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Journal Article

2022

Gregory Wallace, Zhe Bai, Robbie Sadre, Talita Perciano, Nicola Bertelli, Syun'ichi Shiraiwa, Wes Bethel, John Wright, "Towards fast and accurate predictions of radio frequency power deposition and current profile via data-driven modelling: applications to lower hybrid current drive", Journal of Plasma Physics, August 18, 2022, 88:895880401, doi: 10.1017/S0022377822000708

Anne M. Felden, Daniel F. Martin, Esmond G. Ng, "SUHMO: an AMR SUbglacial Hydrology MOdel v1.0", Geosci. Model Dev. Discuss., July 27, 2022,

Akel Hashim, Rich Rines, Victory Omole, Ravi K. Naik, John Mark Kreikebaum, David I. Santiago, Frederic T. Chong, Irfan Siddiqi, Pranav Gokhale, "Optimized SWAP networks with equivalent circuit averaging for QAOA", Phys. Rev. Research, 2022, 033028, doi: 10.1103/PhysRevResearch.4.033028

L. Jin, A. Lazar, C. Brown, V. Garikapati, B. Sun, S. Ravulaparthy, Q. Chen, A. Sim, K. Wu, T. Wenzel, T. Ho, C. A. Spurlock, "What Makes You Hold onto That Old Car? Joint Insights from Machine Learning and Multinomial Logit on Vehicle-level Transaction Decisions", Frontiers in Future Transportation, Connected Mobility and Automation, 2022, 3:894654, doi: 10.3389/ffutr.2022.894654

S. Kim, A. Sim, K. Wu, S. Byna, Y. Son, "Design and Implementation of Dynamic I/O Control Scheme for Large Scale Distributed File Systems", Cluster Computing, 2022, doi: 10.1007/s10586-022-03640-0

B. Dong, A. Popescu, V. Rodriguez Tribaldos, S. Byna, J. Ajo-Franklin and K. Wu, "Real-time and Post-hoc Compression for Data from Distributed Acoustic Sensing", Computers and Geosciences., June 24, 2022,

Jonathan Ajo‐Franklin, Verónica Rodríguez Tribaldos, Avinash Nayak, Feng Cheng, Robert Mellors, Benxin Chi, Todd Wood, Michelle Robertson, Cody Rotermund, Eric Matzel, Dennise C. Templeton, Christina Morency, Kesheng Wu, Bin Dong, Patrick Dobson;, "The Imperial Valley Dark Fiber Project: Toward Seismic Studies Using DAS and Telecom Infrastructure for Geothermal Applications", Seismological Research Letters, June 24, 2022,

Srivatsan Chakram, Kevin He, Akash V. Dixit, Andrew E. Oriani, Ravi K. Naik, Nelson Leung, Hyeokshin Kwon, Wen-Long Ma, Liang Jiang, David I. Schuster, "Multimode photon blockade", Nature Physics, 2022, doi: 10.1038/s41567-022-01630-y

Qiang Du, Dan Wang, Tong Zhou, Antonio Gilardi, Mariam Kiran, Bashir Mohammed, Derun Li, and Russell Wilcox, "Experimental beam combining stabilization using machine learning trained while phases drift", Advanced Solid State Lasers 2022, © 2022 Optica Publishing Group, June 1, 2022, Vol. 30,:pp. 12639-, doi: https://doi.org/10.1364/OE.450255

M. G. Amankwah, D. Camps, E. W. Bethel, R. Van Beeumen, T. Perciano, "Quantum pixel representations and compression for N-dimensional images", Nature Scientific Reports, May 11, 2022, 12:7712, doi: 10.1038/s41598-022-11024-y

Yosep Kim, Alexis Morvan, Long B Nguyen, Ravi K Naik, Christian J\ unger, Larry Chen, John Mark Kreikebaum, David I Santiago, Irfan Siddiqi, "High-fidelity three-qubit iToffoli gate for fixed-frequency superconducting qubits", Nature Physics, 2022, 1--6, doi: 10.1038/s41567-022-01590-3

B. Weinger, J. Kim, A. Sim, M. Nakashima, N. Moustafa, K. Wu, "Enhancing IoT Anomaly Detection Performance for Federated Learning", Digital Communications and Networks, Special Issue on Edge Computation and Intelligence, 2022, doi: 10.1016/j.dcan.2022.02.007

Maximilian Bremer, John Bachan, Cy Chan, Clint Dawson, "Adaptive total variation stable local timestepping for conservation laws", Journal of Computational Physics, April 21, 2022,

S. Zhang, R. Sadre, B. A. Legg, H. Pyles, T. Perciano, E. W. Bethel, D. Baker, O. Rübel, J. J. D. Yoreo, "Rotational dynamics and transition mechanisms of surface-adsorbed proteins", Proceedings of the National Academy of Sciences, April 11, 2022, 119:e202024211, doi: 10.1073/pnas.2020242119

