File Access Patterns of Distributed Deep Learning Applications

Nowadays, Deep Learning (DL) applications have become a necessary solution for analyzing and making predictions with big data in several areas. However, DL applications introduce heavy input/output (I/O) loads on computer systems. These types of applications, when running on distributed systems or distributed memory parallel systems, handle a large amount of information that must be read in the training stage. Inherently parallel and distributed systems and persistent file accesses can easily overwhelm traditional shared file systems and negatively impact application performance. In this way, the management of these applications constitutes a constant challenge due to their popularity in HPC systems. Scientific applications or simulators have traditionally been executed and are optimized for this type systems. Therefore, it is essential to identify the key factors involved in the I/O of a DL application to find the most appropriate form of configuration to minimize the impact of I/O on the performance of this type of application. In the present work, we present an analysis of the behavior of the patterns generated by I/O operations in the training stage of distributed deep learning applications. We selected two well-known datasets such as CIFAR and MNIST to describe file access patterns.