Zobrazeno 1 - 10
of 1 009
pro vyhledávání: '"Farrell , Steven"'
Autor:
Bhimji, Wahid, Calafiura, Paolo, Chakkappai, Ragansu, Chou, Yuan-Tang, Diefenbacher, Sascha, Dudley, Jordan, Farrell, Steven, Ghosh, Aishik, Guyon, Isabelle, Harris, Chris, Hsu, Shih-Chieh, Khoda, Elham E, Lyscar, Rémy, Michon, Alexandre, Nachman, Benjamin, Nugent, Peter, Reymond, Mathis, Rousseau, David, Sluijter, Benjamin, Thorne, Benjamin, Ullah, Ihsan, Zhang, Yulei
The FAIR Universe -- HiggsML Uncertainty Challenge focuses on measuring the physics properties of elementary particles with imperfect simulators due to differences in modelling systematic errors. Additionally, the challenge is leveraging a large-comp
Externí odkaz:
http://arxiv.org/abs/2410.02867
Autor:
Subramanian, Shashank, Rrapaj, Ermal, Harrington, Peter, Chheda, Smeet, Farrell, Steven, Austin, Brian, Williams, Samuel, Wright, Nicholas, Bhimji, Wahid
Generative AI, in particular large transformer models, are increasingly driving HPC system design in science and industry. We analyze performance characteristics of such transformer models and discuss their sensitivity to the transformer type, parall
Externí odkaz:
http://arxiv.org/abs/2410.00273
Autor:
Zhao, Haoran, Naylor, Andrew, Hsu, Shih-Chieh, Calafiura, Paolo, Farrell, Steven, Feng, Yongbing, Harris, Philip Coleman, Khoda, Elham E, Mccormack, William Patrick, Rankin, Dylan Sheldon, Ju, Xiangyang
Recent studies have shown promising results for track finding in dense environments using Graph Neural Network (GNN)-based algorithms. However, GNN-based track finding is computationally slow on CPUs, necessitating the use of coprocessors to accelera
Externí odkaz:
http://arxiv.org/abs/2402.09633
Autor:
Agarwal, Manan, Alameda, Jay, Audenaert, Jeroen, Benoit, Will, Beveridge, Damon, Bhattacharya, Meghna, Chatterjee, Chayan, Chatterjee, Deep, Chen, Andy, Cholayil, Muhammed Saleem, Chou, Chia-Jui, Choudhary, Sunil, Coughlin, Michael, Dax, Maximilian, Desai, Aman, Di Luca, Andrea, Duarte, Javier Mauricio, Farrell, Steven, Feng, Yongbin, Goodarzi, Pooyan, Govorkova, Ekaterina, Graham, Matthew, Guiang, Jonathan, Gunny, Alec, Guo, Weichangfeng, Hakenmueller, Janina, Hawks, Ben, Hsu, Shih-Chieh, Jawahar, Pratik, Ju, Xiangyang, Katsavounidis, Erik, Kellis, Manolis, Khoda, Elham E, Lahbabi, Fatima Zahra, Lian, Van Tha Bik, Liu, Mia, Malanchev, Konstantin, Marx, Ethan, McCormack, William Patrick, McLeod, Alistair, Mo, Geoffrey, Moreno, Eric Anton, Muthukrishna, Daniel, Narayan, Gautham, Naylor, Andrew, Neubauer, Mark, Norman, Michael, Omer, Rafia, Pedro, Kevin, Peterson, Joshua, Pürrer, Michael, Raikman, Ryan, Raj, Shivam, Ricker, George, Robbins, Jared, Samani, Batool Safarzadeh, Scholberg, Kate, Schuy, Alex, Skliris, Vasileios, Soni, Siddharth, Sravan, Niharika, Sutton, Patrick, Villar, Victoria Ashley, Wang, Xiwei, Wen, Linqing, Wuerthwein, Frank, Yang, Tingjun, Yeh, Shu-Wei
Modern large-scale physics experiments create datasets with sizes and streaming rates that can exceed those from industry leaders such as Google Cloud and Netflix. Fully processing these datasets requires both sufficient compute power and efficient w
Externí odkaz:
http://arxiv.org/abs/2306.08106
Autor:
