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522 results on '"Bhimji, Wahid"'

1. FAIR Universe HiggsML Uncertainty Challenge Competition

Author

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, and Zhang, Yulei

Subjects
High Energy Physics - Phenomenology, Computer Science - Machine Learning, High Energy Physics - Experiment, Physics - Data Analysis, Statistics and Probability
Abstract

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-compute-scale AI platform for sharing datasets, training models, and hosting machine learning competitions. Our challenge brings together the physics and machine learning communities to advance our understanding and methodologies in handling systematic (epistemic) uncertainties within AI techniques., Comment: Whitepaper for the FAIR Universe HiggsML Uncertainty Challenge Competition, available : https://fair-universe.lbl.gov

Published
2024

2. Comprehensive Performance Modeling and System Design Insights for Foundation Models

Author

Subramanian, Shashank, Rrapaj, Ermal, Harrington, Peter, Chheda, Smeet, Farrell, Steven, Austin, Brian, Williams, Samuel, Wright, Nicholas, and Bhimji, Wahid

Subjects
Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

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, parallelization strategy, and HPC system features (accelerators and interconnects). We utilize a performance model that allows us to explore this complex design space and highlight its key components. We find that different transformer types demand different parallelism and system characteristics at different training regimes. Large Language Models are performant with 3D parallelism and amplify network needs only at pre-training scales with reduced dependence on accelerator capacity and bandwidth. On the other hand, long-sequence transformers, representative of scientific foundation models, place a more uniform dependence on network and capacity with necessary 4D parallelism. Our analysis emphasizes the need for closer performance modeling of different transformer types keeping system features in mind and demonstrates a path towards this. Our code is available as open-source., Comment: 17 pages, PMBS 2024

Published
2024

3. Towards Foundation Models for Scientific Machine Learning: Characterizing Scaling and Transfer Behavior

Author

Subramanian, Shashank, Harrington, Peter, Keutzer, Kurt, Bhimji, Wahid, Morozov, Dmitriy, Mahoney, Michael, and Gholami, Amir

Subjects
Computer Science - Machine Learning, Mathematics - Numerical Analysis
Abstract

Pre-trained machine learning (ML) models have shown great performance for a wide range of applications, in particular in natural language processing (NLP) and computer vision (CV). Here, we study how pre-training could be used for scientific machine learning (SciML) applications, specifically in the context of transfer learning. We study the transfer behavior of these models as (i) the pre-trained model size is scaled, (ii) the downstream training dataset size is scaled, (iii) the physics parameters are systematically pushed out of distribution, and (iv) how a single model pre-trained on a mixture of different physics problems can be adapted to various downstream applications. We find that-when fine-tuned appropriately-transfer learning can help reach desired accuracy levels with orders of magnitude fewer downstream examples (across different tasks that can even be out-of-distribution) than training from scratch, with consistent behavior across a wide range of downstream examples. We also find that fine-tuning these models yields more performance gains as model size increases, compared to training from scratch on new downstream tasks. These results hold for a broad range of PDE learning tasks. All in all, our results demonstrate the potential of the "pre-train and fine-tune" paradigm for SciML problems, demonstrating a path towards building SciML foundation models. We open-source our code for reproducibility., Comment: 16 pages, 11 figures

Published
2023

4. Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulence

Author

Chattopadhyay, Ashesh, Pathak, Jaideep, Nabizadeh, Ebrahim, Bhimji, Wahid, and Hassanzadeh, Pedram

Subjects
Computer Science - Machine Learning, Nonlinear Sciences - Chaotic Dynamics, Physics - Atmospheric and Oceanic Physics, Physics - Fluid Dynamics, Physics - Geophysics
Abstract

Recent years have seen a surge in interest in building deep learning-based fully data-driven models for weather prediction. Such deep learning models if trained on observations can mitigate certain biases in current state-of-the-art weather models, some of which stem from inaccurate representation of subgrid-scale processes. However, these data-driven models, being over-parameterized, require a lot of training data which may not be available from reanalysis (observational data) products. Moreover, an accurate, noise-free, initial condition to start forecasting with a data-driven weather model is not available in realistic scenarios. Finally, deterministic data-driven forecasting models suffer from issues with long-term stability and unphysical climate drift, which makes these data-driven models unsuitable for computing climate statistics. Given these challenges, previous studies have tried to pre-train deep learning-based weather forecasting models on a large amount of imperfect long-term climate model simulations and then re-train them on available observational data. In this paper, we propose a convolutional variational autoencoder-based stochastic data-driven model that is pre-trained on an imperfect climate model simulation from a 2-layer quasi-geostrophic flow and re-trained, using transfer learning, on a small number of noisy observations from a perfect simulation. This re-trained model then performs stochastic forecasting with a noisy initial condition sampled from the perfect simulation. We show that our ensemble-based stochastic data-driven model outperforms a baseline deterministic encoder-decoder-based convolutional model in terms of short-term skills while remaining stable for long-term climate simulations yielding accurate climatology.

Published
2022

5. Software and Computing for Small HEP Experiments

Author

Casper, Dave, Monzani, Maria Elena, Nachman, Benjamin, Andreopoulos, Costas, Bailey, Stephen, Bard, Deborah, Bhimji, Wahid, Cerati, Giuseppe, Chachamis, Grigorios, Daughhetee, Jacob, Diamond, Miriam, Elvira, V. Daniel, Fan, Alden, Genser, Krzysztof, Girotti, Paolo, Kravitz, Scott, Kutschke, Robert, Pascuzzi, Vincent R., Perdue, Gabriel N., Snider, Erica, Sexton-Kennedy, Elizabeth, Stewart, Graeme Andrew, Szydagis, Matthew, Torrence, Eric, and Tunnell, Christopher

Subjects
High Energy Physics - Experiment, Physics - Computational Physics
Abstract

This white paper briefly summarized key conclusions of the recent US Community Study on the Future of Particle Physics (Snowmass 2021) workshop on Software and Computing for Small High Energy Physics Experiments., Comment: Contribution to Snowmass 2021

Published
2022

6. The Petascale DTN Project: High Performance Data Transfer for HPC Facilities

Author

Dart, Eli, Allcock, William, Bhimji, Wahid, Boerner, Tim, Cheema, Ravinderjeet, Cherry, Andrew, Draney, Brent, Habib, Salman, Hazen, Damian, Hill, Jason, Kollross, Matt, Parete-Koon, Suzanne, Pelfrey, Daniel, Pope, Adrian, Porter, Jeff, and Wheeler, David

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Performance
Abstract

The movement of large-scale (tens of Terabytes and larger) data sets between high performance computing (HPC) facilities is an important and increasingly critical capability. A growing number of scientific collaborations rely on HPC facilities for tasks which either require large-scale data sets as input or produce large-scale data sets as output. In order to enable the transfer of these data sets as needed by the scientific community, HPC facilities must design and deploy the appropriate data transfer capabilities to allow users to do data placement at scale. This paper describes the Petascale DTN Project, an effort undertaken by four HPC facilities, which succeeded in achieving routine data transfer rates of over 1PB/week between the facilities. We describe the design and configuration of the Data Transfer Node (DTN) clusters used for large-scale data transfers at these facilities, the software tools used, and the performance tuning that enabled this capability.

Published
2021

7. 2019 Computing Sciences Strategic Plan

Author

Yelick, Kathy, Agarwal, Deb, Bard, Debbie, Shalf, John, Almgren, Ann, Bhimji, Wahid, Brown, Ben, Carter, Jonathan, Jong, Bert, Doerfler, Doug, Donofrio, David, Guok, Chin, Iancu, Costin, Kiran, Mariam, Li, Sherry, Nugent, Peter, Prabhat, M, Ramakrishnan, Lavanya, Vasudevan, Dilip, Wright, Nick, Cademartori, Helen, Antypas, Katie, and Kincade, Kathy

Published
2021

8. Optimization of Software on High Performance Computing Platforms for the LUX-ZEPLIN Dark Matter Experiment

Author

Ayyar, Venkitesh, Bhimji, Wahid, Monzani, Maria Elena, Naylor, Andrew, Patton, Simon, and Tull, Craig E.

Subjects
Physics - Computational Physics, High Energy Physics - Experiment
Abstract

High Energy Physics experiments like the LUX-ZEPLIN dark matter experiment face unique challenges when running their computation on High Performance Computing resources. In this paper, we describe some strategies to optimize memory usage of simulation codes with the help of profiling tools. We employed this approach and achieved memory reduction of 10-30\%. While this has been performed in the context of the LZ experiment, it has wider applicability to other HEP experimental codes that face these challenges on modern computer architectures., Comment: Contribution to Proceedings of CHEP 2019, Nov 4-8, Adelaide, Australia

Published
2020
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9. The use of Convolutional Neural Networks for signal-background classification in Particle Physics experiments

Author

Ayyar, Venkitesh, Bhimji, Wahid, Gerhardt, Lisa, Robertson, Sally, and Ronaghi, Zahra

Subjects
High Energy Physics - Experiment, Statistics - Machine Learning
Abstract

The success of Convolutional Neural Networks (CNNs) in image classification has prompted efforts to study their use for classifying image data obtained in Particle Physics experiments. Here, we discuss our efforts to apply CNNs to 2D and 3D image data from particle physics experiments to classify signal from background. In this work we present an extensive convolutional neural architecture search, achieving high accuracy for signal/background discrimination for a HEP classification use-case based on simulated data from the Ice Cube neutrino observatory and an ATLAS-like detector. We demonstrate among other things that we can achieve the same accuracy as complex ResNet architectures with CNNs with less parameters, and present comparisons of computational requirements, training and inference times., Comment: Contribution to Proceedings of CHEP 2019, Nov 4-8, Adelaide, Australia

Published
2020
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10. Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale

Author

Baydin, Atılım Güneş, Shao, Lei, Bhimji, Wahid, Heinrich, Lukas, Meadows, Lawrence, Liu, Jialin, Munk, Andreas, Naderiparizi, Saeid, Gram-Hansen, Bradley, Louppe, Gilles, Ma, Mingfei, Zhao, Xiaohui, Torr, Philip, Lee, Victor, Cranmer, Kyle, Prabhat, and Wood, Frank

Subjects
Computer Science - Machine Learning, Computer Science - Performance, Statistics - Machine Learning, 68T37, 68T05, 62P35, G.3, I.2.6, J.2
Abstract

Probabilistic programming languages (PPLs) are receiving widespread attention for performing Bayesian inference in complex generative models. However, applications to science remain limited because of the impracticability of rewriting complex scientific simulators in a PPL, the computational cost of inference, and the lack of scalable implementations. To address these, we present a novel PPL framework that couples directly to existing scientific simulators through a cross-platform probabilistic execution protocol and provides Markov chain Monte Carlo (MCMC) and deep-learning-based inference compilation (IC) engines for tractable inference. To guide IC inference, we perform distributed training of a dynamic 3DCNN--LSTM architecture with a PyTorch-MPI-based framework on 1,024 32-core CPU nodes of the Cori supercomputer with a global minibatch size of 128k: achieving a performance of 450 Tflop/s through enhancements to PyTorch. We demonstrate a Large Hadron Collider (LHC) use-case with the C++ Sherpa simulator and achieve the largest-scale posterior inference in a Turing-complete PPL., Comment: 14 pages, 8 figures

Published
2019
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11. Machine Learning Templates for QCD Factorization in the Search for Physics Beyond the Standard Model

Author

Lin, Joshua, Bhimji, Wahid, and Nachman, Benjamin

Subjects
High Energy Physics - Phenomenology, High Energy Physics - Experiment
Abstract

High-multiplicity all-hadronic final states are an important, but difficult final state for searching for physics beyond the Standard Model. A powerful search method is to look for large jets with accidental substructure due to multiple hard partons falling within a single jet. One way for estimating the background in this search is to exploit an approximate factorization in quantum chromodynamics whereby the jet mass distribution is determined only by its kinematic properties. Traditionally, this approach has been executed using histograms constructed in a background-rich region. We propose a new approach based on Generative Adversarial Networks (GANs). These neural network approaches are naturally unbinned and can be readily conditioned on multiple jet properties. In addition to using vanilla GANs for this purpose, a modification to the traditional WGAN approach has been investigated where weight clipping is replaced with a naturally compact set (in this case, the circle). Both the vanilla and modified WGAN approaches significantly outperform the histogram method, especially when modeling the dependence on features not used in the histogram construction. These results can be useful for enhancing the sensitivity of LHC searches to high-multiplicity final states involving many quarks and gluons and serve as a useful benchmark where GANs may have immediate benefit to the HEP community., Comment: 17 pages, 6 figures. v2: Updated to journal version

Published
2019
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12. A pattern recognition algorithm for quantum annealers

Author

Bapst, Frederic, Bhimji, Wahid, Calafiura, Paolo, Gray, Heather, Lavrijsen, Wim, and Linder, Lucy

Subjects
Quantum Physics
Abstract

The reconstruction of charged particles will be a key computing challenge for the high-luminosity Large Hadron Collider (HL-LHC) where increased data rates lead to large increases in running time for current pattern recognition algorithms. An alternative approach explored here expresses pattern recognition as a Quadratic Unconstrained Binary Optimization (QUBO) using software and quantum annealing. At track densities comparable with current LHC conditions, our approach achieves physics performance competitive with state-of-the-art pattern recognition algorithms. More research will be needed to achieve comparable performance in HL-LHC conditions, as increasing track density decreases the purity of the QUBO track segment classifier.

