In the field of data mining, clustering has shown to be an important technique. Numerous
clustering methods have been devised and put into practice, and most of them locate high …

The high-performance computing I/O stack has been complex due to multiple software
layers, the inter-dependencies among these layers, and the different performance tuning …

Although deep learning has made great progress in recent years, the exploding economic
and environmental costs of training neural networks are becoming unsustainable. To …

Nowdays, it is prevalent to train deep learning (DL) models in cloud-native platforms that
actively leverage containerization and orchestration technologies for high elasticity, low and …

GPU technology has been improving at an expedited pace in terms of size and performance,
empowering HPC and AI/ML researchers to advance the scientific discovery process …

Stochastic gradient descent (SGD) is the most prevalent algorithm for training Deep Neural
Networks (DNN). SGD iterates the input data set in each training epoch processing data …

Input pipelines, which ingest and transform input data, are an essential part of training
Machine Learning (ML) models. However, it is challenging to implement efficient input …

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 …

Checkpointing is an I/O intensive operation increasingly used by High-Performance
Computing (HPC) applications to revisit previous intermediate datasets at scale. Unlike the …

Recent years have seen a surge in deep learning approaches to accelerate numerical
solvers, which provide faithful but computationally intensive simulations of the physical …