Liu, Ryan, Calafiura, Paolo, Farrell, Steven, Ju, Xiangyang, Murnane, Daniel Thomas, Pham, Tuan Minh
We introduce a novel variant of GNN for particle tracking called Hierarchical Graph Neural Network (HGNN). The architecture creates a set of higher-level representations which correspond to tracks and assigns spacepoints to these tracks, allowing dis
Externí odkaz:
http://arxiv.org/abs/2303.01640
Autor:
Ju, xiangyang, Wang, Yunsong, Murnane, Daniel, Choma, Nicholas, Farrell, Steven, Calafiura, Paolo
Many artificial intelligence (AI) devices have been developed to accelerate the training and inference of neural networks models. The most common ones are the Graphics Processing Unit (GPU) and Tensor Processing Unit (TPU). They are highly optimized
Externí odkaz:
http://arxiv.org/abs/2210.12247
Autor:
Wang, Chun-Yi, Ju, Xiangyang, Hsu, Shih-Chieh, Murnane, Daniel, Calafiura, Paolo, Farrell, Steven, Spiropulu, Maria, Vlimant, Jean-Roch, Aurisano, Adam, Hewes, V, Cerati, Giuseppe, Gray, Lindsey, Klijnsma, Thomas, Kowalkowski, Jim, Atkinson, Markus, Neubauer, Mark, DeZoort, Gage, Thais, Savannah, Ballow, Alexandra, Lazar, Alina, Caillou, Sylvain, Rougier, Charline, Stark, Jan, Vallier, Alexis, Sardain, Jad
Particle tracking is a challenging pattern recognition task at the Large Hadron Collider (LHC) and the High Luminosity-LHC. Conventional algorithms, such as those based on the Kalman Filter, achieve excellent performance in reconstructing the prompt
Externí odkaz:
http://arxiv.org/abs/2203.08800
Autor:
Lazar, Alina, Ju, Xiangyang, Murnane, Daniel, Calafiura, Paolo, Farrell, Steven, Xu, Yaoyuan, Spiropulu, Maria, Vlimant, Jean-Roch, Cerati, Giuseppe, Gray, Lindsey, Klijnsma, Thomas, Kowalkowski, Jim, Atkinson, Markus, Neubauer, Mark, DeZoort, Gage, Thais, Savannah, Hsu, Shih-Chieh, Aurisano, Adam, Hewes, V, Ballow, Alexandra, Acharya, Nirajan, Wang, Chun-yi, Liu, Emma, Lucas, Alberto
Recently, graph neural networks (GNNs) have been successfully used for a variety of particle reconstruction problems in high energy physics, including particle tracking. The Exa.TrkX pipeline based on GNNs demonstrated promising performance in recons
Externí odkaz:
http://arxiv.org/abs/2202.06929
Autor:
Farrell, Steven, Emani, Murali, Balma, Jacob, Drescher, Lukas, Drozd, Aleksandr, Fink, Andreas, Fox, Geoffrey, Kanter, David, Kurth, Thorsten, Mattson, Peter, Mu, Dawei, Ruhela, Amit, Sato, Kento, Shirahata, Koichi, Tabaru, Tsuguchika, Tsaris, Aristeidis, Balewski, Jan, Cumming, Ben, Danjo, Takumi, Domke, Jens, Fukai, Takaaki, Fukumoto, Naoto, Fukushi, Tatsuya, Gerofi, Balazs, Honda, Takumi, Imamura, Toshiyuki, Kasagi, Akihiko, Kawakami, Kentaro, Kudo, Shuhei, Kuroda, Akiyoshi, Martinasso, Maxime, Matsuoka, Satoshi, Mendonça, Henrique, Minami, Kazuki, Ram, Prabhat, Sawada, Takashi, Shankar, Mallikarjun, John, Tom St., Tabuchi, Akihiro, Vishwanath, Venkatram, Wahib, Mohamed, Yamazaki, Masafumi, Yin, Junqi
Scientific communities are increasingly adopting machine learning and deep learning models in their applications to accelerate scientific insights. High performance computing systems are pushing the frontiers of performance with a rich diversity of h
Externí odkaz:
http://arxiv.org/abs/2110.11466
Publikováno v:
In Computational Materials Science April 2024 238