Published
2019

13. A Pattern Recognition Algorithm for Quantum Annealers

Author

Bapst, Frédéric, Bhimji, Wahid, Calafiura, Paolo, Gray, Heather, Lavrijsen, Wim, Linder, Lucy, and Smith, Alex

Subjects
Particle and High Energy Physics, Physical Sciences, quant-ph
Abstract

The reconstruction of charged particles will be a key computing challenge for the high-luminosity Large Hadron Collider (HL-LHC) where increased data rates lead to a large increase in running time for current pattern recognition algorithms. An alternative approach explored here expresses pattern recognition as a quadratic unconstrained binary optimization (QUBO), which allows algorithms to be run on classical and quantum annealers. While the overall timing of the proposed approach and its scaling has still to be measured and studied, we demonstrate that, in terms of efficiency and purity, the same physics performance of the LHC tracking algorithms can be achieved. More research will be needed to achieve comparable performance in HL-LHC conditions, as increasing track density decreases the purity of the QUBO track segment classifier.

Published
2020

14. Graph Neural Networks for IceCube Signal Classification

Author

Choma, Nicholas, Monti, Federico, Gerhardt, Lisa, Palczewski, Tomasz, Ronaghi, Zahra, Prabhat, Bhimji, Wahid, Bronstein, Michael M., Klein, Spencer R., and Bruna, Joan

Subjects
Computer Science - Machine Learning, Astrophysics - Instrumentation and Methods for Astrophysics, Statistics - Machine Learning
Abstract

Tasks involving the analysis of geometric (graph- and manifold-structured) data have recently gained prominence in the machine learning community, giving birth to a rapidly developing field of geometric deep learning. In this work, we leverage graph neural networks to improve signal detection in the IceCube neutrino observatory. The IceCube detector array is modeled as a graph, where vertices are sensors and edges are a learned function of the sensors' spatial coordinates. As only a subset of IceCube's sensors is active during a given observation, we note the adaptive nature of our GNN, wherein computation is restricted to the input signal support. We demonstrate the effectiveness of our GNN architecture on a task classifying IceCube events, where it outperforms both a traditional physics-based method as well as classical 3D convolution neural networks.

Published
2018

15. Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model

Author

Baydin, Atılım Güneş, Heinrich, Lukas, Bhimji, Wahid, Shao, Lei, Naderiparizi, Saeid, Munk, Andreas, Liu, Jialin, Gram-Hansen, Bradley, Louppe, Gilles, Meadows, Lawrence, Torr, Philip, Lee, Victor, Prabhat, Cranmer, Kyle, and Wood, Frank

Subjects
Computer Science - Machine Learning, High Energy Physics - Phenomenology, Physics - Data Analysis, Statistics and Probability, Statistics - Machine Learning, 68T37, 68T05, 62P35, G.3, I.2.6, J.2
Abstract

We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which allows general-purpose inference engines to record and control random number draws within simulators in a language-agnostic way. The execution of existing simulators as probabilistic programs enables highly interpretable posterior inference in the structured model defined by the simulator code base. We demonstrate the technique in particle physics, on a scientifically accurate simulation of the tau lepton decay, which is a key ingredient in establishing the properties of the Higgs boson. Inference efficiency is achieved via inference compilation where a deep recurrent neural network is trained to parameterize proposal distributions and control the stochastic simulator in a sequential importance sampling scheme, at a fraction of the computational cost of a Markov chain Monte Carlo baseline., Comment: 20 pages, 9 figures

Published
2018

16. Machine Learning in High Energy Physics Community White Paper

Author

Albertsson, Kim, Altoe, Piero, Anderson, Dustin, Anderson, John, Andrews, Michael, Espinosa, Juan Pedro Araque, Aurisano, Adam, Basara, Laurent, Bevan, Adrian, Bhimji, Wahid, Bonacorsi, Daniele, Burkle, Bjorn, Calafiura, Paolo, Campanelli, Mario, Capps, Louis, Carminati, Federico, Carrazza, Stefano, Chen, Yi-fan, Childers, Taylor, Coadou, Yann, Coniavitis, Elias, Cranmer, Kyle, David, Claire, Davis, Douglas, De Simone, Andrea, Duarte, Javier, Erdmann, Martin, Eschle, Jonas, Farbin, Amir, Feickert, Matthew, Castro, Nuno Filipe, Fitzpatrick, Conor, Floris, Michele, Forti, Alessandra, Garra-Tico, Jordi, Gemmler, Jochen, Girone, Maria, Glaysher, Paul, Gleyzer, Sergei, Gligorov, Vladimir, Golling, Tobias, Graw, Jonas, Gray, Lindsey, Greenwood, Dick, Hacker, Thomas, Harvey, John, Hegner, Benedikt, Heinrich, Lukas, Heintz, Ulrich, Hooberman, Ben, Junggeburth, Johannes, Kagan, Michael, Kane, Meghan, Kanishchev, Konstantin, Karpiński, Przemysław, Kassabov, Zahari, Kaul, Gaurav, Kcira, Dorian, Keck, Thomas, Klimentov, Alexei, Kowalkowski, Jim, Kreczko, Luke, Kurepin, Alexander, Kutschke, Rob, Kuznetsov, Valentin, Köhler, Nicolas, Lakomov, Igor, Lannon, Kevin, Lassnig, Mario, Limosani, Antonio, Louppe, Gilles, Mangu, Aashrita, Mato, Pere, Meenakshi, Narain, Meinhard, Helge, Menasce, Dario, Moneta, Lorenzo, Moortgat, Seth, Neubauer, Mark, Newman, Harvey, Otten, Sydney, Pabst, Hans, Paganini, Michela, Paulini, Manfred, Perdue, Gabriel, Perez, Uzziel, Picazio, Attilio, Pivarski, Jim, Prosper, Harrison, Psihas, Fernanda, Radovic, Alexander, Reece, Ryan, Rinkevicius, Aurelius, Rodrigues, Eduardo, Rorie, Jamal, Rousseau, David, Sauers, Aaron, Schramm, Steven, Schwartzman, Ariel, Severini, Horst, Seyfert, Paul, Siroky, Filip, Skazytkin, Konstantin, Sokoloff, Mike, Stewart, Graeme, Stienen, Bob, Stockdale, Ian, Strong, Giles, Sun, Wei, Thais, Savannah, Tomko, Karen, Upfal, Eli, Usai, Emanuele, Ustyuzhanin, Andrey, Vala, Martin, Vasel, Justin, Vallecorsa, Sofia, Verzetti, Mauro, Vilasís-Cardona, Xavier, Vlimant, Jean-Roch, Vukotic, Ilija, Wang, Sean-Jiun, Watts, Gordon, Williams, Michael, Wu, Wenjing, Wunsch, Stefan, Yang, Kun, and Zapata, Omar

Subjects
Physics - Computational Physics, Computer Science - Machine Learning, High Energy Physics - Experiment, Statistics - Machine Learning
Abstract

Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas for machine learning in particle physics. We detail a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit., Comment: Editors: Sergei Gleyzer, Paul Seyfert and Steven Schramm

Published
2018

17. A Roadmap for HEP Software and Computing R&D for the 2020s

Author

Albrecht, Johannes, Alves, Antonio Augusto, Amadio, Guilherme, Andronico, Giuseppe, Anh-Ky, Nguyen, Aphecetche, Laurent, Apostolakis, John, Asai, Makoto, Atzori, Luca, Babik, Marian, Bagliesi, Giuseppe, Bandieramonte, Marilena, Banerjee, Sunanda, Barisits, Martin, Bauerdick, Lothar AT, Belforte, Stefano, Benjamin, Douglas, Bernius, Catrin, Bhimji, Wahid, Bianchi, Riccardo Maria, Bird, Ian, Biscarat, Catherine, Blomer, Jakob, Bloom, Kenneth, Boccali, Tommaso, Bockelman, Brian, Bold, Tomasz, Bonacorsi, Daniele, Boveia, Antonio, Bozzi, Concezio, Bracko, Marko, Britton, David, Buckley, Andy, Buncic, Predrag, Calafiura, Paolo, Campana, Simone, Canal, Philippe, Canali, Luca, Carlino, Gianpaolo, Castro, Nuno, Cattaneo, Marco, Cerminara, Gianluca, Cervantes Villanueva, Javier, Chang, Philip, Chapman, John, Chen, Gang, Childers, Taylor, Clarke, Peter, Clemencic, Marco, Cogneras, Eric, Coles, Jeremy, Collier, Ian, Colling, David, Corti, Gloria, Cosmo, Gabriele, Costanzo, Davide, Couturier, Ben, Cranmer, Kyle, Cranshaw, Jack, Cristella, Leonardo, Crooks, David, Crépé-Renaudin, Sabine, Currie, Robert, Dallmeier-Tiessen, Sünje, De, Kaushik, De Cian, Michel, De Roeck, Albert, Delgado Peris, Antonio, Derue, Frédéric, Di Girolamo, Alessandro, Di Guida, Salvatore, Dimitrov, Gancho, Doglioni, Caterina, Dotti, Andrea, Duellmann, Dirk, Duflot, Laurent, Dykstra, Dave, Dziedziniewicz-Wojcik, Katarzyna, Dziurda, Agnieszka, Egede, Ulrik, Elmer, Peter, Elmsheuser, Johannes, Elvira, V Daniel, Eulisse, Giulio, Farrell, Steven, Ferber, Torben, Filipcic, Andrej, Fisk, Ian, Fitzpatrick, Conor, Flix, José, Formica, Andrea, Forti, Alessandra, Franzoni, Giovanni, Frost, James, Fuess, Stu, Gaede, Frank, Ganis, Gerardo, Gardner, Robert, Garonne, Vincent, and Gellrich, Andreas

Subjects
Information and Computing Sciences, Physical Sciences, Software Engineering, physics.comp-ph, hep-ex
Abstract

Particle physics has an ambitious and broad experimental programme for the coming decades. This programme requires large investments in detector hardware, either to build new facilities and experiments, or to upgrade existing ones. Similarly, it requires commensurate investment in the R&D of software to acquire, manage, process, and analyse the shear amounts of data to be recorded. In planning for the HL-LHC in particular, it is critical that all of the collaborating stakeholders agree on the software goals and priorities, and that the efforts complement each other. In this spirit, this white paper describes the R&D activities required to prepare for this software upgrade.