Lipeng Wan, Axel Huebl, Junmin Gu, Franz Poeschel, Ana Gainaru, Ruonan Wang, Jieyang Chen, Xin Liang, Dmitry Ganyushin, Todd Munson, Ian Foster, Jean-Luc Vay, Norbert Podhorszki, Kesheng Wu, Scott Klasky, "Improving I/O Performance for Exascale Applications Through Online Data Layout Reorganization", IEEE Transactions on Parallel and Distributed Systems, 2022, 33:878-890, doi: 10.1109/TPDS.2021.3100784

Houjun Tang, Quincey Koziol, John Ravi, and Suren Byna,, "Transparent Asynchronous Parallel I/O using Background Threads", IEEE Transactions on Parallel and Distributed Systems, April 4, 2022, 33, doi: 10.1109/TPDS.2021.3090322

W. D. Fullmer, R. Porcu, J. Musser, A. S. Almgren, I. Srivastava, "The Divergence of Nearby Trajectories in Soft-Sphere DEM", Particuology, April 1, 2022, 63:1 - 8, doi: 10.1016/j.partic.2021.06.008

M. Avaylon, R. Sadre, Z. Bai, T. Perciano, "Adaptable Deep Learning and Probabilistic Graphical Model System for Semantic Segmentation", Advances in Artificial Intelligence and Machine Learnin, March 31, 2022, 2:288--302, doi: 10.54364/AAIML.2022.1119

Adrián P. Diéguez, Margarita Amor, Ramón Doallo, Akira Nukada, Satoshi Matsuoka, "Efficient high-precision integer multiplication on the GPU", The International Journal of High Performance Computing Applications, March 2022, 36:356-369, doi: 10.1177/10943420221077964

Samuel B. Kachuck, Morgan Whitcomb, Jeremy N. Bassis, Daniel F. Martin, Stephen F. Price, "Simulating ice-shelf extent using damage mechanics", Journal of Glaciology, March 7, 2022, 1-12, doi: 10.1017/jog.2022.12

George Michelogiannakis, Benjamin Klenk, Brandon Cook, Min Yee Teh, Madeleine Glick, Larry Dennison, Keren Bergman, John Shalf, "A Case For Intra-Rack Resource Disaggregation in HPC", ACM Transactions on Architecture and Code Optimization, February 2022,

Hannah Klion, Alexander Tchekhovskoy, Daniel Kasen, Adithan Kathirgamaraju, Eliot Quataert, Rodrigo Fernandez, "The impact of r-process heating on the dynamics of neutron star merger accretion disc winds and their electromagnetic radiation", Monthly Notices of the RAS, 2022, 510:2968-2979, doi: 10.1093/mnras/stab3583

A. P. Santos, I. Srivastava, L. E. Silbert, J. B. Lechman, G. S. Grest, "Fluctuations and power-law scaling of dry, frictionless granular rheology near the hard-particle limit", arXiv:2201.03680, January 10, 2022,

Z. Yao, R. Jambunathan, Y. Zeng, and A. Nonaka, "A Massively Parallel Time-Domain Coupled Electrodynamics-Micromagnetics Solver", International Journal of High Performance Computing Applications, January 10, 2022, accepted,

Sugeerth Murugesan, Mariam Kiran, Bernd Hamann, Gunther H. Weber, "Netostat: Analyzing Dynamic Flow Patterns in High-Speed Networks", Cluster Computing, 2022, doi: 10.1007/s10586-022-03543-0

P Diego-Palazuelos, JR Eskilt, Y Minami, M Tristram, RM Sullivan, AJ Banday, RB Barreiro, HK Eriksen, KM Górski, R Keskitalo, E Komatsu, E Martínez-González, D Scott, P Vielva, IK Wehus, "Cosmic Birefringence from the Planck Data Release 4", Physical review letters, 2022, 128:091302, doi: 10.1103/physrevlett.128.091302

C Varadharajan, AP Appling, B Arora, DS Christianson, VC Hendrix, V Kumar, AR Lima, J Müller, S Oliver, M Ombadi, T Perciano, JM Sadler, H Weierbach, JD Willard, Z Xu, J Zwart, "Can machine learning accelerate process understanding and decision-relevant predictions of river water quality?", Hydrological Processes, January 1, 2022, 36, doi: 10.1002/hyp.14565

Hengjie Wang, Robert Planas, Aparna Chandramowlishwaran, Ramin Bostanabad, "Mosaic flows: A transferable deep learning framework for solving PDEs on unseen domains", Computer Methods in Applied Mechanics and Engineering, 2022, 389:114424,

