AI integration is revolutionizing the landscape of HPC simulations, enhancing the
importance, use, and performance of AI-driven HPC workflows. This paper surveys the …

Real-time multi-task multi-model (MTMM) workloads, a new form of deep learning inference
workloads, are emerging for applications areas like extended reality (XR) to support …

Benchmarks are essential in the design of modern HPC installations, as they define key
aspects of system components. Beyond synthetic workloads, it is crucial to include real …

Recommendation systems are of crucial importance for a variety of modern apps and web
services, such as news feeds, social networks, e-commerce, search, etc. To achieve peak …

Successful discrimination of, isolation from, and recovery against short-circuit electrical faults
within microgrids having distributed energy resources (DERs) is challenging, as protection …

Dragonfly is an indispensable interconnect topology for exascale HPC systems. To link tens
of thousands of compute nodes at a reasonable cost, Dragonfly shares network resources …

Applications of machine learning (ML) are growing by the day for many unique and
challenging scientific applications. However, a crucial challenge facing these applications is …

Rapid adoption of machine learning (ML) technologies has led to a surge in power
consumption across diverse systems, from tiny IoT devices to massive datacenter clusters …

AlphaFold2 has been hailed as a breakthrough in protein folding. It can rapidly predict
protein structures with lab-grade accuracy. However, its training procedure is prohibitively …

Distributed deep training has become a significant consumer of bandwidth across
datacenter-scale networks. The diverse parallel strategies employed in deep training require …