Published
2019

18. Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators

Author

Casado, Mario Lezcano, Baydin, Atilim Gunes, Rubio, David Martinez, Le, Tuan Anh, Wood, Frank, Heinrich, Lukas, Louppe, Gilles, Cranmer, Kyle, Ng, Karen, Bhimji, Wahid, and Prabhat

Subjects
Computer Science - Artificial Intelligence, Physics - Data Analysis, Statistics and Probability, 68T37, 68T05, 62P35, G.3, I.2.6, J.2
Abstract

We consider the problem of Bayesian inference in the family of probabilistic models implicitly defined by stochastic generative models of data. In scientific fields ranging from population biology to cosmology, low-level mechanistic components are composed to create complex generative models. These models lead to intractable likelihoods and are typically non-differentiable, which poses challenges for traditional approaches to inference. We extend previous work in "inference compilation", which combines universal probabilistic programming and deep learning methods, to large-scale scientific simulators, and introduce a C++ based probabilistic programming library called CPProb. We successfully use CPProb to interface with SHERPA, a large code-base used in particle physics. Here we describe the technical innovations realized and planned for this library., Comment: 7 pages, 2 figures

Published
2017

19. A Roadmap for HEP Software and Computing R&D for the 2020s

Author

Albrecht, Johannes, Alves Jr, Antonio Augusto, Amadio, Guilherme, Andronico, Giuseppe, Anh-Ky, Nguyen, Aphecetche, Laurent, Apostolakis, John, Asai, Makoto, Atzori, Luca, Babik, Marian, Bagliesi, Giuseppe, Bandieramonte, Marilena, Banerjee, Sunanda, Barisits, Martin, Bauerdick, Lothar A. T., Belforte, Stefano, Benjamin, Douglas, Bernius, Catrin, Bhimji, Wahid, Bianchi, Riccardo Maria, Bird, Ian, Biscarat, Catherine, Blomer, Jakob, Bloom, Kenneth, Boccali, Tommaso, Bockelman, Brian, Bold, Tomasz, Bonacorsi, Daniele, Boveia, Antonio, Bozzi, Concezio, Bracko, Marko, Britton, David, Buckley, Andy, Buncic, Predrag, Calafiura, Paolo, Campana, Simone, Canal, Philippe, Canali, Luca, Carlino, Gianpaolo, Castro, Nuno, Cattaneo, Marco, Cerminara, Gianluca, Villanueva, Javier Cervantes, Chang, Philip, Chapman, John, Chen, Gang, Childers, Taylor, Clarke, Peter, Clemencic, Marco, Cogneras, Eric, Coles, Jeremy, Collier, Ian, Colling, David, Corti, Gloria, Cosmo, Gabriele, Costanzo, Davide, Couturier, Ben, Cranmer, Kyle, Cranshaw, Jack, Cristella, Leonardo, Crooks, David, Crépé-Renaudin, Sabine, Currie, Robert, Dallmeier-Tiessen, Sünje, De, Kaushik, De Cian, Michel, De Roeck, Albert, Peris, Antonio Delgado, Derue, Frédéric, Di Girolamo, Alessandro, Di Guida, Salvatore, Dimitrov, Gancho, Doglioni, Caterina, Dotti, Andrea, Duellmann, Dirk, Duflot, Laurent, Dykstra, Dave, Dziedziniewicz-Wojcik, Katarzyna, Dziurda, Agnieszka, Egede, Ulrik, Elmer, Peter, Elmsheuser, Johannes, Elvira, V. Daniel, Eulisse, Giulio, Farrell, Steven, Ferber, Torben, Filipcic, Andrej, Fisk, Ian, Fitzpatrick, Conor, Flix, José, Formica, Andrea, Forti, Alessandra, Franzoni, Giovanni, Frost, James, Fuess, Stu, Gaede, Frank, Ganis, Gerardo, Gardner, Robert, Garonne, Vincent, Gellrich, Andreas, Genser, Krzysztof, George, Simon, Geurts, Frank, Gheata, Andrei, Gheata, Mihaela, Giacomini, Francesco, Giagu, Stefano, Giffels, Manuel, Gingrich, Douglas, Girone, Maria, Gligorov, Vladimir V., Glushkov, Ivan, Gohn, Wesley, Lopez, Jose Benito Gonzalez, Caballero, Isidro González, Fernández, Juan R. González, Govi, Giacomo, Grandi, Claudio, Grasland, Hadrien, Gray, Heather, Grillo, Lucia, Guan, Wen, Gutsche, Oliver, Gyurjyan, Vardan, Hanushevsky, Andrew, Hariri, Farah, Hartmann, Thomas, Harvey, John, Hauth, Thomas, Hegner, Benedikt, Heinemann, Beate, Heinrich, Lukas, Heiss, Andreas, Hernández, José M., Hildreth, Michael, Hodgkinson, Mark, Hoeche, Stefan, Holzman, Burt, Hristov, Peter, Huang, Xingtao, Ivanchenko, Vladimir N., Ivanov, Todor, Iven, Jan, Jashal, Brij, Jayatilaka, Bodhitha, Jones, Roger, Jouvin, Michel, Jun, Soon Yung, Kagan, Michael, Kalderon, Charles William, Kane, Meghan, Karavakis, Edward, Katz, Daniel S., Kcira, Dorian, Keeble, Oliver, Kersevan, Borut Paul, Kirby, Michael, Klimentov, Alexei, Klute, Markus, Komarov, Ilya, Konstantinov, Dmitri, Koppenburg, Patrick, Kowalkowski, Jim, Kreczko, Luke, Kuhr, Thomas, Kutschke, Robert, Kuznetsov, Valentin, Lampl, Walter, Lancon, Eric, Lange, David, Lassnig, Mario, Laycock, Paul, Leggett, Charles, Letts, James, Lewendel, Birgit, Li, Teng, Lima, Guilherme, Linacre, Jacob, Linden, Tomas, Livny, Miron, Presti, Giuseppe Lo, Lopienski, Sebastian, Love, Peter, Lyon, Adam, Magini, Nicolò, Marshall, Zachary L., Martelli, Edoardo, Martin-Haugh, Stewart, Mato, Pere, Mazumdar, Kajari, McCauley, Thomas, McFayden, Josh, McKee, Shawn, McNab, Andrew, Mehdiyev, Rashid, Meinhard, Helge, Menasce, Dario, Lorenzo, Patricia Mendez, Mete, Alaettin Serhan, Michelotto, Michele, Mitrevski, Jovan, Moneta, Lorenzo, Morgan, Ben, Mount, Richard, Moyse, Edward, Murray, Sean, Nairz, Armin, Neubauer, Mark S., Norman, Andrew, Novaes, Sérgio, Novak, Mihaly, Oyanguren, Arantza, Ozturk, Nurcan, Pages, Andres Pacheco, Paganini, Michela, Pansanel, Jerome, Pascuzzi, Vincent R., Patrick, Glenn, Pearce, Alex, Pearson, Ben, Pedro, Kevin, Perdue, Gabriel, Yzquierdo, Antonio Perez-Calero, Perrozzi, Luca, Petersen, Troels, Petric, Marko, Petzold, Andreas, Piedra, Jónatan, Piilonen, Leo, Piparo, Danilo, Pivarski, Jim, Pokorski, Witold, Polci, Francesco, Potamianos, Karolos, Psihas, Fernanda, Navarro, Albert Puig, Quast, Günter, Raven, Gerhard, Reuter, Jürgen, Ribon, Alberto, Rinaldi, Lorenzo, Ritter, Martin, Robinson, James, Rodrigues, Eduardo, Roiser, Stefan, Rousseau, David, Roy, Gareth, Rybkine, Grigori, Sailer, Andre, Sakuma, Tai, Santana, Renato, Sartirana, Andrea, Schellman, Heidi, Schovancová, Jaroslava, Schramm, Steven, Schulz, Markus, Sciabà, Andrea, Seidel, Sally, Sekmen, Sezen, Serfon, Cedric, Severini, Horst, Sexton-Kennedy, Elizabeth, Seymour, Michael, Sgalaberna, Davide, Shapoval, Illya, Shiers, Jamie, Shiu, Jing-Ge, Short, Hannah, Siroli, Gian Piero, Skipsey, Sam, Smith, Tim, Snyder, Scott, Sokoloff, Michael D., Spentzouris, Panagiotis, Stadie, Hartmut, Stark, Giordon, Stewart, Gordon, Stewart, Graeme A., Sánchez, Arturo, Sánchez-Hernández, Alberto, Taffard, Anyes, Tamponi, Umberto, Templon, Jeff, Tenaglia, Giacomo, Tsulaia, Vakhtang, Tunnell, Christopher, Vaandering, Eric, Valassi, Andrea, Vallecorsa, Sofia, Valsan, Liviu, Van Gemmeren, Peter, Vernet, Renaud, Viren, Brett, Vlimant, Jean-Roch, Voss, Christian, Votava, Margaret, Vuosalo, Carl, Sierra, Carlos Vázquez, Wartel, Romain, Watts, Gordon T., Wenaus, Torre, Wenzel, Sandro, Williams, Mike, Winklmeier, Frank, Wissing, Christoph, Wuerthwein, Frank, Wynne, Benjamin, Xiaomei, Zhang, Yang, Wei, and Yazgan, Efe

Subjects
Physics - Computational Physics, High Energy Physics - Experiment
Abstract

Particle physics has an ambitious and broad experimental programme for the coming decades. This programme requires large investments in detector hardware, either to build new facilities and experiments, or to upgrade existing ones. Similarly, it requires commensurate investment in the R&D of software to acquire, manage, process, and analyse the shear amounts of data to be recorded. In planning for the HL-LHC in particular, it is critical that all of the collaborating stakeholders agree on the software goals and priorities, and that the efforts complement each other. In this spirit, this white paper describes the R&D activities required to prepare for this software upgrade.

Published
2017
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20. Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC

Author

Bhimji, Wahid, Farrell, Steven Andrew, Kurth, Thorsten, Paganini, Michela, Prabhat, and Racah, Evan

Subjects
High Energy Physics - Experiment, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Learning, Physics - Data Analysis, Statistics and Probability
Abstract

There has been considerable recent activity applying deep convolutional neural nets (CNNs) to data from particle physics experiments. Current approaches on ATLAS/CMS have largely focussed on a subset of the calorimeter, and for identifying objects or particular particle types. We explore approaches that use the entire calorimeter, combined with track information, for directly conducting physics analyses: i.e. classifying events as known-physics background or new-physics signals. We use an existing RPV-Supersymmetry analysis as a case study and explore CNNs on multi-channel, high-resolution sparse images: applied on GPU and multi-node CPU architectures (including Knights Landing (KNL) Xeon Phi nodes) on the Cori supercomputer at NERSC., Comment: Presented at ACAT 2017 Conference, Submitted to J. Phys. Conf. Ser

Published
2017

21. Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data

Author

Kurth, Thorsten, Zhang, Jian, Satish, Nadathur, Mitliagkas, Ioannis, Racah, Evan, Patwary, Mostofa Ali, Malas, Tareq, Sundaram, Narayanan, Bhimji, Wahid, Smorkalov, Mikhail, Deslippe, Jack, Shiryaev, Mikhail, Sridharan, Srinivas, Prabhat, and Dubey, Pradeep

Subjects
Computer Science - Performance, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Learning
Abstract

This paper presents the first, 15-PetaFLOP Deep Learning system for solving scientific pattern classification problems on contemporary HPC architectures. We develop supervised convolutional architectures for discriminating signals in high-energy physics data as well as semi-supervised architectures for localizing and classifying extreme weather in climate data. Our Intelcaffe-based implementation obtains $\sim$2TFLOP/s on a single Cori Phase-II Xeon-Phi node. We use a hybrid strategy employing synchronous node-groups, while using asynchronous communication across groups. We use this strategy to scale training of a single model to $\sim$9600 Xeon-Phi nodes; obtaining peak performance of 11.73-15.07 PFLOP/s and sustained performance of 11.41-13.27 PFLOP/s. At scale, our HEP architecture produces state-of-the-art classification accuracy on a dataset with 10M images, exceeding that achieved by selections on high-level physics-motivated features. Our semi-supervised architecture successfully extracts weather patterns in a 15TB climate dataset. Our results demonstrate that Deep Learning can be optimized and scaled effectively on many-core, HPC systems., Comment: 12 pages, 9 figures

Published
2017

22. CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks

Author

Mustafa, Mustafa, Bard, Deborah, Bhimji, Wahid, Lukić, Zarija, Al-Rfou, Rami, and Kratochvil, Jan M.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Machine Learning
Abstract

Inferring model parameters from experimental data is a grand challenge in many sciences, including cosmology. This often relies critically on high fidelity numerical simulations, which are prohibitively computationally expensive. The application of deep learning techniques to generative modeling is renewing interest in using high dimensional density estimators as computationally inexpensive emulators of fully-fledged simulations. These generative models have the potential to make a dramatic shift in the field of scientific simulations, but for that shift to happen we need to study the performance of such generators in the precision regime needed for science applications. To this end, in this work we apply Generative Adversarial Networks to the problem of generating weak lensing convergence maps. We show that our generator network produces maps that are described by, with high statistical confidence, the same summary statistics as the fully simulated maps., Comment: 11 pages, 8 figures

Published
2017
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24. Interactive Distributed Deep Learning with Jupyter Notebooks

Author

Farrell, Steve, Vose, Aaron, Evans, Oliver, Henderson, Matthew, Cholia, Shreyas, Pérez, Fernando, Bhimji, Wahid, Canon, Shane, Thomas, Rollin, and Prabhat

Subjects
Jupyter, Deep learning, Distributed training, Hyperparameter optimization, High-performance computing, Genetic algorithms, Artificial Intelligence & Image Processing
Abstract

Deep learning researchers are increasingly using Jupyter notebooks to implement interactive, reproducible workflows with embedded visualization, steering and documentation. Such solutions are typically deployed on small-scale (e.g. single server) computing systems. However, as the sizes and complexities of datasets and associated neural network models increase, high-performance distributed systems become important for training and evaluating models in a feasible amount of time. In this paper we describe our vision for Jupyter notebook solutions to deploy deep learning workloads onto high-performance computing systems. We demonstrate the effectiveness of notebooks for distributed training and hyper-parameter optimization of deep neural networks with efficient, scalable backends.

