Large-scale machine learning (ML) models rely on extremely large datasets to learn their
exponentially growing number of parameters. While these models achieve unprecedented …

Near-storage data processing and computational storage have recently received
considerable attention from the industry as energy-and cost-efficient ways to improve system …

We observe three problems in existing storage and caching systems for deep-learning
training (DLT) tasks:(1) accessing a dataset containing a large number of small files takes a …

A growing number of Machine Learning Frameworks recently made Deep Learning
accessible to a wider audience of engineers, scientists, and practitioners, by allowing …

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 …

I/O for Deep Learning at Scale Page 1 I/O for Deep Learning at Scale Quincey Koziol Principal
Data Architect, NERSC koziol@lbl.gov IXPUG, September 24, 2019 Page 2 Acknowledgments …

Computational Storage Devices (CSD) with near-storage acceleration is gaining popularity
for data-intensive applications, by moving power-efficient hardware acceleration closer to …

Near-data processing (NDP) refers to augmenting memory or storage with processing
power. Despite its potential for acceleration computing and reducing power requirements …

Training deep neural network (DNN) models is a resource-intensive, iterative process. For
this reason, nowadays, complex optimizers like Adam are widely adopted as it increases the …

The training dataset used in deep neural networks (DNNs) keeps on increasing. When a
training dataset grows larger, the reading performance of such a large training dataset …