High Energy Physics Forum for Computational Excellence: Working Group Reports (I. Applications Software II. Software Libraries and Tools III. Systems)
October 29, 2015 ยท Declared Dead ยท + Add venue
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Salman Habib, Robert Roser, Tom LeCompte, Zach Marshall, Anders Borgland, Brett Viren, Peter Nugent, Makoto Asai, Lothar Bauerdick, Hal Finkel, Steve Gottlieb, Stefan Hoeche, Paul Sheldon, Jean-Luc Vay, Peter Elmer, Michael Kirby, Simon Patton, Maxim Potekhin, Brian Yanny, Paolo Calafiura, Eli Dart, Oliver Gutsche, Taku Izubuchi, Adam Lyon, Don Petravick
arXiv ID
1510.08545
Category
physics.comp-ph
Cross-listed
cs.CE,
cs.DC,
hep-ex
Citations
0
Last Checked
1 month ago
Abstract
Computing plays an essential role in all aspects of high energy physics. As computational technology evolves rapidly in new directions, and data throughput and volume continue to follow a steep trend-line, it is important for the HEP community to develop an effective response to a series of expected challenges. In order to help shape the desired response, the HEP Forum for Computational Excellence (HEP-FCE) initiated a roadmap planning activity with two key overlapping drivers -- 1) software effectiveness, and 2) infrastructure and expertise advancement. The HEP-FCE formed three working groups, 1) Applications Software, 2) Software Libraries and Tools, and 3) Systems (including systems software), to provide an overview of the current status of HEP computing and to present findings and opportunities for the desired HEP computational roadmap. The final versions of the reports are combined in this document, and are presented along with introductory material.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ physics.comp-ph
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
R.I.P.
๐ป
Ghosted
Heterogeneous Parallelization and Acceleration of Molecular Dynamics Simulations in GROMACS
R.I.P.
๐ป
Ghosted
By-passing the Kohn-Sham equations with machine learning
R.I.P.
๐ป
Ghosted
Machine Learning of coarse-grained Molecular Dynamics Force Fields
R.I.P.
๐ป
Ghosted
Towards Physics-informed Deep Learning for Turbulent Flow Prediction
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Language Models are Few-Shot Learners
R.I.P.
๐ป
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
๐ป
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
๐ป
Ghosted