Published
2018

25. PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures

Author

Patwary, Md. Mostofa Ali, Satish, Nadathur Rajagopalan, Sundaram, Narayanan, Liu, Jialin, Sadowski, Peter, Racah, Evan, Byna, Suren, Tull, Craig, Bhimji, Wahid, Prabhat, and Dubey, Pradeep

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Computing $k$-Nearest Neighbors (KNN) is one of the core kernels used in many machine learning, data mining and scientific computing applications. Although kd-tree based $O(\log n)$ algorithms have been proposed for computing KNN, due to its inherent sequentiality, linear algorithms are being used in practice. This limits the applicability of such methods to millions of data points, with limited scalability for Big Data analytics challenges in the scientific domain. In this paper, we present parallel and highly optimized kd-tree based KNN algorithms (both construction and querying) suitable for distributed architectures. Our algorithm includes novel approaches for pruning search space and improving load balancing and partitioning among nodes and threads. Using TB-sized datasets from three science applications: astrophysics, plasma physics, and particle physics, we show that our implementation can construct kd-tree of 189 billion particles in 48 seconds on utilizing $\sim$50,000 cores. We also demonstrate computation of KNN of 19 billion queries in 12 seconds. We demonstrate almost linear speedup both for shared and distributed memory computers. Our algorithms outperforms earlier implementations by more than order of magnitude; thereby radically improving the applicability of our implementation to state-of-the-art Big Data analytics problems. In addition, we showcase performance and scalability on the recently released Intel Xeon Phi processor showing that our algorithm scales well even on massively parallel architectures., Comment: 11 pages in PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures, Md. Mostofa Ali Patwary et.al., IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2016

Published
2016
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26. Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks

Author

Racah, Evan, Ko, Seyoon, Sadowski, Peter, Bhimji, Wahid, Tull, Craig, Oh, Sang-Yun, Baldi, Pierre, and Prabhat

Subjects
Statistics - Machine Learning, Computer Science - Learning, Physics - Data Analysis, Statistics and Probability
Abstract

Experiments in particle physics produce enormous quantities of data that must be analyzed and interpreted by teams of physicists. This analysis is often exploratory, where scientists are unable to enumerate the possible types of signal prior to performing the experiment. Thus, tools for summarizing, clustering, visualizing and classifying high-dimensional data are essential. In this work, we show that meaningful physical content can be revealed by transforming the raw data into a learned high-level representation using deep neural networks, with measurements taken at the Daya Bay Neutrino Experiment as a case study. We further show how convolutional deep neural networks can provide an effective classification filter with greater than 97% accuracy across different classes of physics events, significantly better than other machine learning approaches.

Published
2016
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27. Experiences with the Burst Buffer at NERSC:

Author

Bard, Debbie, Bhimji, Wahid, Paul, David, Lockwood, Glenn K, Wright, Nicholas J, Antypas, Katie, Prabhat, Prabhat, Farrell, Steve, Ovsyannikov, Andrey, Romanus, Melissa, Van Straalen, Brian, Trebotich, David, and Weber, Guenter

Abstract

NVRAM-based Burst Buffers are an important part of the emerging HPC storage landscape. The National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory recently installed one of the first Burst Buffer systems as part of its new Cori supercomputer, collaborating with Cray on the development of the DataWarp software. NERSC has over 6500 users in 750 different projects spanning a wide variety of scientific applications, including climate modeling, combustion, fusion, astrophysics, computational biology, and many more. The applications of the Burst Buffer at NERSC are therefore also considerable and diverse. We describe here experiences with the first year of the NERSC Burst Buffer. A number of research projects have had early access to the Burst Buffer and exercise its different capabilities to enable new scientific advancements. We present in-depth performance results and lessons-learned from these real applications as well as benchmark results and system configuration experiences.

Published
2016

29. Establishing Applicability of SSDs to LHC Tier-2 Hardware Configuration

Author

Skipsey, Samuel C, Bhimji, Wahid, and Kenyon, Mike

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Solid State Disk technologies are increasingly replacing high-speed hard disks as the storage technology in high-random-I/O environments. There are several potentially I/O bound services within the typical LHC Tier-2 - in the back-end, with the trend towards many-core architectures continuing, worker nodes running many single-threaded jobs and storage nodes delivering many simultaneous files can both exhibit I/O limited efficiency. We estimate the effectiveness of affordable SSDs in the context of worker nodes, on a large Tier-2 production setup using both low level tools and real LHC I/O intensive data analysis jobs comparing and contrasting with high performance spinning disk based solutions. We consider the applicability of each solution in the context of its price/performance metrics, with an eye on the pragmatic issues facing Tier-2 provision and upgrades, Comment: 6 pages, 1 figure, 4 tables. Conference proceedings for CHEP2010

Published
2011
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31. 2019 Computing Sciences Strategic Plan

Author

Yelick, Kathy, primary, Agarwal, Deb, additional, Bard, Debbie, additional, Shalf, John, additional, Almgren, Ann, additional, Bhimji, Wahid, additional, Brown, Ben, additional, Carter, Jonathan, additional, Jong, Bert, additional, Doerfler, Doug, additional, Donofrio, David, additional, Guok, Chin, additional, Iancu, Costin, additional, Kiran, Mariam, additional, Li, Sherry, additional, Nugent, Peter, additional, Prabhat, M., additional, Ramakrishnan, Lavanya, additional, Vasudevan, Dilip, additional, Wright, Nick, additional, Cademartori, Helen, additional, Antypas, Katie, additional, and Kincade, Kathy, additional

Published
2021
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33. The NERSC Cori HPC System

Author

Brian Austin, Katie Antypas, primary, Bard, Deborah, additional, Bhimji, Wahid, additional, Cook, Brandon, additional, Declerck, Tina, additional, Deslippe, Jack, additional, Gerber, Richard, additional, Hartman–Baker, Rebecca, additional, He, Yun (Helen), additional, Jacobsen, Douglas, additional, Kurth, Thorsten, additional, Srinivasan, Jay, additional, and Wright, Nicholas J., additional

Published
2019
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34. The NERSC Cori HPC System

Author

Austin, Katie Antypas Brian, Bard, Deborah, Bhimji, Wahid, Cook, Brandon, Declerck, Tina, Deslippe, Jack, Gerber, Richard, Hartman–Baker, Rebecca, He, Yun, Jacobsen, Douglas, Kurth, Thorsten, Srinivasan, Jay, and Wright, Nicholas J

Subjects
Information and Computing Sciences, Applied Computing
Published
2019

38. Next Generation Generative Neural Networks for HEP

Author

Farrell Steven, Bhimji Wahid, Kurth Thorsten, Mustafa Mustafa, Bard Deborah, Lukic Zarija, Nachman Benjamin, and Patton Harley

Subjects
Physics, QC1-999
Abstract

Initial studies have suggested generative adversarial networks (GANs) have promise as fast simulations within HEP. These studies, while promising, have been insufficiently precise and also, like GANs in general, suffer from stability issues.We apply GANs to to generate full particle physics events (not individual physics objects), explore conditioning of generated events based on physics theory parameters and evaluate the precision and generalization of the produced datasets. We apply this to SUSY mass parameter interpolation and pileup generation. We also discuss recent developments in convergence and representations that match the structure of the detector better than images.In addition we describe on-going work making use of large-scale distributed resources on the Cori supercomputer at NERSC, and developments to control distributed training via interactive jupyter notebook sessions. This will allow tackling high-resolution detector data; model selection and hyper-parameter tuning in a productive yet scalable deep learning environment.

Published
2019
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39. Long-term stability and generalization of observationallyconstrained stochastic data-driven models for geophysical turbulence.

Author

Chattopadhyay, Ashesh, Pathak, Jaideep, Nabizadeh, Ebrahim, Bhimji, Wahid, and Hassanzadeh, Pedram

Subjects
GEOPHYSICS, STOCHASTIC analysis, PARAMETERIZATION, ATMOSPHERIC models, DEEP learning
Abstract

Recent years have seen a surge in interest in building deep learning-based fully data-driven models for weather prediction. Such deep learning models, if trained on observations can mitigate certain biases in current state-of-the-art weather models, some of which stem from inaccurate representation of subgrid-scale processes. However, these datadriven models, being over-parameterized, require a lot of training data which may not be available from reanalysis (observational data) products. Moreover, an accurate, noise-free, initial condition to start forecasting with a datadriven weather model is not available in realistic scenarios. Finally, deterministic data-driven forecasting models suffer from issues with long-term stability and unphysical climate drift, which makes these data-driven models unsuitable for computing climate statistics. Given these challenges, previous studies have tried to pre-train deep learning-based weather forecasting models on a large amount of imperfect long-term climate model simulations and then re-train them on available observational data. In this article, we propose a convolutional variational autoencoder (VAE)-based stochastic data-driven model that is pre-trained on an imperfect climate model simulation from a twolayer quasi-geostrophic flow and re-trained, using transfer learning, on a small number of noisy observations from a perfect simulation. This re-trained model then performs stochastic forecasting with a noisy initial condition sampled from the perfect simulation. We show that our ensemble-based stochastic data-driven model outperforms a baseline deterministic encoder–decoder-based convolutional model in terms of short-term skills, while remaining stable for long-term climate simulations yielding accurate climatology. [ABSTRACT FROM AUTHOR]

Published
2023
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41. Storage 2020: A Vision for the Future of HPC Storage

Author

Lockwood, Glenn, primary, Hazen, Damien, additional, Koziol, Quincey, additional, Canon, R., additional, Antypas, Katie, additional, Balewski, Jan, additional, Balthaser, Nicholas, additional, Bhimji, Wahid, additional, Botts, James, additional, Broughton, Jeff, additional, Butler, Tina, additional, Butler, Gregory, additional, Cheema, Ravi, additional, Daley, Christopher, additional, Declerck, Tina, additional, Gerhardt, Lisa, additional, Hurlbert, Wayne, additional, Kallback-Rose, Kristy, additional, Leak, Stephen, additional, Lee, Jason, additional, Lee, Rei, additional, Liu, Jianlin, additional, Lozinskiy, Kirill, additional, Paul, David, additional, Prabhat, None, additional, Snavely, Cory, additional, Srinivasan, Jay, additional, Stone Gibbins, Tavia, additional, and Wright, Nicholas, additional

Published
2017
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42. Machine Learning in High Energy Physics Community White Paper

