data independently, causing redundant consumption of I/O and computations. Although the page cache or a centralized cache component can alleviate the redundancies by reusing the data prep work, each job's data sampled uniformly at random presents a low sampling locality in the shared dataset that causes the heavy cache thrashing. Prior work tries to solve the problem by enforcing all training jobs iterating over the dataset in the same order and requesting each data in lockstep, leading to strong constraints: all jobs must have the same dataset and run simultaneously. In this paper, we propose a dependent sampling algorithm (DSA) and domain-specific cache policy to relax the constraints.