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Interactive urgent computing is a small but growing user of supercomputing resources. However there are numerous technical challenges that must be overcome to make supercomputers fully suited to the wide range of urgent workloads which…
Modern networks carry increasingly diverse and encrypted traffic types that demand classification techniques beyond traditional port-based and payload-based methods. This tutorial provides a practical, end-to-end guide to building…
High performance computing (HPC) has driven collaborative science discovery for decades. Exascale computing platforms, currently in the design stage, will be deployed around 2022. The next generation of supercomputers is expected to utilize…
Number of connected devices is steadily increasing and these devices continuously generate data streams. Real-time processing of data streams is arousing interest despite many challenges. Clustering is one of the most suitable methods for…
Artificial intelligence (AI) and high-performance computing (HPC) are rapidly becoming the engines of modern science. However, their joint effect on discovery has yet to be quantified at scale. Drawing on metadata from over five million…
The rapid evolution of Cyber-Physical Systems (CPS) across various domains like mobility systems, networked control systems, sustainable manufacturing, smart power grids, and the Internet of Things necessitates innovative solutions that…
Exponential increases in scientific experimental data are outstripping the rate of progress in silicon technology. As a result, heterogeneous combinations of architectures and process or device technologies are increasingly important to…
Recent advancements in detector technology have significantly increased the size and complexity of experimental data, and high-performance computing (HPC) provides a path towards more efficient and timely data processing. However, movement…
High Performance Distributed Computing is essential to boost scientific progress in many areas of science and to efficiently deploy a number of complex scientific applications. These applications have different characteristics that require…
While detailed resource usage monitoring is possible on the low-level using proper tools, associating such usage with higher-level abstractions in the application layer that actually cause the resource usage in the first place presents a…
The area of online machine learning in big data streams covers algorithms that are (1) distributed and (2) work from data streams with only a limited possibility to store past data. The first requirement mostly concerns software…
In design of new power transformers, reliable and efficient tools are required to expedite research and development processes. Some of these tools are used to interpret the data obtained from the transformer tests for better judgement about…
Pulsar data analysis pipelines have historically been comprised of bespoke software systems, supporting the off-line analysis of data. However modern data acquisition systems are making off-line analyses impractical. They often output…
Growing interest in Artificial Intelligence (AI) has resulted in a surge in demand for faster methods of Machine Learning (ML) model training and inference. This demand for speed has prompted the use of high performance computing (HPC)…
High Performance Computing (HPC) applications are essential for scientists and engineers to create and understand models and their properties. These professionals depend on the execution of large sets of computational jobs that explore…
The scale of scientific High Performance Computing (HPC) and High Throughput Computing (HTC) has increased significantly in recent years, and is becoming sensitive to total energy use and cost. Energy-efficiency has thus become an important…
In chemical processing and bioprocessing, conventional online sensors are limited to measure only basic process variables like pressure and temperature, pH, dissolved O and CO$_2$ and viable cell density (VCD). The concentration of other…
Massive upgrades to science infrastructure are driving data velocities upwards while stimulating adoption of increasingly data-intensive analytics. While next-generation exascale supercomputers promise strong support for I/O-intensive…
AI integration is revolutionizing the landscape of HPC simulations, enhancing the importance, use, and performance of AI-driven HPC workflows. This paper surveys the diverse and rapidly evolving field of AI-driven HPC and provides a common…
There is an ever-increasing need for computational power to train complex artificial intelligence (AI) & machine learning (ML) models to tackle large scientific problems. High performance computing (HPC) resources are required to…