Author

Massachusetts Institute of Technology. Department of Physics, Albertsson, Kim, Altoe, Piero, Anderson, Dustin, Andrews, Michael, Araque Espinosa, Juan Pedro, Aurisano, Adam, Basara, Laurent, Bevan, Adrian, Bhimji, Wahid, Bonacorsi, Daniele, Calafiura, Paolo, Campanelli, Mario, Capps, Louis, Carminati, Federico, Carrazza, Stefano, Childers, Taylor, Coniavitis, Elias, Cranmer, Kyle, David, Claire, Davis, Douglas, Duarte, Javier, Erdmann, Martin, Eschle, Jonas, Farbin, Amir, Feickert, Matthew, Castro, Nuno Filipe, Fitzpatrick, Conor, Floris, Michele, Forti, Alessandra, Garra-Tico, Jordi, Gemmler, Jochen, Girone, Maria, Glaysher, Paul, Gleyzer, Sergei, Gligorov, Vladimir, Golling, Tobias, Graw, Jonas, Gray, Lindsey, Greenwood, Dick, Hacker, Thomas, Harvey, John, Hegner, Benedikt, Heinrich, Lukas, Hooberman, Ben, Junggeburth, Johannes, Kagan, Michael, Kane, Meghan, Kanishchev, Konstantin, Karpiński, Przemysław, Kassabov, Zahari, Kaul, Gaurav, Kcira, Dorian, Keck, Thomas, Klimentov, Alexei, Kowalkowski, Jim, Kreczko, Luke, Kurepin, Alexander, Kutschke, Rob, Kuznetsov, Valentin, Köhler, Nicolas, Lakomov, Igor, Lannon, Kevin, Lassnig, Mario, Limosani, Antonio, Louppe, Gilles, Mangu, Aashrita, Mato, Pere, Meinhard, Helge, Menasce, Dario, Moneta, Lorenzo, Moortgat, Seth, Narain, Meenakshi, Neubauer, Mark, Newman, Harvey, Pabst, Hans, Paganini, Michela, Paulini, Manfred, Perdue, Gabriel, Perez, Uzziel, Picazio, Attilio, Pivarski, Jim, Prosper, Harrison, Psihas, Fernanda, Radovic, Alexander, Reece, Ryan, Rinkevicius, Aurelius, Rodrigues, Eduardo, Rorie, Jamal, Rousseau, David, Sauers, Aaron, Schramm, Steven, Schwartzman, Ariel, Severini, Horst, Seyfert, Paul, Siroky, Filip, Skazytkin, Konstantin, Sokoloff, Mike, Stewart, Graeme, Stienen, Bob, Stockdale, Ian, Strong, Giles, Thais, Savannah, Tomko, Karen, Upfal, Eli, Usai, Emanuele, Ustyuzhanin, Andrey, Vala, Martin, Vallecorsa, Sofia, Vasel, Justin, Verzetti, Mauro, Vilasís-Cardona, Xavier, Vlimant, Jean-Roch, Vukotic, Ilija, Wang, Sean-Jiun, Watts, Gordon, Williams, Michael, Wu, Wenjing, Wunsch, Stefan, Zapata, Omar, Massachusetts Institute of Technology. Department of Physics, Albertsson, Kim, Altoe, Piero, Anderson, Dustin, Andrews, Michael, Araque Espinosa, Juan Pedro, Aurisano, Adam, Basara, Laurent, Bevan, Adrian, Bhimji, Wahid, Bonacorsi, Daniele, Calafiura, Paolo, Campanelli, Mario, Capps, Louis, Carminati, Federico, Carrazza, Stefano, Childers, Taylor, Coniavitis, Elias, Cranmer, Kyle, David, Claire, Davis, Douglas, Duarte, Javier, Erdmann, Martin, Eschle, Jonas, Farbin, Amir, Feickert, Matthew, Castro, Nuno Filipe, Fitzpatrick, Conor, Floris, Michele, Forti, Alessandra, Garra-Tico, Jordi, Gemmler, Jochen, Girone, Maria, Glaysher, Paul, Gleyzer, Sergei, Gligorov, Vladimir, Golling, Tobias, Graw, Jonas, Gray, Lindsey, Greenwood, Dick, Hacker, Thomas, Harvey, John, Hegner, Benedikt, Heinrich, Lukas, Hooberman, Ben, Junggeburth, Johannes, Kagan, Michael, Kane, Meghan, Kanishchev, Konstantin, Karpiński, Przemysław, Kassabov, Zahari, Kaul, Gaurav, Kcira, Dorian, Keck, Thomas, Klimentov, Alexei, Kowalkowski, Jim, Kreczko, Luke, Kurepin, Alexander, Kutschke, Rob, Kuznetsov, Valentin, Köhler, Nicolas, Lakomov, Igor, Lannon, Kevin, Lassnig, Mario, Limosani, Antonio, Louppe, Gilles, Mangu, Aashrita, Mato, Pere, Meinhard, Helge, Menasce, Dario, Moneta, Lorenzo, Moortgat, Seth, Narain, Meenakshi, Neubauer, Mark, Newman, Harvey, Pabst, Hans, Paganini, Michela, Paulini, Manfred, Perdue, Gabriel, Perez, Uzziel, Picazio, Attilio, Pivarski, Jim, Prosper, Harrison, Psihas, Fernanda, Radovic, Alexander, Reece, Ryan, Rinkevicius, Aurelius, Rodrigues, Eduardo, Rorie, Jamal, Rousseau, David, Sauers, Aaron, Schramm, Steven, Schwartzman, Ariel, Severini, Horst, Seyfert, Paul, Siroky, Filip, Skazytkin, Konstantin, Sokoloff, Mike, Stewart, Graeme, Stienen, Bob, Stockdale, Ian, Strong, Giles, Thais, Savannah, Tomko, Karen, Upfal, Eli, Usai, Emanuele, Ustyuzhanin, Andrey, Vala, Martin, Vallecorsa, Sofia, Vasel, Justin, Verzetti, Mauro, Vilasís-Cardona, Xavier, Vlimant, Jean-Roch, Vukotic, Ilija, Wang, Sean-Jiun, Watts, Gordon, Williams, Michael, Wu, Wenjing, Wunsch, Stefan, and Zapata, Omar

Abstract

© Published under licence by IOP Publishing Ltd. Machine learning is an important applied research area in particle physics, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas in machine learning in particle physics with a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit.

