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The Pan-STARRS Data Processing System is responsible for the steps needed to downloaded, archive, and process all images obtained by the Pan-STARRS telescopes, including real-time detection of transient sources such as supernovae and moving…
The impressive progress in data rate capabilities, pattern recognition, and spatial resolution of current detectors in experimental particle physics has been possible thanks to the availability of sophisticated analog processors combined…
Dynamo is a full-stack software solution for scientific data management. Dynamo's architecture is modular, extensible, and customizable, making the software suitable for managing data in a wide range of installation scales, from a few…
Fast, incremental evolution of physics instrumentation raises the question of efficient software abstraction and transferability of algorithms across similar technologies. This contribution aims to provide an answer by introducing Track…
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…
As spatial and temporal resolutions of scientific instruments improve, the explosion in the volume of data produced is becoming a key challenge. It can be a critical bottleneck for integration between scientific instruments at the edge and…
The increased bandwidth coupled with the large numbers of antennas of several new radio telescope arrays has resulted in an exponential increase in the amount of data that needs to be recorded and processed. In many cases, it is necessary…
The rapid growth of data-intensive applications such as generative AI, scientific simulations, and large-scale analytics is driving modern supercomputers and data centers toward increasingly heterogeneous and tightly integrated…
The recent explosion of recorded digital data and its processed derivatives threatens to overwhelm researchers when analysing their experimental data or when looking up data items in archives and file systems. While current hardware…
In modeling time series data, we often need to augment the existing data records to increase the modeling accuracy. In this work, we describe a number of techniques to extract dynamic information about the current state of a large…
Extracting information from big data sets, both real and simulated, is a modern hallmark of the physical sciences. In practice, students face barriers to learning ``Big Data'' methods in undergraduate physics and astronomy curricula. As an…
As the need for more computing power grows, traditional methods are hitting limits. To boost performance, we're expanding Central Processing Unit (CPU) capabilities and using specialized hardware accelerators. For example, mobile devices…
Novel compute systems are an emerging research topic, aiming towards building next-generation compute platforms. For these systems to thrive, they need to be provided as research infrastructure to allow acceptance and usage by a large…
The recent rapid increase in demand for data processing has resulted in the need for novel machine learning concepts and hardware. Physical reservoir computing and an extreme learning machine are novel computing paradigms based on physical…
Atom probe tomography (APT) has matured to a versatile nanoanalytical characterization tool with applications that range from materials science to geology and possibly beyond. Already, well over 100 APT microscopes exist worldwide.…
As Deep Neural Networks (DNNs) have become an increasingly ubiquitous workload, the range of libraries and tooling available to aid in their development and deployment has grown significantly. Scalable, production quality tools are freely…
Digital twins are transforming the way we monitor, analyze, and control physical systems, but designing architectures that balance real-time responsiveness with heavy computational demands remains a challenge. Cloud-based solutions often…
Understanding land cover holds considerable potential for a myriad of practical applications, particularly as data accessibility transitions from being exclusive to governmental and commercial entities to now including the broader research…
The ability to timely process significant amounts of continuously updated spatial data is mandatory for an increasing number of applications. In this paper we focus on a specific data-intensive problem concerning the repeated processing of…
As deep learning models scale, they become increasingly competitive from domains spanning from computer vision to natural language processing; however, this happens at the expense of efficiency since they require increasingly more memory…