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Astronomical data reduction is usually done with processing pipelines that consist of a series of individual processing steps that can be executed stand-alone. These processing steps are then strung together into workflows and fed with data…
A wealth of cosmological and astrophysical information is expected from many ongoing and upcoming large-scale surveys. It is crucial to prepare for these surveys now and develop tools that can efficiently extract most information. We…
Optimization techniques play an important role in several scientific and real-world applications, thus becoming of great interest for the community. As a consequence, a number of open-source libraries are available in the literature, which…
The ultimate goal of this work is a real-time processing framework for ultrasound image reconstruction augmented with machine learning. To attain this, we have implemented WaveFlow - a set of ultrasound data acquisition and processing tools…
Fully automated astronomical data calibration and imaging pipelines are difficult to develop without a good prototyping method which permits to bridge the time between observatory commissioning and the moment when the special features and…
We have designed and implemented a novel way to process wide-field astronomical data within a distributed environment of hardware resources and humanpower. The system is characterized by integration of archiving, calibration, and…
The Data Science domain has expanded monumentally in both research and industry communities during the past decade, predominantly owing to the Big Data revolution. Artificial Intelligence (AI) and Machine Learning (ML) are bringing more…
Research in ultrasound imaging is limited in reproducibility by two factors: First, many existing ultrasound pipelines are protected by intellectual property, rendering exchange of code difficult. Second, most pipelines are implemented in…
Much of the progress made in time-domain astronomy is accomplished by relating observational multi-wavelength time series data to models derived from our understanding of physical laws. This goal is typically accomplished by dividing the…
Heterogeneous computing is one of the most important computational solutions to meet rapidly increasing demands on system performance. It typically allows the main flow of applications to be executed on a CPU while the most computationally…
pynucastro is a python library that provides visualization and analyze techniques to classify, construct, and evaluate nuclear reaction rates and networks. It provides tools that allow users to determine the importance of each rate in the…
BlackSky introduces Smartflow, a cloud-based framework enabling scalable spatiotemporal geospatial research built on open-source tools and technologies. Using STAC-compliant catalogs as a common input, heterogeneous geospatial data can be…
We present a case study of a cloud-based computational workflow for processing large astronomical data sets from the Murchison Widefield Array (MWA) cosmology experiment. Cloud computing is well-suited to large-scale, episodic computation…
With the growing number of gamma-ray monitors in operation, several research teams have adopted a strategy of joint operation and scientific duty to improve efficiency. A successful example is the GECAM-HXMT-SVOM (GHS) constellation…
Advances in sequencing techniques have led to exponential growth in biological data, demanding the development of large-scale bioinformatics experiments. Because these experiments are computation- and data-intensive, they require…
Large scale simulations of complex systems ranging from climate and astrophysics to crowd dynamics, produce routinely petabytes of data and are projected to reach the zettabytes level in the coming decade. These simulations enable…
The use of approximation is fundamental in computational science. Almost all computational methods adopt approximations in some form in order to obtain a favourable cost/accuracy trade-off and there are usually many approximations that…
As software systems increase in complexity, conventional monitoring methods struggle to provide a comprehensive overview or identify performance issues, often missing unexpected problems. Observability, however, offers a holistic approach,…
To increase performance and efficiency, systems use FPGAs as reconfigurable accelerators. A key challenge in designing these systems is partitioning computation between processors and an FPGA. An appropriate division of labor may be…
We present FORECAST, a new flexible and adaptable software package that performs forward modeling of the output of any cosmological hydrodynamical simulations to create a wide range of realistic synthetic astronomical images. With…