Published
2021

43. A roadmap for HEP software and computing R&D for the 2020s

Author

Massachusetts Institute of Technology. Department of Physics, Albrecht, Johannes, Alves, Antonio A, Amadio, Guilherme, Andronico, Giuseppe, Anh-Ky, Nguyen, Aphecetche, Laurent, Apostolakis, John, Asai, Makoto, Atzori, Luca, Babik, Marian, Bagliesi, Giuseppe, Bandieramonte, Marilena, Banerjee, Sunanda, Barisits, Martin, Bauerdick, Lothar A T, Belforte, Stefano, Benjamin, Douglas, Bernius, Catrin, Bhimji, Wahid, Bianchi, Riccardo M, Bird, Ian, Biscarat, Catherine, Blomer, Jakob, Bloom, Kenneth, Boccali, Tommaso, Bockelman, Brian, Bold, Tomasz, Bonacorsi, Daniele, Boveia, Antonio, Bozzi, Concezio, Bracko, Marko, Britton, David, Buckley, Andy, Buncic, Predrag, Calafiura, Paolo, Campana, Simone, Canal, Philippe, Canali, Luca, Carlino, Gianpaolo, Castro, Nuno, Cattaneo, Marco, Cerminara, Gianluca, Cervantes Villanueva, Javier, Chang, Philip, Chapman, John, Chen, Gang, Childers, Taylor, Clarke, Peter, Clemencic, Marco, Cogneras, Eric, Coles, Jeremy, Collier, Ian, Colling, David, Corti, Gloria, Cosmo, Gabriele, Costanzo, Davide, Couturier, Ben, Cranmer, Kyle, Cranshaw, Jack, Cristella, Leonardo, Crooks, David, Crépé-Renaudin, Sabine, Currie, Robert, Dallmeier-Tiessen, Sünje, De, Kaushik, De Cian, Michel, De Roeck, Albert, Delgado Peris, Antonio, Derue, Frédéric, Di Girolamo, Alessandro, Di Guida, Salvatore, Dimitrov, Gancho, Doglioni, Caterina, Dotti, Andrea, Duellmann, Dirk, Duflot, Laurent, Dykstra, Dave, Dziedziniewicz-Wojcik, Katarzyna, Dziurda, Agnieszka, Egede, Ulrik, Elmer, Peter, Elmsheuser, Johannes, Elvira, V. D, Eulisse, Giulio, Farrell, Steven, Ferber, Torben, Filipcic, Andrej, Fisk, Ian, Fitzpatrick, Conor, Flix, José, Formica, Andrea, Forti, Alessandra, Franzoni, Giovanni, Frost, James, Fuess, Stu, Gaede, Frank, Ganis, Gerardo, Gardner, Robert, Garonne, Vincent, Gellrich, Andreas, Genser, Krzysztof, George, Simon, Geurts, Frank, Gheata, Andrei, Gheata, Mihaela, Giacomini, Francesco, Giagu, Stefano, Giffels, Manuel, Gingrich, Douglas, Girone, Maria, Gligorov, Vladimir V, Glushkov, Ivan, Gohn, Wesley, Gonzalez Lopez, Jose B, González Caballero, Isidro, González Fernández, Juan R, Govi, Giacomo, Grandi, Claudio, Grasland, Hadrien, Gray, Heather, Grillo, Lucia, Guan, Wen, Gutsche, Oliver, Gyurjyan, Vardan, Hanushevsky, Andrew, Hariri, Farah, Hartmann, Thomas, Harvey, John, Hauth, Thomas, Hegner, Benedikt, Heinemann, Beate, Heinrich, Lukas, Heiss, Andreas, Hernández, José M, Hildreth, Michael, Hodgkinson, Mark, Hoeche, Stefan, Holzman, Burt, Hristov, Peter, Huang, Xingtao, Ivanchenko, Vladimir N, Ivanov, Todor, Iven, Jan, Jashal, Brij, Jayatilaka, Bodhitha, Jones, Roger, Jouvin, Michel, Jun, Soon Y, Kagan, Michael, Kalderon, Charles W, Kane, Meghan, Karavakis, Edward, Katz, Daniel S, Kcira, Dorian, Keeble, Oliver, Kersevan, Borut P, Kirby, Michael, Klimentov, Alexei, Klute, Markus, Komarov, Ilya, Konstantinov, Dmitri, Koppenburg, Patrick, Kowalkowski, Jim, Kreczko, Luke, Kuhr, Thomas, Kutschke, Robert, Kuznetsov, Valentin, Lampl, Walter, Lancon, Eric, Lange, David, Lassnig, Mario, Laycock, Paul, Leggett, Charles, Letts, James, Lewendel, Birgit, Li, Teng, Lima, Guilherme, Linacre, Jacob, Linden, Tomas, Livny, Miron, Lo Presti, Giuseppe, Lopienski, Sebastian, Love, Peter, Lyon, Adam, Magini, Nicolò, Marshall, Zachary L, Martelli, Edoardo, Martin-Haugh, Stewart, Mato, Pere, Mazumdar, Kajari, McCauley, Thomas, McFayden, Josh, McKee, Shawn, McNab, Andrew, Mehdiyev, Rashid, Meinhard, Helge, Menasce, Dario, Mendez Lorenzo, Patricia, Mete, Alaettin S, Michelotto, Michele, Mitrevski, Jovan, Moneta, Lorenzo, Morgan, Ben, Mount, Richard, Moyse, Edward, Murray, Sean, Nairz, Armin, Neubauer, Mark S, Norman, Andrew, Novaes, Sérgio, Novak, Mihaly, Oyanguren, Arantza, Ozturk, Nurcan, Pacheco Pages, Andres, Paganini, Michela, Pansanel, Jerome, Pascuzzi, Vincent R, Patrick, Glenn, Pearce, Alex, Pearson, Ben, Pedro, Kevin, Perdue, Gabriel, Perez-Calero Yzquierdo, Antonio, Perrozzi, Luca, Petersen, Troels, Petric, Marko, Petzold, Andreas, Piedra, Jónatan, Piilonen, Leo, Piparo, Danilo, Pivarski, Jim, Pokorski, Witold, Polci, Francesco, Potamianos, Karolos, Psihas, Fernanda, Puig Navarro, Albert, Quast, Günter, Raven, Gerhard, Reuter, Jürgen, Ribon, Alberto, Rinaldi, Lorenzo, Ritter, Martin, Robinson, James, Rodrigues, Eduardo, Roiser, Stefan, Rousseau, David, Roy, Gareth, Rybkine, Grigori, Sailer, Andre, Sakuma, Tai, Santana, Renato, Sartirana, Andrea, Schellman, Heidi, Schovancová, Jaroslava, Schramm, Steven, Schulz, Markus, Sciabà, Andrea, Seidel, Sally, Sekmen, Sezen, Serfon, Cedric, Severini, Horst, Sexton-Kennedy, Elizabeth, Seymour, Michael, Sgalaberna, Davide, Shapoval, Illya, Shiers, Jamie, Shiu, Jing-Ge, Short, Hannah, Siroli, Gian P, Skipsey, Sam, Smith, Tim, Snyder, Scott, Sokoloff, Michael D, Spentzouris, Panagiotis, Stadie, Hartmut, Stark, Giordon, Stewart, Gordon, Stewart, Graeme A, Sánchez, Arturo, Sánchez-Hernández, Alberto, Taffard, Anyes, Tamponi, Umberto, Templon, Jeff, Tenaglia, Giacomo, Tsulaia, Vakhtang, Tunnell, Christopher, Vaandering, Eric, Valassi, Andrea, Vallecorsa, Sofia, Valsan, Liviu, Van Gemmeren, Peter, Vernet, Renaud, Viren, Brett, Vlimant, Jean-Roch, Voss, Christian, Votava, Margaret, Vuosalo, Carl, Vázquez Sierra, Carlos, Wartel, Romain, Watts, Gordon T, Wenaus, Torre, Wenzel, Sandro, Williams, Michael, Winklmeier, Frank, Wissing, Christoph, Wuerthwein, Frank, Wynne, Benjamin, Xiaomei, Zhang, Yang, Wei, Yazgan, Efe, Massachusetts Institute of Technology. Department of Physics, Albrecht, Johannes, Alves, Antonio A, Amadio, Guilherme, Andronico, Giuseppe, Anh-Ky, Nguyen, Aphecetche, Laurent, Apostolakis, John, Asai, Makoto, Atzori, Luca, Babik, Marian, Bagliesi, Giuseppe, Bandieramonte, Marilena, Banerjee, Sunanda, Barisits, Martin, Bauerdick, Lothar A T, Belforte, Stefano, Benjamin, Douglas, Bernius, Catrin, Bhimji, Wahid, Bianchi, Riccardo M, Bird, Ian, Biscarat, Catherine, Blomer, Jakob, Bloom, Kenneth, Boccali, Tommaso, Bockelman, Brian, Bold, Tomasz, Bonacorsi, Daniele, Boveia, Antonio, Bozzi, Concezio, Bracko, Marko, Britton, David, Buckley, Andy, Buncic, Predrag, Calafiura, Paolo, Campana, Simone, Canal, Philippe, Canali, Luca, Carlino, Gianpaolo, Castro, Nuno, Cattaneo, Marco, Cerminara, Gianluca, Cervantes Villanueva, Javier, Chang, Philip, Chapman, John, Chen, Gang, Childers, Taylor, Clarke, Peter, Clemencic, Marco, Cogneras, Eric, Coles, Jeremy, Collier, Ian, Colling, David, Corti, Gloria, Cosmo, Gabriele, Costanzo, Davide, Couturier, Ben, Cranmer, Kyle, Cranshaw, Jack, Cristella, Leonardo, Crooks, David, Crépé-Renaudin, Sabine, Currie, Robert, Dallmeier-Tiessen, Sünje, De, Kaushik, De Cian, Michel, De Roeck, Albert, Delgado Peris, Antonio, Derue, Frédéric, Di Girolamo, Alessandro, Di Guida, Salvatore, Dimitrov, Gancho, Doglioni, Caterina, Dotti, Andrea, Duellmann, Dirk, Duflot, Laurent, Dykstra, Dave, Dziedziniewicz-Wojcik, Katarzyna, Dziurda, Agnieszka, Egede, Ulrik, Elmer, Peter, Elmsheuser, Johannes, Elvira, V. D, Eulisse, Giulio, Farrell, Steven, Ferber, Torben, Filipcic, Andrej, Fisk, Ian, Fitzpatrick, Conor, Flix, José, Formica, Andrea, Forti, Alessandra, Franzoni, Giovanni, Frost, James, Fuess, Stu, Gaede, Frank, Ganis, Gerardo, Gardner, Robert, Garonne, Vincent, Gellrich, Andreas, Genser, Krzysztof, George, Simon, Geurts, Frank, Gheata, Andrei, Gheata, Mihaela, Giacomini, Francesco, Giagu, Stefano, Giffels, Manuel, Gingrich, Douglas, Girone, Maria, Gligorov, Vladimir V, Glushkov, Ivan, Gohn, Wesley, Gonzalez Lopez, Jose B, González Caballero, Isidro, González Fernández, Juan R, Govi, Giacomo, Grandi, Claudio, Grasland, Hadrien, Gray, Heather, Grillo, Lucia, Guan, Wen, Gutsche, Oliver, Gyurjyan, Vardan, Hanushevsky, Andrew, Hariri, Farah, Hartmann, Thomas, Harvey, John, Hauth, Thomas, Hegner, Benedikt, Heinemann, Beate, Heinrich, Lukas, Heiss, Andreas, Hernández, José M, Hildreth, Michael, Hodgkinson, Mark, Hoeche, Stefan, Holzman, Burt, Hristov, Peter, Huang, Xingtao, Ivanchenko, Vladimir N, Ivanov, Todor, Iven, Jan, Jashal, Brij, Jayatilaka, Bodhitha, Jones, Roger, Jouvin, Michel, Jun, Soon Y, Kagan, Michael, Kalderon, Charles W, Kane, Meghan, Karavakis, Edward, Katz, Daniel S, Kcira, Dorian, Keeble, Oliver, Kersevan, Borut P, Kirby, Michael, Klimentov, Alexei, Klute, Markus, Komarov, Ilya, Konstantinov, Dmitri, Koppenburg, Patrick, Kowalkowski, Jim, Kreczko, Luke, Kuhr, Thomas, Kutschke, Robert, Kuznetsov, Valentin, Lampl, Walter, Lancon, Eric, Lange, David, Lassnig, Mario, Laycock, Paul, Leggett, Charles, Letts, James, Lewendel, Birgit, Li, Teng, Lima, Guilherme, Linacre, Jacob, Linden, Tomas, Livny, Miron, Lo Presti, Giuseppe, Lopienski, Sebastian, Love, Peter, Lyon, Adam, Magini, Nicolò, Marshall, Zachary L, Martelli, Edoardo, Martin-Haugh, Stewart, Mato, Pere, Mazumdar, Kajari, McCauley, Thomas, McFayden, Josh, McKee, Shawn, McNab, Andrew, Mehdiyev, Rashid, Meinhard, Helge, Menasce, Dario, Mendez Lorenzo, Patricia, Mete, Alaettin S, Michelotto, Michele, Mitrevski, Jovan, Moneta, Lorenzo, Morgan, Ben, Mount, Richard, Moyse, Edward, Murray, Sean, Nairz, Armin, Neubauer, Mark S, Norman, Andrew, Novaes, Sérgio, Novak, Mihaly, Oyanguren, Arantza, Ozturk, Nurcan, Pacheco Pages, Andres, Paganini, Michela, Pansanel, Jerome, Pascuzzi, Vincent R, Patrick, Glenn, Pearce, Alex, Pearson, Ben, Pedro, Kevin, Perdue, Gabriel, Perez-Calero Yzquierdo, Antonio, Perrozzi, Luca, Petersen, Troels, Petric, Marko, Petzold, Andreas, Piedra, Jónatan, Piilonen, Leo, Piparo, Danilo, Pivarski, Jim, Pokorski, Witold, Polci, Francesco, Potamianos, Karolos, Psihas, Fernanda, Puig Navarro, Albert, Quast, Günter, Raven, Gerhard, Reuter, Jürgen, Ribon, Alberto, Rinaldi, Lorenzo, Ritter, Martin, Robinson, James, Rodrigues, Eduardo, Roiser, Stefan, Rousseau, David, Roy, Gareth, Rybkine, Grigori, Sailer, Andre, Sakuma, Tai, Santana, Renato, Sartirana, Andrea, Schellman, Heidi, Schovancová, Jaroslava, Schramm, Steven, Schulz, Markus, Sciabà, Andrea, Seidel, Sally, Sekmen, Sezen, Serfon, Cedric, Severini, Horst, Sexton-Kennedy, Elizabeth, Seymour, Michael, Sgalaberna, Davide, Shapoval, Illya, Shiers, Jamie, Shiu, Jing-Ge, Short, Hannah, Siroli, Gian P, Skipsey, Sam, Smith, Tim, Snyder, Scott, Sokoloff, Michael D, Spentzouris, Panagiotis, Stadie, Hartmut, Stark, Giordon, Stewart, Gordon, Stewart, Graeme A, Sánchez, Arturo, Sánchez-Hernández, Alberto, Taffard, Anyes, Tamponi, Umberto, Templon, Jeff, Tenaglia, Giacomo, Tsulaia, Vakhtang, Tunnell, Christopher, Vaandering, Eric, Valassi, Andrea, Vallecorsa, Sofia, Valsan, Liviu, Van Gemmeren, Peter, Vernet, Renaud, Viren, Brett, Vlimant, Jean-Roch, Voss, Christian, Votava, Margaret, Vuosalo, Carl, Vázquez Sierra, Carlos, Wartel, Romain, Watts, Gordon T, Wenaus, Torre, Wenzel, Sandro, Williams, Michael, Winklmeier, Frank, Wissing, Christoph, Wuerthwein, Frank, Wynne, Benjamin, Xiaomei, Zhang, Yang, Wei, and Yazgan, Efe

Abstract

Particle physics has an ambitious and broad experimental programme for the coming decades. This programme requires large investments in detector hardware, either to build new facilities and experiments, or to upgrade existing ones. Similarly, it requires commensurate investment in the R&D of software to acquire, manage, process, and analyse the shear amounts of data to be recorded. In planning for the HL-LHC in particular, it is critical that all of the collaborating stakeholders agree on the software goals and priorities, and that the efforts complement each other. In this spirit, this white paper describes the R&D activities required to prepare for this software upgrade. ©2019

Published
2020

46. Deep Learning for HEP on HPC at NERSC

Author

Bhimji, Wahid, Farrell, Steven, and Mustafa, Mustafa

Abstract

We present recent work in supporting deep learning for particle physics and cosmology at NERSC, the US Dept. of Energy mission HPC center. We describe infrastructure and software to support both large-scale distributed training across (CPU and GPU) HPC resources and for productive interfaces via Jupyter notebooks. We also detail plans for accelerated hardware for deep learning in the future HPC machines at NERSC, 'Perlmutter' and beyond. We demonstrate these capabilities with a characterisation of the emerging deep learning workload running at NERSC. We also present use of these resources to implement specific cutting-edge applications including conditional Generative Adversarial Networks for particle physics and dark-matter cosmology simulations and bayesian inference via probabilistic programming for LHC analyses.

