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Cataloging the Visible Universe through Bayesian Inference at Petascale [article]

Jeffrey Regier, Kiran Pamnany, Keno Fischer, Andreas Noack, Maximilian Lam, Jarrett Revels, Steve Howard, Ryan Giordano, David Schlegel, Jon McAuliffe, Rollin Thomas, Prabhat
2018 arXiv   pre-print
We construct an astronomical catalog from 55 TB of imaging data using Celeste, a Bayesian variational inference code written entirely in the high-productivity programming language Julia.  ...  Astronomical catalogs derived from wide-field imaging surveys are an important tool for understanding the Universe.  ...  We owe an enormous debt to the Julia and LLVM open source communities. In particular, we thank Jeff Bezanson, Jameson Nash and Yichao Yu.  ... 
arXiv:1801.10277v1 fatcat:eig2kwxqhrfwfkwnnzk4be45qe

Learning an Astronomical Catalog of the Visible Universe through Scalable Bayesian Inference [article]

Jeffrey Regier, Kiran Pamnany, Ryan Giordano, Rollin Thomas, David Schlegel, Jon McAuliffe, Prabhat
2016 arXiv   pre-print
Celeste is a procedure for inferring astronomical catalogs that attains state-of-the-art scientific results.  ...  To date, Celeste has been scaled to at most hundreds of megabytes of astronomical images: Bayesian posterior inference is notoriously demanding computationally.  ...  The authors express their gratitude to Tina Declerck, Doug Jacobsen, David Paul and Zhengi Zhao who helped make the results presented in this paper possible.  ... 
arXiv:1611.03404v1 fatcat:yqrvmlv3gvdevciz72gc5rtake

Approximate Inference for Constructing Astronomical Catalogs from Images [article]

Jeffrey Regier, Andrew C. Miller, David Schlegel, Ryan P. Adams, Jon D. McAuliffe, Prabhat
2019 arXiv   pre-print
The MCMC procedure excels at quantifying uncertainty, while the VI procedure is 1000 times faster.  ...  On a supercomputer, the VI procedure efficiently uses 665,000 CPU cores to construct an astronomical catalog from 50 terabytes of images in 14.6 minutes, demonstrating the scaling characteristics necessary  ...  Bayesian inference at petascale. Catalog inference is a "big data" problem that does not parallelize trivially.  ... 
arXiv:1803.00113v3 fatcat:54236cyeorc2vmrps56mwygy34

Big data and extreme-scale computing

M Asch, T Moore, R Badia, M Beck, P Beckman, T Bidot, F Bodin, F Cappello, A Choudhary, B de Supinski, E Deelman, J Dongarra (+27 others)
2018 The international journal of high performance computing applications  
and other devices at the networks edge, and the centralized resources of commercial clouds and HPC centers.  ...  We suggest that the prospects for the future integration of technological infrastructures and research ecosystems need to be considered at three different levels.  ...  (AIST), Barcelona Supercomputer Center, Kyoto University, Kyushu University, Riken, The University of Tokyo, the Tokyo Institute of Technology, and the University of Tsukuba Center for Computational Sciences  ... 
doi:10.1177/1094342018778123 fatcat:vwrrxmad4rhtppq6ioaz4h5q7a

The National Center for Atmospheric Research: 2006 Annual Report NCAR NCAR Annual Report CISL report ESSL report EOL report RAL report SERE report NCAR Director's Message

Search Nar
2016 unpublished
Through generous support from the National Science Foundation, NCAR continues to be dedicated to exploring and understanding our atmosphere in the broadest terms, including interactions with the sun, the  ...  We will install the new ICESS machine in early 2007, increasing the total production computing capacity at NCAR to over 2.2 teraflops sustained, thereby providing our community with secure, leading-edge  ...  at NCAR in FY 2007.  ... 

Dagstuhl Reports, Volume 11, Issue 7, August 2021, Complete Issue [article]

We have investigated the interplay of temporal and multi-way interactions in [3] and found effects, that differ from their projections.  ...  In my talk, I outline two ways of extending higher-order model research, motivated by my previous work on the interplay of dynamics and multi-body topology [1, 2].  ...  of London, GB), Vito Latora (Queen Mary University of London, GB), and Yamir Moreno (University of Zaragoza, ES) In my previous work I have focused on statistical inference of higher-order network models  ... 
doi:10.4230/dagrep.11.7 fatcat:4b73kynisffo7payzzghe5rfiq

The International Symposium on Modeling and Optimization INFOCOMP 2017 Technical Program Committee

Alfred Geiger, Philipp Kremer, German, Edgar Leon, Lawrence, Bernhard Bandow, Alfred Geiger, Philipp Kremer, German, Edgar Leon, Lawrence, Mehmet Aksit (+22 others)
2017 unpublished
measurement, control and management  Modeling and optimization in complex systems  Empirical modeling  Common Grounds for Parallel Interfaces in HPC and Data Science The conference included the following  ...  We also gratefully thank the members of the INFOCOMP 2017 organizing committee for their help in handling the logistics and for their work that made this professional meeting a success.  ...  sciences, both at Smith College.  ...