Zobrazeno 1 - 10
of 2 409
pro vyhledávání: '"Ben-Nun A"'
Publikováno v:
J. Space Weather Space Clim. Volume 14, 2024
Understanding the large-scale three-dimensional structure of the inner heliosphere, while important in its own right, is crucial for space weather applications, such as forecasting the time of arrival and propagation of coronal mass ejections (CMEs).
Externí odkaz:
http://arxiv.org/abs/2405.00174
Autor:
Baumann, Yves, Ben-Nun, Tal, Besta, Maciej, Gianinazzi, Lukas, Hoefler, Torsten, Luczynski, Piotr
Publikováno v:
IEEE International Parallel and Distributed Processing Symposium, IPDPS 2024, San Francisco, CA, USA, May 27-31 (2024) 180-192
Contemporary accelerator designs exhibit a high degree of spatial localization, wherein two-dimensional physical distance determines communication costs between processing elements. This situation presents considerable algorithmic challenges, particu
Externí odkaz:
http://arxiv.org/abs/2404.12953
Autor:
Gianinazzi, Lukas, Ziogas, Alexandros Nikolaos, Huang, Langwen, Luczynski, Piotr, Ashkboos, Saleh, Scheidl, Florian, Carigiet, Armon, Ge, Chio, Abubaker, Nabil, Besta, Maciej, Ben-Nun, Tal, Hoefler, Torsten
Publikováno v:
PPoPP'24: Proceedings of the 29th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming (2024) 404-416
We propose a novel approach to iterated sparse matrix dense matrix multiplication, a fundamental computational kernel in scientific computing and graph neural network training. In cases where matrix sizes exceed the memory of a single compute node, d
Externí odkaz:
http://arxiv.org/abs/2402.19364
Publikováno v:
Revista Mexicana de F\'isica 70 031501 1-10. May-June 2024
This paper explores the effects of numerical algorithms on global magnetohydrodynamics (MHD) simulations of solar wind (SW) in the inner heliosphere. To do so, we use sunRunner3D, a 3-D MHD model that employs the boundary conditions generated by CORH
Externí odkaz:
http://arxiv.org/abs/2401.14480
Autor:
Ben-Nun, Michal, Török, Tibor, Palmerio, Erika, Downs, Cooper, Titov, Viacheslav S., Linton, Mark G., Caplan, Ronald M., Lionello, Roberto
The trajectories of coronal mass ejections (CMEs) are often seen to substantially deviate from a purely radial propagation direction. Such deviations occur predominantly in the corona and have been attributed to "channeling" or deflection of the erup
Externí odkaz:
http://arxiv.org/abs/2310.02412
Autor:
Castro, Roberto L., Ivanov, Andrei, Andrade, Diego, Ben-Nun, Tal, Fraguela, Basilio B., Hoefler, Torsten
The increasing success and scaling of Deep Learning models demands higher computational efficiency and power. Sparsification can lead to both smaller models as well as higher compute efficiency, and accelerated hardware is becoming available. However
Externí odkaz:
http://arxiv.org/abs/2310.02065
Autor:
Grossman, Aiden, Paehler, Ludger, Parasyris, Konstantinos, Ben-Nun, Tal, Hegna, Jacob, Moses, William, Diaz, Jose M Monsalve, Trofin, Mircea, Doerfert, Johannes
Code is increasingly becoming a core data modality of modern machine learning research impacting not only the way we write code with conversational agents like OpenAI's ChatGPT, Google's Bard, or Anthropic's Claude, the way we translate code from one
Externí odkaz:
http://arxiv.org/abs/2309.15432
Autor:
Bazinska, Julia, Ivanov, Andrei, Ben-Nun, Tal, Dryden, Nikoli, Besta, Maciej, Shen, Siyuan, Hoefler, Torsten
Graph Neural Networks (GNNs) are a powerful tool for handling structured graph data and addressing tasks such as node classification, graph classification, and clustering. However, the sparse nature of GNN computation poses new challenges for perform
Externí odkaz:
http://arxiv.org/abs/2308.12093
Execution graphs of parallel loop programs exhibit a nested, repeating structure. We show how such graphs that are the result of nested repetition can be represented by succinct parametric structures. This parametric graph template representation all
Externí odkaz:
http://arxiv.org/abs/2307.08420
Autor:
Sarabeth M. Mathis, Alexander E. Webber, Tomás M. León, Erin L. Murray, Monica Sun, Lauren A. White, Logan C. Brooks, Alden Green, Addison J. Hu, Roni Rosenfeld, Dmitry Shemetov, Ryan J. Tibshirani, Daniel J. McDonald, Sasikiran Kandula, Sen Pei, Rami Yaari, Teresa K. Yamana, Jeffrey Shaman, Pulak Agarwal, Srikar Balusu, Gautham Gururajan, Harshavardhan Kamarthi, B. Aditya Prakash, Rishi Raman, Zhiyuan Zhao, Alexander Rodríguez, Akilan Meiyappan, Shalina Omar, Prasith Baccam, Heidi L. Gurung, Brad T. Suchoski, Steve A. Stage, Marco Ajelli, Allisandra G. Kummer, Maria Litvinova, Paulo C. Ventura, Spencer Wadsworth, Jarad Niemi, Erica Carcelen, Alison L. Hill, Sara L. Loo, Clifton D. McKee, Koji Sato, Claire Smith, Shaun Truelove, Sung-mok Jung, Joseph C. Lemaitre, Justin Lessler, Thomas McAndrew, Wenxuan Ye, Nikos Bosse, William S. Hlavacek, Yen Ting Lin, Abhishek Mallela, Graham C. Gibson, Ye Chen, Shelby M. Lamm, Jaechoul Lee, Richard G. Posner, Amanda C. Perofsky, Cécile Viboud, Leonardo Clemente, Fred Lu, Austin G. Meyer, Mauricio Santillana, Matteo Chinazzi, Jessica T. Davis, Kunpeng Mu, Ana Pastore y Piontti, Alessandro Vespignani, Xinyue Xiong, Michal Ben-Nun, Pete Riley, James Turtle, Chis Hulme-Lowe, Shakeel Jessa, V. P. Nagraj, Stephen D. Turner, Desiree Williams, Avranil Basu, John M. Drake, Spencer J. Fox, Ehsan Suez, Monica G. Cojocaru, Edward W. Thommes, Estee Y. Cramer, Aaron Gerding, Ariane Stark, Evan L. Ray, Nicholas G. Reich, Li Shandross, Nutcha Wattanachit, Yijin Wang, Martha W. Zorn, Majd Al Aawar, Ajitesh Srivastava, Lauren A. Meyers, Aniruddha Adiga, Benjamin Hurt, Gursharn Kaur, Bryan L. Lewis, Madhav Marathe, Srinivasan Venkatramanan, Patrick Butler, Andrew Farabow, Naren Ramakrishnan, Nikhil Muralidhar, Carrie Reed, Matthew Biggerstaff, Rebecca K. Borchering
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-13 (2024)
Abstract Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. For the 2021–22 and 2022–23 influenza seasons, 26 forecasting teams provided national and jurisdiction-specific probabilistic predi
Externí odkaz:
https://doaj.org/article/b22341a399b644b886f816887477dc5b