Published
2019
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47. Corrigendum: A search for an excited muon decaying to a muon and two jets in pp collisions at $\sqrt{s}=8\,\mathrm{TeV}$ with the ATLAS detector

Author

Aad, Georges, Abbott, Brad, Abreu, Ricardo, Artoni, Giacomo, Harrison, Paul Fraser, Hartjes, Fred, Hasegawa, Makoto, Hasegawa, Yoji, Hasib, A., Hassani, Samira, Haug, Sigve, Hauser, Reiner, Hauswald, Lorenz, Havranek, Miroslav, Artz, Sebastian, Hawkes, Christopher, Hawkings, Richard John, Hawkins, Anthony David, Hayashi, Takayasu, Hayden, Daniel, Hays, Chris, Hays, Jonathan Michael, Hayward, Helen, Haywood, Stephen, Head, Simon, Asai, Shoji, Heck, Tobias, Hedberg, Vincent, Heelan, Louise, Heim, Sarah, Heim, Timon, Heinemann, Beate, Heinrich, Lukas, Hejbal, Jiri, Helary, Louis, Hellman, Sten, Asbah, Nedaa, Helsens, Clement, Henderson, James, Henderson, Robert, Heng, Yang, Henkelmann, Steffen, Henriques Correia, Ana Maria, Henrot-Versille, Sophie, Herbert, Geoffrey Henry, Hernández Jiménez, Yesenia, Herten, Gregor, Ashkenazi, Adi, Hertenberger, Ralf, Hervas, Luis, Hesketh, Gavin Grant, Hessey, Nigel, Hetherly, Jeffrey Wayne, Hickling, Robert, Higón-Rodriguez, Emilio, Hill, Ewan, Hill, John, Hiller, Karl Heinz, Åsman, Barbro, Hillier, Stephen, Hinchliffe, Ian, Hines, Elizabeth, Hinman, Rachel Reisner, Hirose, Minoru, Hirschbuehl, Dominic, Hobbs, John, Hod, Noam, Hodgkinson, Mark, Hodgson, Paul, Asquith, Lily, Hoecker, Andreas, Hoeferkamp, Martin, Hoenig, Friedrich, Hohlfeld, Marc, Hohn, David, Holmes, Tova Ray, Homann, Michael, Hong, Tae Min, Hooberman, Benjamin Henry, Hopkins, Walter, Assamagan, Ketevi, Horii, Yasuyuki, Horton, Arthur James, Hostachy, Jean-Yves, Hou, Suen, Hoummada, Abdeslam, Howard, Jacob, Howarth, James, Hrabovsky, Miroslav, Hristova, Ivana, Hrivnac, Julius, Astalos, Robert, Hryn'ova, Tetiana, Hrynevich, Aliaksei, Hsu, Catherine, Hsu, Pai-hsien Jennifer, Hsu, Shih-Chieh, Hu, Diedi, Hu, Qipeng, Huang, Yanping, Hubacek, Zdenek, Hubaut, Fabrice, Atkinson, Markus, Huegging, Fabian, Huffman, Todd Brian, Hughes, Emlyn, Hughes, Gareth, Huhtinen, Mika, Hülsing, Tobias Alexander, Huseynov, Nazim, Huston, Joey, Huth, John, Iacobucci, Giuseppe, Abulaiti, Yiming, Atlay, Naim Bora, Iakovidis, Georgios, Ibragimov, Iskander, Iconomidou-Fayard, Lydia, Ideal, Emma, Idrissi, Zineb, Iengo, Paolo, Igonkina, Olga, Iizawa, Tomoya, Ikegami, Yoichi, Ikeno, Masahiro, Augsten, Kamil, Ilchenko, Iurii, Iliadis, Dimitrios, Ilic, Nikolina, Ince, Tayfun, Introzzi, Gianluca, Ioannou, Pavlos, Iodice, Mauro, Iordanidou, Kalliopi, Ippolito, Valerio, Irles Quiles, Adrian, Avolio, Giuseppe, Isaksson, Charlie, Ishino, Masaya, Ishitsuka, Masaki, Ishmukhametov, Renat, Issever, Cigdem, Istin, Serhat, Iturbe Ponce, Julia Mariana, Iuppa, Roberto, Ivarsson, Jenny, Iwanski, Wieslaw, Axen, Bradley, Iwasaki, Hiroyuki, Izen, Joseph, Izzo, Vincenzo, Jabbar, Samina, Jackson, Brett, Jackson, Matthew, Jackson, Paul, Jain, Vivek, Jakobi, Katharina Bianca, Jakobs, Karl, Ayoub, Mohamad Kassem, Jakobsen, Sune, Jakoubek, Tomas, Jamin, David Olivier, Jana, Dilip, Jansen, Eric, Jansky, Roland, Janssen, Jens, Janus, Michel, Jarlskog, Göran, Javadov, Namig, Azuelos, Georges, Javůrek, Tomáš, Jeanneau, Fabien, Jeanty, Laura, Jejelava, Juansher, Jeng, Geng-yuan, Jennens, David, Jenni, Peter, Jentzsch, Jennifer, Jeske, Carl, Jézéquel, Stéphane, Baak, Max, Ji, Haoshuang, Jia, Jiangyong, Jiang, Hai, Jiang, Yi, Jiggins, Stephen, Jimenez Pena, Javier, Jin, Shan, Jinaru, Adam, Jinnouchi, Osamu, Johansson, Per, Baas, Alessandra, Johns, Kenneth, Johnson, William Joseph, Jon-And, Kerstin, Jones, Graham, Jones, Roger, Jones, Sarah, Jones, Tim, Jongmanns, Jan, Jorge, Pedro, Jovicevic, Jelena, Baca, Matthew John, Ju, Xiangyang, Juste Rozas, Aurelio, Köhler, Markus Konrad, Kaci, Mohammed, Kaczmarska, Anna, Kado, Marumi, Kagan, Harris, Kagan, Michael, Kahn, Sebastien Jonathan, Kajomovitz, Enrique, Bachacou, Henri, Kalderon, Charles William, Kaluza, Adam, Kama, Sami, Kamenshchikov, Andrey, Kanaya, Naoko, Kaneti, Steven, Kantserov, Vadim, Kanzaki, Junichi, Kaplan, Benjamin, Kaplan, Laser Seymour, Acharya, Bobby Samir, Bachas, Konstantinos, Kapliy, Anton, Kar, Deepak, Karakostas, Konstantinos, Karamaoun, Andrew, Karastathis, Nikolaos, Kareem, Mohammad Jawad, Karentzos, Efstathios, Karnevskiy, Mikhail, Karpov, Sergey, Karpova, Zoya, Backes, Moritz, Karthik, Krishnaiyengar, Kartvelishvili, Vakhtang, Karyukhin, Andrey, Kasahara, Kota, Kashif, Lashkar, Kass, Richard, Kastanas, Alex, Kataoka, Yousuke, Kato, Chikuma, Katre, Akshay, Backhaus, Malte, Katzy, Judith, Kawade, Kentaro, Kawagoe, Kiyotomo, Kawamoto, Tatsuo, Kawamura, Gen, Kazama, Shingo, Kazanin, Vassili, Keeler, Richard, Kehoe, Robert, Keller, John, Bagiacchi, Paolo, Kempster, Jacob Julian, Keoshkerian, Houry, Kepka, Oldrich, Kerševan, Borut Paul, Kersten, Susanne, Keyes, Robert, Khalil-zada, Farkhad, Khandanyan, Hovhannes, Khanov, Alexander, Kharlamov, Alexey, Bagnaia, Paolo, Khoo, Teng Jian, Khovanskiy, Valery, Khramov, Evgeniy, Khubua, Jemal, Kido, Shogo, Kim, Hee Yeun, Kim, Shinhong, Kim, Young-Kee, Kimura, Naoki, Kind, Oliver Maria, Bai, Yu, King, Barry, King, Matthew, King, Samuel Burton, Kirk, Julie, Kiryunin, Andrey, Kishimoto, Tomoe, Kisielewska, Danuta, Kiss, Florian, Kiuchi, Kenji, Kivernyk, Oleh, Baines, John, Kladiva, Eduard, Klein, Matthew Henry, Klein, Max, Klein, Uta, Kleinknecht, Konrad, Klimek, Pawel, Klimentov, Alexei, Klingenberg, Reiner, Klinger, Joel Alexander, Klioutchnikova, Tatiana, Baker, Oliver Keith, Kluge, Eike-Erik, Kluit, Peter, Kluth, Stefan, Knapik, Joanna, Kneringer, Emmerich, Knoops, Edith, Knue, Andrea, Kobayashi, Aine, Kobayashi, Dai, Kobayashi, Tomio, Baldin, Evgenii, Kobel, Michael, Kocian, Martin, Kodys, Peter, Koffas, Thomas, Koffeman, Els, Kogan, Lucy Anne, Kohlmann, Simon, Kohriki, Takashi, Koi, Tatsumi, Kolanoski, Hermann, Balek, Petr, Kolb, Mathis, Koletsou, Iro, Komar, Aston, Komori, Yuto, Kondo, Takahiko, Kondrashova, Nataliia, Köneke, Karsten, König, Adriaan, Kono, Takanori, Konoplich, Rostislav, Adamczyk, Leszek, Balestri, Thomas, Konstantinidis, Nikolaos, Kopeliansky, Revital, Koperny, Stefan, Köpke, Lutz, Kopp, Anna Katharina, Korcyl, Krzysztof, Kordas, Kostantinos, Korn, Andreas, Korol, Aleksandr, Korolkov, Ilya, Balli, Fabrice, Korolkova, Elena, Kortner, Oliver, Kortner, Sandra, Kosek, Tomas, Kostyukhin, Vadim, Kotov, Vladislav, Kotwal, Ashutosh, Kourkoumeli-Charalampidi, Athina, Kourkoumelis, Christine, Kouskoura, Vasiliki, Balunas, William Keaton, Koutsman, Alex, Kowalewski, Robert Victor, Kowalski, Tadeusz, Kozanecki, Witold, Kozhin, Anatoly, Kramarenko, Viktor, Kramberger, Gregor, Krasnopevtsev, Dimitriy, Krasny, Mieczyslaw Witold, Krasznahorkay, Attila, Banas, Elzbieta, Kraus, Jana, Kravchenko, Anton, Kretz, Moritz, Kretzschmar, Jan, Kreutzfeldt, Kristof, Krieger, Peter, Krizka, Karol, Kroeninger, Kevin, Kroha, Hubert, Kroll, Joe, Banerjee, Swagato, Kroseberg, Juergen, Krstic, Jelena, Kruchonak, Uladzimir, Krüger, Hans, Krumnack, Nils, Kruse, Amanda, Kruse, Mark, Kruskal, Michael, Kubota, Takashi, Kucuk, Hilal, Bannoura, Arwa A E, Kuday, Sinan, Kuechler, Jan Thomas, Kuehn, Susanne, Kugel, Andreas, Kuger, Fabian, Kuhl, Andrew, Kuhl, Thorsten, Kukhtin, Victor, Kukla, Romain, Kulchitsky, Yuri, Barak, Liron, Kuleshov, Sergey, Kuna, Marine, Kunigo, Takuto, Kupco, Alexander, Kurashige, Hisaya, Kurochkin, Yurii, Kus, Vlastimil, Kuwertz, Emma Sian, Kuze, Masahiro, Kvita, Jiri, Barberio, Elisabetta Luigia, Kwan, Tony, Kyriazopoulos, Dimitrios, La Rosa, Alessandro, La Rosa Navarro, Jose Luis, La Rotonda, Laura, Lacasta, Carlos, Lacava, Francesco, Lacey, James, Lacker, Heiko, Lacour, Didier, Barberis, Dario, Lacuesta, Vicente Ramón, Ladygin, Evgueni, Lafaye, Remi, Laforge, Bertrand, Lagouri, Theodota, Lai, Stanley, Lambourne, Luke, Lammers, Sabine, Lampen, Caleb, Lampl, Walter, Barbero, Marlon, Lançon, Eric, Landgraf, Ulrich, Landon, Murrough, Lang, Valerie Susanne, Lange, J örn Christian, Lankford, Andrew, Lanni, Francesco, Lantzsch, Kerstin, Lanza, Agostino, Laplace, Sandrine, Adams, David, Barillari, Teresa, Lapoire, Cecile, Laporte, Jean-Francois, Lari, Tommaso, Lasagni Manghi, Federico, Lassnig, Mario, Laurelli, Paolo, Lavrijsen, Wim, Law, Alexander, Laycock, Paul, Lazovich, Tomo, Barisonzi, Marcello, Le Dortz, Olivier, Le Guirriec, Emmanuel, Le Menedeu, Eve, LeBlanc, Matthew Edgar, LeCompte, Thomas, Ledroit-Guillon, Fabienne Agnes Marie, Lee, Claire Alexandra, Lee, Shih-Chang, Lee, Lawrence, Lefebvre, Guillaume, Barklow, Timothy, Lefebvre, Michel, Legger, Federica, Leggett, Charles, Lehan, Allan, Lehmann Miotto, Giovanna, Lei, Xiaowen, Leight, William Axel, Leisos, Antonios, Leister, Andrew Gerard, Leite, Marco Aurelio Lisboa, Barlow, Nick, Leitner, Rupert, Lellouch, Daniel, Lemmer, Boris, Leney, Katharine, Lenz, Tatjana, Lenzi, Bruno, Leone, Robert, Leone, Sandra, Leonidopoulos, Christos, Leontsinis, Stefanos, Barnes, Sarah Louise, Leroy, Claude, Lester, Christopher, Levchenko, Mikhail, Levêque, Jessica, Levin, Daniel, Levinson, Lorne, Levy, Mark, Leyko, Agnieszka, Leyton, Michael, Li, Bing, Barnett, Bruce, Li, Haifeng, Li, Ho Ling, Li, Lei, Li, Liang, Li, Shu, Li, 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David, Barranco Navarro, Laura, Lopez Paredes, Brais, Lopez Paz, Ivan, Lopez Solis, Alvaro, Lorenz, Jeanette, Lorenzo Martinez, Narei, Losada, Marta, Lösel, Philipp Jonathan, Lou, XinChou, Lounis, Abdenour, Love, Jeremy, Barreiro, Fernando, Love, Peter, Lu, Haonan, Lu, Nan, Lubatti, Henry, Luci, Claudio, Lucotte, Arnaud, Luedtke, Christian, Luehring, Frederick, Lukas, Wolfgang, Luminari, Lamberto, Barreiro Guimarães da Costa, João, Lundberg, Olof, Lund-Jensen, Bengt, Lynn, David, Lysak, Roman, Lytken, Else, Ma, Hong, Ma, Lian Liang, Maccarrone, Giovanni, Macchiolo, Anna, Macdonald, Calum Michael, Bartoldus, Rainer, Maček, Boštjan, Machado Miguens, Joana, Madaffari, Daniele, Madar, Romain, Maddocks, Harvey Jonathan, Mader, Wolfgang, Madsen, Alexander, Maeda, Junpei, Maeland, Steffen, Maeno, Tadashi, Barton, Adam Edward, Maevskiy, Artem, Magradze, Erekle, Mahlstedt, Joern, Maiani, Camilla, Maidantchik, Carmen, Maier, Andreas Alexander, Maier, Thomas, Maio, Amélia, Majewski, Stephanie, Makida, Yasuhiro, Bartos, Pavol, Makovec, Nikola, Malaescu, Bogdan, Malecki, Pawel, Maleev, Victor, Malek, Fairouz, Mallik, Usha, Malon, David, Malone, Caitlin, Maltezos, Stavros, Malyshev, Vladimir, Basalaev, Artem, Malyukov, Sergei, Mamuzic, Judita, Mancini, Giada, Mandelli, Beatrice, Mandelli, Luciano, Mandić, Igor, Maneira, José, Manhaes de Andrade Filho, Luciano, Manjarres Ramos, Joany, Mann, Alexander, Bassalat, Ahmed, Mansoulie, Bruno, Mantifel, Rodger, Mantoani, Matteo, Manzoni, Stefano, Mapelli, Livio, March, Luis, Marchiori, Giovanni, Marcisovsky, Michal, Marjanovic, Marija, Marley, Daniel, Basye, Austin, Marroquim, Fernando, Marsden, Stephen Philip, Marshall, Zach, Marti, Lukas Fritz, Marti-Garcia, Salvador, Martin, Brian Thomas, Martin, Tim, Martin, Victoria Jane, Martin dit Latour, Bertrand, Martinez, Mario, Adomeit, Stefanie, Bates, Richard, Martin-Haugh, Stewart, Martoiu, Victor Sorin, Martyniuk, Alex, Marx, Marilyn, Marzano, Francesco, Marzin, Antoine, Masetti, Lucia, 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Fleischmann, Philipp, Anjos, Nuno, Fletcher, Gareth Thomas, Fletcher, Gregory, Fletcher, Rob Roy MacGregor, Flick, Tobias, Floderus, Anders, Flores Castillo, Luis, Flowerdew, Michael, Forcolin, Giulio Tiziano, Formica, Andrea, Forti, Alessandra, Annovi, Alberto, Fournier, Daniel, Fox, Harald, Fracchia, Silvia, Francavilla, Paolo, Franchini, Matteo, Francis, David, Franconi, Laura, Franklin, Melissa, Frate, Meghan, Fraternali, Marco, Abramowicz, Halina, Antonelli, Mario, Freeborn, David, Fressard-Batraneanu, Silvia, Friedrich, Felix, Froidevaux, Daniel, Frost, James, Fukunaga, Chikara, Fullana Torregrosa, Esteban, Fusayasu, Takahiro, Fuster, Juan, Gabaldon, Carolina, Antonov, Alexey, Gabizon, Ofir, Gabrielli, Alessandro, Gabrielli, Andrea, Gach, Grzegorz, Gadatsch, Stefan, Gadomski, Szymon, Gagliardi, Guido, Gagnon, Pauline, Galea, Cristina, Galhardo, Bruno, Antos, Jaroslav, Gallas, Elizabeth, Gallop, Bruce, Gallus, Petr, Galster, Gorm Aske Gram Krohn, Gan, KK, Gao, Jun, Gao, Yanyan, Gao, Yongsheng, Garay Walls, Francisca, García, Carmen, Anulli, Fabio, García Navarro, José Enrique, Garcia-Sciveres, Maurice, Gardner, Robert, Garelli, Nicoletta, Garonne, Vincent, Gascon Bravo, Alberto, Gatti, Claudio, Gaudiello, Andrea, Gaudio, Gabriella, Gaur, Bakul, Aoki, Masato, Gauthier, Lea, Gavrilenko, Igor, Gay, Colin, Gaycken, Goetz, Gazis, Evangelos, Gecse, Zoltan, Gee, Norman, Geich-Gimbel, Christoph, Geisler, Manuel Patrice, Gemme, Claudia, Aperio Bella, Ludovica, Genest, Marie-Hélène, Geng, Cong, Gentile, Simonetta, George, Simon, Gerbaudo, Davide, Gershon, Avi, Ghasemi, Sara, Ghazlane, Hamid, Giacobbe, Benedetto, Giagu, Stefano, Arabidze, Giorgi, Giannetti, Paola, Gibbard, Bruce, Gibson, Stephen, Gignac, Matthew, Gilchriese, Murdock, Gillam, Thomas, Gillberg, Dag, Gilles, Geoffrey, Gingrich, Douglas, Giokaris, Nikos, Arai, Yasuo, Giordani, MarioPaolo, Giorgi, Filippo Maria, Giorgi, Francesco Michelangelo, Giraud, Pierre-Francois, Giromini, Paolo, Giugni, Danilo, Giuliani, Claudia, Giulini, Maddalena, Gjelsten, Børge Kile, Gkaitatzis, Stamatios, Araque, Juan Pedro, Gkialas, Ioannis, Gkougkousis, Evangelos Leonidas, Gladilin, Leonid, Glasman, Claudia, Glatzer, Julian, Glaysher, Paul, Glazov, Alexandre, Goblirsch-Kolb, Maximilian, Godlewski, Jan, Goldfarb, Steven, Arce, Ayana, Golling, Tobias, Golubkov, Dmitry, Gomes, Agostinho, Gonçalo, Ricardo, Goncalves Pinto Firmino Da Costa, Joao, Gonella, Laura, González de la Hoz, Santiago, Gonzalez Parra, Garoe, Gonzalez-Sevilla, Sergio, Goossens, Luc, Abreu, Henso, Arduh, Francisco Anuar, Gorbounov, Petr Andreevich, Gordon, Howard, Gorelov, Igor, Gorini, Benedetto, Gorini, Edoardo, Gorišek, Andrej, Gornicki, Edward, Goshaw, Alfred, Gössling, Claus, Gostkin, Mikhail Ivanovitch, Arguin, Jean-Francois, Goudet, Christophe Raymond, Goujdami, Driss, Goussiou, Anna, Govender, Nicolin, Gozani, Eitan, Graber, Lars, Grabowska-Bold, Iwona, Gradin, Per Olov Joakim, Grafström, Per, Gramling, Johanna, Argyropoulos, Spyridon, Gramstad, Eirik, Grancagnolo, Sergio, Gratchev, Vadim, Gray, Heather, Graziani, Enrico, Greenwood, Zeno Dixon, Grefe, Christian, Gregersen, Kristian, Gregor, Ingrid-Maria, Grenier, Philippe, Arik, Metin, Grevtsov, Kirill, Griffiths, Justin, Grillo, Alexander, Grimm, Kathryn, Grinstein, Sebastian, Gris, Philippe Luc Yves, Grivaz, Jean-Francois, Groh, Sabrina, Grohs, Johannes Philipp, Gross, Eilam, Armbruster, Aaron James, Grosse-Knetter, Joern, Grossi, Giulio Cornelio, Grout, Zara Jane, Guan, Liang, Guenther, Jaroslav, Guescini, Francesco, Guest, Daniel, Gueta, Orel, Guido, Elisa, Guillemin, Thibault, Arnaez, Olivier, Guindon, Stefan, Gul, Umar, Gumpert, Christian, Guo, Jun, Guo, Yicheng, Gupta, Shaun, Gustavino, Giuliano, Gutierrez, Phillip, Gutierrez Ortiz, Nicolas Gilberto, Gutschow, Christian, Arnold, Hannah, Guyot, Claude, Gwenlan, Claire, Gwilliam, Carl, Haas, Andy, Haber, Carl, Hadavand, Haleh Khani, Haddad, Nacim, Hadef, Asma, Haefner, Petra, Hageböck, Stephan, Arratia, Miguel, Hajduk, Zbigniew, Hakobyan, Hrachya, Haleem, Mahsana, Haley, Joseph, Hall, David, Halladjian, Garabed, Hallewell, Gregory David, Hamacher, Klaus, Hamal, Petr, Hamano, Kenji, Arslan, Ozan, Hamilton, Andrew, Hamity, Guillermo Nicolas, Hamnett, Phillip George, Han, Liang, Hanagaki, Kazunori, Hanawa, Keita, Hance, Michael, Haney, Bijan, Hanke, Paul, Hanna, Remie, Artamonov, Andrei, Hansen, Jørgen Beck, Hansen, Jorn Dines, Hansen, Maike Christina, Hansen, Peter Henrik, Hara, Kazuhiko, Hard, Andrew, Harenberg, Torsten, Hariri, Faten, Harkusha, Siarhei, Harrington, Robert, and Tong, Baojia

