Gabrielle Allen

Frank Löffler, Steven R. Brandt, Gabrielle Allen et al.

The Cactus Framework is an open-source, modular, portable programming environment for the collaborative development and deployment of scientific applications using high-performance computing. Its roots reach back to 1996 at the National Center for Supercomputer Applications and the Albert Einstein Institute in Germany, where its development jumpstarted. Since then, the Cactus framework has witnessed major changes in hardware infrastructure as well as its own community. This paper describes its endurance through these past changes and, drawing upon lessons from its past, also discusses future

E. A. Huerta, Gabrielle Allen, Igor Andreoni et al.

Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos. In this Expert Recommendation, we review the key challenges of real-time observations of gravitational wave sources and their electromagnetic and astroparticle counterparts, and make a number of recommendations to maximize their potential for scientific discovery. These recommendations refer to the design of scalable and computationally efficient machine learning algorithms; the cyber-infrastructure to numerically simulate astrophysical sources, and to process and interpret multi-messenger astrophysics data; the management of gravitational wave detections to trigger real-time alerts for electromagnetic and astroparticle follow-ups; a vision to harness future developments of machine learning and cyber-infrastructure resources to cope with the big-data requirements; and the need to build a community of experts to realize the goals of multi-messenger astrophysics.

Gabrielle Allen, Igor Andreoni, Etienne Bachelet et al.

This report provides an overview of recent work that harnesses the Big Data Revolution and Large Scale Computing to address grand computational challenges in Multi-Messenger Astrophysics, with a particular emphasis on real-time discovery campaigns. Acknowledging the transdisciplinary nature of Multi-Messenger Astrophysics, this document has been prepared by members of the physics, astronomy, computer science, data science, software and cyberinfrastructure communities who attended the NSF-, DOE- and NVIDIA-funded "Deep Learning for Multi-Messenger Astrophysics: Real-time Discovery at Scale" workshop, hosted at the National Center for Supercomputing Applications, October 17-19, 2018. Highlights of this report include unanimous agreement that it is critical to accelerate the development and deployment of novel, signal-processing algorithms that use the synergy between artificial intelligence (AI) and high performance computing to maximize the potential for scientific discovery with Multi-Messenger Astrophysics. We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics; (ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data; (iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups; (iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big Data Era; (v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics.

E. A. Huerta, Daniel George, Zhizhen Zhao et al.

We discuss the development of novel deep learning algorithms to enable real-time regression analysis for time series data. We showcase the application of this new method with a timely case study, and then discuss the applicability of this approach to tackle similar challenges across science domains.

Daniel S. Katz, Sou-Cheng T. Choi, Kyle E. Niemeyer et al.

This report records and discusses the Third Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE3). The report includes a description of the keynote presentation of the workshop, which served as an overview of sustainable scientific software. It also summarizes a set of lightning talks in which speakers highlighted to-the-point lessons and challenges pertaining to sustaining scientific software. The final and main contribution of the report is a summary of the discussions, future steps, and future organization for a set of self-organized working groups on topics including developing pathways to funding scientific software; constructing useful common metrics for crediting software stakeholders; identifying principles for sustainable software engineering design; reaching out to research software organizations around the world; and building communities for software sustainability. For each group, we include a point of contact and a landing page that can be used by those who want to join that group's future activities. The main challenge left by the workshop is to see if the groups will execute these activities that they have scheduled, and how the WSSSPE community can encourage this to happen.

Daniel S. Katz, Gabrielle Allen, Neil Chue Hong et al.

This technical report discusses the submission and peer-review process used by the Second Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE2) and the results of that process. It is intended to record both the alternative submission and program organization model used by WSSSPE2 as well as the papers associated with the workshop that resulted from that process.

Daniel S. Katz, Gabrielle Allen, Neil Chue Hong et al.

This technical report discusses the submission and peer-review process used by the First Workshop on on Sustainable Software for Science: Practice and Experiences (WSSSPE) and the results of that process. It is intended to record both this alternative model as well as the papers associated with the workshop that resulted from that process.