Zobrazeno 1 - 10
of 659
pro vyhledávání: '"P Papka"'
People commonly utilize visualizations not only to examine a given dataset, but also to draw generalizable conclusions about the underlying models or phenomena. Prior research has compared human visual inference to that of an optimal Bayesian agent,
Externí odkaz:
http://arxiv.org/abs/2407.16871
Visualizations support rapid analysis of scientific datasets, allowing viewers to glean aggregate information (e.g., the mean) within split-seconds. While prior research has explored this ability in conventional charts, it is unclear if spatial visua
Externí odkaz:
http://arxiv.org/abs/2406.14452
Autor:
Marrinan, Thomas, Moeller, Madeleine, Kanayinkal, Alina, Mateevitsi, Victor A., Papka, Michael E.
Scientists often explore and analyze large-scale scientific simulation data by leveraging two- and three-dimensional visualizations. The data and tasks can be complex and therefore best supported using myriad display technologies, from mobile devices
Externí odkaz:
http://arxiv.org/abs/2404.17619
Autor:
Ma, Xiaolong, Yan, Feng, Yang, Lei, Foster, Ian, Papka, Michael E., Liu, Zhengchun, Kettimuthu, Rajkumar
First-come first-serve scheduling can result in substantial (up to 10%) of transiently idle nodes on supercomputers. Recognizing that such unfilled nodes are well-suited for deep neural network (DNN) training, due to the flexible nature of DNN traini
Externí odkaz:
http://arxiv.org/abs/2404.15668
Autor:
Li, Boyang, Fan, Yuping, Dearing, Matthew, Lan, Zhiling, Richy, Paul, Allcocky, William, Papka, Michael
Emerging workloads in high-performance computing (HPC) are embracing significant changes, such as having diverse resource requirements instead of being CPU-centric. This advancement forces cluster schedulers to consider multiple schedulable resources
Externí odkaz:
http://arxiv.org/abs/2403.16298
In the field of high-performance computing (HPC), there has been recent exploration into the use of deep reinforcement learning for cluster scheduling (DRL scheduling), which has demonstrated promising outcomes. However, a significant challenge arise
Externí odkaz:
http://arxiv.org/abs/2403.16293
Quantitative data is frequently represented using color, yet designing effective color mappings is a challenging task, requiring one to balance perceptual standards with personal color preference. Current design tools either overwhelm novices with co
Externí odkaz:
http://arxiv.org/abs/2401.15032
Autor:
Mateevitsi, Victor A., Bode, Mathis, Ferrier, Nicola, Fischer, Paul, Göbbert, Jens Henrik, Insley, Joseph A., Lan, Yu-Hsiang, Min, Misun, Papka, Michael E., Patel, Saumil, Rizzi, Silvio, Windgassen, Jonathan
In the realm of Computational Fluid Dynamics (CFD), the demand for memory and computation resources is extreme, necessitating the use of leadership-scale computing platforms for practical domain sizes. This intensive requirement renders traditional c
Externí odkaz:
http://arxiv.org/abs/2312.09888
Autor:
Vyfers, E. C., Pesudo, V., Triambak, S., Kamil, M., Adsley, P., Brown, B. A., Jivan, H., Marin-Lambarri, D. J., Neveling, R., Ondze, J. C. Nzobadila, Papka, P., Pellegri, L., Rebeiro, B. M., Singh, B., Smit, F. D., Steyn, G. F.
Background: The nucleosynthesis of several proton-rich nuclei is determined by radiative proton-capture reactions on unstable nuclei in nova explosions. One such reaction is $^{23}{\rm Mg}(p,\gamma)^{24}{\rm Al}$, which links the NeNa and MgAl cycles
Externí odkaz:
http://arxiv.org/abs/2311.15935
Autor:
Song, Shuaiwen Leon, Kruft, Bonnie, Zhang, Minjia, Li, Conglong, Chen, Shiyang, Zhang, Chengming, Tanaka, Masahiro, Wu, Xiaoxia, Rasley, Jeff, Awan, Ammar Ahmad, Holmes, Connor, Cai, Martin, Ghanem, Adam, Zhou, Zhongzhu, He, Yuxiong, Luferenko, Pete, Kumar, Divya, Weyn, Jonathan, Zhang, Ruixiong, Klocek, Sylwester, Vragov, Volodymyr, AlQuraishi, Mohammed, Ahdritz, Gustaf, Floristean, Christina, Negri, Cristina, Kotamarthi, Rao, Vishwanath, Venkatram, Ramanathan, Arvind, Foreman, Sam, Hippe, Kyle, Arcomano, Troy, Maulik, Romit, Zvyagin, Maxim, Brace, Alexander, Zhang, Bin, Bohorquez, Cindy Orozco, Clyde, Austin, Kale, Bharat, Perez-Rivera, Danilo, Ma, Heng, Mann, Carla M., Irvin, Michael, Pauloski, J. Gregory, Ward, Logan, Hayot, Valerie, Emani, Murali, Xie, Zhen, Lin, Diangen, Shukla, Maulik, Foster, Ian, Davis, James J., Papka, Michael E., Brettin, Thomas, Balaprakash, Prasanna, Tourassi, Gina, Gounley, John, Hanson, Heidi, Potok, Thomas E, Pasini, Massimiliano Lupo, Evans, Kate, Lu, Dan, Lunga, Dalton, Yin, Junqi, Dash, Sajal, Wang, Feiyi, Shankar, Mallikarjun, Lyngaas, Isaac, Wang, Xiao, Cong, Guojing, Zhang, Pei, Fan, Ming, Liu, Siyan, Hoisie, Adolfy, Yoo, Shinjae, Ren, Yihui, Tang, William, Felker, Kyle, Svyatkovskiy, Alexey, Liu, Hang, Aji, Ashwin, Dalton, Angela, Schulte, Michael, Schulz, Karl, Deng, Yuntian, Nie, Weili, Romero, Josh, Dallago, Christian, Vahdat, Arash, Xiao, Chaowei, Gibbs, Thomas, Anandkumar, Anima, Stevens, Rick
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences. This could herald a new era of scientific exploration, bringing significant advancements across sectors fro
Externí odkaz:
http://arxiv.org/abs/2310.04610