Subjects
excited state [muon], 8000 GeV-cms, branching ratio: upper limit [channel cross section], (2muon 2jet) [final state], neutral current, mass [muon], contact interaction, hadronic decay [excited state], ATLAS, mass dependence, validity test [electroweak interaction], +muon+2jet%22">muon* --> muon 2jet, CERN LHC Coll, lower limit [mass], scattering [p p], ddc:530, associated production [jet], colliding beams [p p], +muon*+muon+anything%22">p p --> muon* muon anything, experimental results
Published
2019
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48. The use of Convolutional Neural Networks for signal-background classification in Particle Physics experiments.

Author

Doglioni, C., Kim, D., Stewart, G.A., Silvestris, L., Jackson, P., Kamleh, W., Ayyar, Venkitesh, Bhimji, Wahid, Gerhardt, Lisa, Robertson, Sally, and Ronaghi, Zahra

Subjects
CONVOLUTIONAL neural networks, PARTICLE physics, PARTICLE interactions, DEEP learning, CHERENKOV radiation
Abstract

The success of Convolutional Neural Networks (CNNs) in image classification has prompted efforts to study their use for classifying image data obtained in Particle Physics experiments. Here, we discuss our efforts to apply CNNs to 2D and 3D image data from particle physics experiments to classify signal from background. In this work we present an extensive convolutional neural architecture search, achieving high accuracy for signal/background discrimination for a HEP classification use-case based on simulated data from the Ice Cube neutrino observatory and an ATLAS-like detector. We demonstrate among other things that we can achieve the same accuracy as complex ResNet architectures with CNNs with less parameters, and present comparisons of computational requirements, training and inference times. [ABSTRACT FROM AUTHOR]

Published
2020
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49. Optimization of Software on High Performance Computing Platforms for the LUX-ZEPLIN Dark Matter Experiment.

Author

Doglioni, C., Kim, D., Stewart, G.A., Silvestris, L., Jackson, P., Kamleh, W., Ayyar, Venkitesh, Bhimji, Wahid, Monzani, Maria Elena, Naylor, Andrew, Patton, Simon, and Tull, Craig E.

Subjects
PARTICLE physics, HIGH performance computing, COMPUTER software, COMPUTER simulation, COMPUTER architecture
Abstract

High Energy Physics experiments like the LUX-ZEPLIN dark matter experiment face unique challenges when running their computation on High Performance Computing resources. In this paper, we describe some strategies to optimize memory usage of simulation codes with the help of profiling tools. We employed this approach and achieved memory reduction of 10-30%. While this has been performed in the context of the LZ experiment, it has wider applicability to other HEP experimental codes that face these challenges on modern computer architectures. [ABSTRACT FROM AUTHOR]

Published
2020
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