Research Computing Infrastructure and Researcher Engagement: Notes from Neuroscience
Pages 139 - 143
Abstract
The advent of the Mortimer B. Zuckerman Mind Brain Behavior Institute (Zuckerman Institute) at Columbia University over a decade ago presented the opportunity to design a discipline-focused Research Computing (RC) group allowing for close collaboration with a relatively fixed number of neuroscience laboratories to enhance discovery. Experiences and observations related to customizing Zuckerman computing infrastructure, creating “task-based” services and systems, and engaging with researchers in our Institute are shared to inform others about establishing discipline-focused research computing teams. Case studies related to providing a GPU cluster service tailored to Institute needs and the evolution of infrastructure choices to hybrid designs allowing bursting to vendor-provided cloud services are reviewed. Future directions involving research software engineering and sharing whole data analysis pipelines are noted.
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References
[1]
2019. NIH Science and Technology Research Infrastructure for Discovery, Experimentation, and Sustainability (STRIDES) Initiative. Retrieved May 14, 2020 from https://datascience.nih.gov/strides
[2]
2020. US Research Software Engineer Association (US-RSE). Retrieved May 14, 2020 from https://us-rse.org
[3]
Rajendra Bose and Bruno Scap. 2017. Research Storage for Columbia’s Zuckerman Mind Brain Behavior Institute. an experience/position paper for the Workshop on Research Data Management Implementations. Arlington, VA. https://rdmi.uchicago.edu/papers/08212017163548_NSF_RDMI_CU_paper_20170821_final.pdf
[4]
Jim Bottum et al.2017. The Future of Cloud for Academic Research Computing. Results of an NSF-Supported Workshop, Entitled “Cloud Forward” Supported by NSF ACI/CSE Award 1632037. https://www.microsoft.com/en-us/research/publication/the-future-of-cloud-for-academic-research-computing/
[5]
Dennis Gannon. 2018. The State of the Cloud for Science-2018. Technical Report. https://doi.org/10.13140/RG.2.2.27664.30729
[6]
Russ Poldrack. 2019. Computing models for a neuroimaging lab. Retrieved May 14, 2020 from http://www.russpoldrack.org/2019/12/computing-models-for-neuroimaging-lab.html
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Published In
July 2020
556 pages
ISBN:9781450366892
DOI:10.1145/3311790
Copyright © 2020 ACM.
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Association for Computing Machinery
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Published: 26 July 2020
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- Refereed limited
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PEARC '20: Practice and Experience in Advanced Research Computing
July 26 - 30, 2020
OR, Portland, USA
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Overall Acceptance Rate 133 of 202 submissions, 66%
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