Deep Learning (DL) has become fundamental to the advancement of several areas, such as
computer vision, natural language processing and expert systems. Utilizing DL techniques …

We present Monarch, a framework-agnostic storage middleware that transparently employs
storage tiering to accelerate Deep Learning (DL) training. It leverages existing storage tiers …

Due to convenience and usability, many deep learning (DL) jobs resort to the available
shared parallel file system (PFS) for storing and accessing training data when running in …

Deep Learning (DL) training requires efficient access to large collections of data, leading DL
frameworks to implement individual I/O optimizations to take full advantage of storage …

Deep learning training (DLT) applications exhibit unique I/O workload behaviors that pose
new challenges for storage system design. DLT is I/O intensive since data samples need to …

Massive scale deep learning enables HPC systems to finish the training of the large-scale
data sets (eg ImageNet) in several tens of seconds [1]. Therefore, even a small tail latency of …

DNN models trained with very large datasets can perform rich deep learning tasks with high
accuracy. However, feeding huge volumes of training data exerts significant pressure on IO …

The ability to create value from large-scale data is now an essential part of research and
driving technological development everywhere from everyday technology to life-saving …

Abstract Deep Learning, specifically Deep Neural Networks (DNNs), is stressing storage
systems in new ways, moving the training bottleneck to the data pipeline (fetching, pre …

SF-22-136 Deep learning has been shown as a successful method for various tasks, and its
popularity results in numerous open-source deep learning software tools. Deep learning has …