The key principle of physical layer security (PLS) is to permit the secure transmission of
confidential data using efficient signal-processing techniques. Also, deep learning (DL) has …

The efficiency of Deep Learning (DL) training jobs is critically dependent on dataloaders,
which facilitate the transfer of data from storage to DL-accelerated hardware during training …

As many artificial neural libraries are develo** the deep learning algorithm and
implementing it became accessible to anyone. This study points out the disparity of …

With the rapid evolution of the Internet, Internet of Things (IoT), and geographic information
systems (GIS), spatio-temporal Big Data (STBD) is experiencing exponential growth …

Abstract Machine learning techniques have become ubiquitous both in industry and
academic applications. Increasing model sizes and training data volumes necessitate fast …

The Deep Learning (DL) training process consists of multiple phases—data augmentation,
training, and validation of the trained model. Traditionally, these phases are executed either …

Artificial intelligence (AI) and Machine learning (ML) workloads are an increasingly larger
share of the compute workloads in traditional High-Performance Computing (HPC) centers …

With the ever-increasing computing power of supercomputers and the growing scale of
scientific applications, the efficiency of MPI collective communications turns out to be a …

Deep neural networks and deep learning are becoming important and popular techniques in
modern services and applications. The training of these networks is computationally …

GPU-aware collective communication has become a major bottleneck for modern computing
platforms as GPU computing power rapidly rises. A traditional approach is to directly …