TY - GEN
T1 - Revisiting Computation for Research
T2 - 2024 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2024
AU - Giordani, Jeremiah
AU - Xu, Ziyang
AU - Colby, Ella
AU - Ning, August
AU - Godala, Bhargav Reddy
AU - Chaturvedi, Ishita
AU - Zhu, Shaowei
AU - Chon, Yebin
AU - Chan, Greg
AU - Tan, Zujun
AU - Collier, Galen
AU - Halverson, Jonathan D.
AU - Deiana, Enrico Armenio
AU - Liang, Jasper
AU - Sossai, Federico
AU - Su, Yian
AU - Patel, Atmn
AU - Pham, Bangyen
AU - Greiner, Nathan
AU - Campanoni, Simone
AU - August, David I.
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - In the field of computational science, effectively supporting researchers necessitates a deep understanding of how they utilize computational resources. Building upon a decade-old survey that explored the practices and challenges of research computation, this study aims to bridge the understanding gap between providers of computational resources and researchers who rely on them. This study revisits key survey questions and gathers feedback on open-ended topics from over a hundred interviews. Quantitative analyses of present and past results illuminate the landscape of research computation. Qualitative analyses, including careful use of large language models, highlight trends and challenges with concrete evidence. Given the rapid evolution of computational science, this paper offers a toolkit with methodologies and insights to simplify future research and ensure ongoing examination of the landscape. This study, with its findings and toolkit, guides enhancements to computational systems, deepens understanding of user needs, and streamlines reassessment of the computational landscape.
AB - In the field of computational science, effectively supporting researchers necessitates a deep understanding of how they utilize computational resources. Building upon a decade-old survey that explored the practices and challenges of research computation, this study aims to bridge the understanding gap between providers of computational resources and researchers who rely on them. This study revisits key survey questions and gathers feedback on open-ended topics from over a hundred interviews. Quantitative analyses of present and past results illuminate the landscape of research computation. Qualitative analyses, including careful use of large language models, highlight trends and challenges with concrete evidence. Given the rapid evolution of computational science, this paper offers a toolkit with methodologies and insights to simplify future research and ensure ongoing examination of the landscape. This study, with its findings and toolkit, guides enhancements to computational systems, deepens understanding of user needs, and streamlines reassessment of the computational landscape.
UR - http://www.scopus.com/inward/record.url?scp=85214998715&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85214998715&partnerID=8YFLogxK
U2 - 10.1109/SC41406.2024.00076
DO - 10.1109/SC41406.2024.00076
M3 - Conference contribution
AN - SCOPUS:85214998715
T3 - International Conference for High Performance Computing, Networking, Storage and Analysis, SC
BT - Proceedings of SC 2024
PB - IEEE Computer Society
Y2 - 17 November 2024 through 22 November 2024
ER -