Multi-messenger astronomy is one of NSF's thrust areas part of the "Big Ideas" initiative. This research area includes efforts that support gravitational wave, optical, x-ray, gamma-ray and neutrino observations from astrophysical systems. Signal detection and analysis is perhaps the most challenging parts of these efforts. CoCoA is a tunable GW analysis technique that can leverage source modeling to improve the sensitivity of a search, without sacrificing robustness against deviations from the expected waveform.
In this CyberTeams project, the CoCoA algorithm was implemented on a parallel-HPC environment and GPU-acceleration was explored. This allows for much higher throughput on important signal searches thus significantly benefiting detection efforts. Despite being heavily dependent on high performance computing, most existing gravitational wave analysis techniques don't leverage the full potential of modern architecture. This project has helped change that, by building modern tools into an analysis technique that hopes to make first-of-its-kind detections starting with the next gravitational wave observing run in Winter 2022-2023.
The approach taken towards parallelism and the lessons learned are applicable to a variety of other areas in science and engineering. Those outcomes may benefit other disciplines in a similar context -- increased throughput on relevant computations. The lack of GPU support and optimization is pervasive across many (if not most) disciplines that have pivoted to high performance computing in recent decades, and the framework developed during this progress is discipline agnostic. Even now, Chris has pivoted directly to working in applied mathematics – wholly unrelated to gravitational wave astronomy.
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The supported RCF: Chris Nadeau is very interested now in a career in the area of research computing owing to his experience in CyberTeams. The supported student was effectively retained i.e. will continue on to a STEM career as a direct outcome of this grant. Funded research opportunities appear to have a strong impact on students in STEM disciplines.
In addition, these student was mentored and trained on how research in physics (and other sciences) is conducted, the mathematical and technological tools involved, and how to overcome roadblocks and challenges when working with unknowns.
Given the outcome of this CyberTeams project, URI better appreciates the role that can played by students to support research computing efforts. URI is now building a student support team within its research computing department.
The project objectives related to signal search enhancement will improve the data-analysis capabilities of NSF's largest project LIGO and other associated gravitational wave observatories around the world. Improvements of the type this project will contribute may result in effectively higher- sensitivity for these instruments that would enhance their capabilities.
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Advancement of scientific efforts and the development of a STEM trained workforce has many established positive impacts on society beyond those particular areas. While difficult to quantify, we envision that in the long run there will be a tangible positive impact of such projects well beyond their domains.
Beyond the scientific benefits that can be obtained from advanced instrumentation such as HPC systems, it became clear that a student with the right combination of interests and background can positively impact the throughput of a research lab over a short-term engagement as long as there is a supporting team available to the student as a resource.
Additionally, such positive short-term engagements seemed to be sufficient to get the student enthusiastic about a career in research computing support.
At a technical level -- Bottlenecking occurs during transfer to and from GPUs that requires careful forethought about what processes to offload to the GPU. This was true even for processes that were highly optimized for GPU processing.
Improved throughput of multi-messenger astronomy signal search algorithms -- the results were quite promising, showing dramatic speedup in certain circumstances on numerous different computing platforms;
Positive impact from short-term student engagement on research lab throughput and student retention in a STEM discipline;
Workforce development and training in a STEM area (research computing).