Function-as-a-Service (FaaS) platforms have recently gained rapid popularity. Many stateful
applications have been migrated to FaaS platforms due to their ease of deployment …

Disaggregated memory is a promising approach that addresses the limitations of traditional
memory architectures by enabling memory to be decoupled from compute nodes and …

Graph neural networks (GNNs) are a class of Deep Learning models used in designing
atomistic materials for effective screening of large chemical spaces. To ensure robust …

Tightly-coupled HPC systems have rigid memory allocation and can result in expensive
memory resource underutilization. As novel memory and network technologies mature …

The shift towards high-bandwidth networks driven by AI workloads in data centers and HPC
clusters has unintentionally aggravated network latency, adversely affecting the …

Although differential privacy (DP) is used to obfuscate local information and avoid data
leakage, very little research exists on the neural network model performance with applied …

As next-generation experimental and observational instruments for scientific research are
being deployed with higher resolutions and faster data capture rates, the fundamental …

To meet increasing data access performance demands of applications run on high-
performance computing (HPC) systems, an efficient design of HPC storage file system is …

In distributed deep learning, the improper use of the collective communication library can
lead to a decline in deep learning performance due to increased communication time …

Newly developed machine learning technology is promising to profoundly impact high-
performance computing, with the potential to significantly accelerate scientific discoveries …