XSEDE: Accelerating Scientific Discovery

By John Towns, T. Cockerill, M. Dahan, Ian T Foster, K. Gaither, Andrew S. Grimshaw, Victor Hazlewood, Scott D. Lathrop, D. Lifka, G. D. Peterson and 3 more. 2014

2,452 citations recorded by the source corpus.

Computing in science and engineering is now ubiquitous: digital technologies underpin, accelerate, and enable new, even transformational, research in all domains. Access to an array of integrated and well-supported high-end digital services is critical for the advancement of knowledge. Driven by community needs, the Extreme Science and Engineering Discovery Environment (XSEDE) project substantially enhances the productivity of a growing community of scholars, researchers, and engineers (collectively referred to as "scientists"' throughout this article) through access to advanced digital services that support open research. XSEDE's integrated, comprehensive suite of advanced digital services federates with other high-end facilities and with campus-based resources, serving as the foundation for a national e-science infrastructure ecosystem. XSEDE's e-science infrastructure has tremendous potential for enabling new advancements in research and education. XSEDE's vision is a world of digitally enabled scholars, researchers, and engineers participating in multidisciplinary collaborations to tackle society's grand challenges.

Subfields: Computer Networks and Communications, Information Systems, Information Systems and Management

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Citations in the corpus

I-TASSER-MR: automated molecular replacement for distant-homology proteins using iterative fragment assembly and progressive sequence truncation (2017, methodology): “Part of the simulation jobs is also run on the community-wide resource from the XSEDE super-computing cluster (24).”
PacGAN: The Power of Two Samples in Generative Adversarial Networks (2017, background): “com/fjxmlzn/PacGAN, and were run with support from [69, 54].”
Non-thermal particle acceleration in collisionless relativistic electron-proton reconnection (2016, methodology): “This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation (NSF) grant number ACI-1053575 (Towns et al. 2014), and in particular we used the Stampede supercomputer at the Texas Advanced Computing Center (TACC) at The University”
Buffet-Onset Constraint Formulation for Aerodynamic Shape Optimization (2017, methodology): “The computations were performed in the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1053575 [72].”
Looking Enhances Listening: Recovering Missing Speech Using Images (2020, methodology): “This work used the computational resources of the PSC Bridges cluster at Extreme Science and Engineering Discovery Environment (XSEDE) [22].”
HOOMD-blue: A Python package for high-performance molecular dynamics and hard particle Monte Carlo simulations (2013, methodology): “Software was validated and benchmarked on the Extreme Science and Engineering Discovery Environment (XSEDE) [58], which is supported by National Science Foundation grant number ACI-1053575 (XSEDE award DMR 140129); on resources of the Oak Ridge Leadership Computing Facility which is a DOE Office of ”
COFACTOR: improved protein function prediction by combining structure, sequence and protein–protein interaction information (2017, methodology): “Part of the method training and benchmarking work was done on the Extreme Science and Engineering Discovery Environment (XSEDE) (40) and the Open Science Grid.”
Multipoint Aerodynamic Shape Optimization Investigations of the Common Research Model Wing (2015, methodology): “All the computations were performed on the Stampede Compute Cluster at the University of Texas [36].”
AOGNets: Compositional Grammatical Architectures for Deep Learning (2017, methodology): “Some early experiments used the XSEDE [59] at the SDSC Comet GPU Nodes through allocation IRI180024 (XSEDE is supported by NSF grant ACI-1548562).”