Deep Learning (DL) has had an immense success in the recent past, leading to state-of-the-
art results in various domains, such as image recognition and natural language processing …

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 …

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 …

Fetching a large amount of DNN training data from storage systems incurs long I/O latency
and fetch stalls of GPUs. Importance sampling in DNN training can reduce the amount of …

Scientific communities are increasingly adopting deep learning (DL) models in their
applications to accelerate scientific discovery processes. However, with rapid growth in the …

Deep learning (DL) has become a key technique for solving complex problems in scientific
research and discovery. DL training for science is substantially challenging because it has to …

We propose an HPC network architecture with co-packaged optics enabling 128-port 51.2-
Tb/s switches. Simulations for a> 34,000-GPU system show up to 11.2 x throughput …

Cloud computing provides a powerful yet low-cost environment for distributed deep learning
workloads. However, training complex deep learning models often requires accessing large …

Large scale machine learning (ML) and deep learning (DL) platforms face challenges when
integrated with deduplication enabled storage clusters. In the quest to achieve smart and …

We investigate the advantages of using co-packaged optics in next-generation data center
and AI supercomputer networks. The increased escape bandwidth offered by co-packaged …