2021

Zhe Bai, Liqian Peng, "Non-intrusive nonlinear model reduction via machine learning approximations to low-dimensional operators", Advanced Modeling and Simulation in Engineering Sciences, 2021, 8:28, doi: 10.1186/s40323-021-00213-5

Melanie E. Moses, Steven Hofmeyr, Judy L Cannon, Akil Andrews, Rebekah Gridley, Monica Hinga, Kirtus Leyba, Abigail Pribisova, Vanessa Surjadidjaja, Humayra Tasnim, Stephanie Forrest, "Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection", PLOS Computational Biology, December 2021, 17(12), doi: 10.1371/journal.pcbi.1009735

J. T. Clemmer, I. Srivastava, G. S. Grest, J. B. Lechman, "Shear is Not Always Simple: Rate-Dependent Effects of Loading Geometry on Granular Rheology", Physical Review Letters, December 22, 2021, 127:268003, doi: 10.1103/PhysRevLett.127.268003

Qiao Kang, Scot Breitenfeld, Kaiyuan Hou, Wei-keng Liao, Robert Ross, and Suren Byna,, "Optimizing Performance of Parallel I/O Accesses to Non-contiguous Blocks in Multiple Array Variables", IEEE BigData 2021 conference, December 19, 2021,

I. Srivastava, L. E. Silbert, J. B. Lechman, G. S. Grest, "Flow and Arrest in Stressed Granular Materials", Soft Matter, December 17, 2021, doi: 10.1039/D1SM01344K

Shen Sheng, Mariam Kiran, Bashir Mohammed, "DynamicDeepFlow: An Approach for Identifying Changes in Network Traffic Flow Using Unsupervised Clustering", (BEST PAPER) 4th International Conference on Machine Learning for Networking (MLN'2021), December 6, 2021,

Andrew Myers, Ann Almgren, Diana Almorim, John Bell, Luca Fedeli, Lixin Ge, Kevin Gott, David Grote, Mark Hogan, Axel Huebl, Revathi Jambunathan, Remi Lehe, Cho Ng, Michael Rowan, Olga Shapoval, Maxence Thevenet, Jean-Luc Vay, Henri Vincenti, Eloise Yang, Neil Zaim, Weiqun Zhang, Yin Zhao, Edoardo Zoni, "Porting WarpX to GPU-accelerated platforms", Parallel Computing, December 1, 2021,

Akel Hashim, Ravi K. Naik, Alexis Morvan, Jean-Loup Ville, Bradley Mitchell, John Mark Kreikebaum, Marc Davis, Ethan Smith, Costin Iancu, Kevin P. O Brien, Ian Hincks, Joel J. Wallman, Joseph Emerson, Irfan Siddiqi, "Randomized Compiling for Scalable Quantum Computing on a Noisy Superconducting Quantum Processor", Physical Review X, 2021, 11:041039, doi: 10.1103/PhysRevX.11.041039

Kenneth Rudinger, Craig W Hogle, Ravi K Naik, Akel Hashim, Daniel Lobser, David I Santiago, Matthew D Grace, Erik Nielsen, Timothy Proctor, Stefan Seritan, others, "Experimental Characterization of Crosstalk Errors with Simultaneous Gate Set Tomography", PRX Quantum, 2021, 2:040338, doi: 10.1103/PRXQuantum.2.040338

Bashir Mohammed, Mariam Kiran, Bjoern Enders, "NetGraf: An End-to-End Learning Network Monitoring Service", 2021 IEEE Workshop on Innovating the Network for Data-Intensive Science (INDIS), November 15, 2021, doi: 10.1109/INDIS54524.2021.00007

Bradley K. Mitchell, Ravi K. Naik, Alexis Morvan, Akel Hashim, John Mark Kreikebaum, Brian Marinelli, Wim Lavrijsen, Kasra Nowrouzi, David I. Santiago, Irfan Siddiqi, "Hardware-Efficient Microwave-Activated Tunable Coupling between Superconducting Qubits", Physical Review Letters, 2021, 127:200502, doi: 10.1103/PhysRevLett.127.200502

S. B. Sayed, Y. Liu, L. J. Gomez, A. C. Yucel, "A butterfly-accelerated volume integral equation solver for broad permittivity and large-scale electromagnetic analysis", arxiv-preprint, November 5, 2021,

B Mohammed, M Kiran; N Krishnaswamy; Keshang, Wu, "Predicting WAN Traffic Volumes using Fourier and Multivariate SARIMA Approach", International Journal of Big Data Intelligence, November 3, 2021,

A. Syal, A. Lazar, J. Kim, A. Sim, K. Wu, "Network traffic performance analysis from passive measurements using gradient boosting machine learning", International Journal of Big Data Intelligence, 2021, 8:13-30, doi: 10.1504/IJBDI.2021.118741

Pietro Benedusi, Michael L Minion, Rolf Krause, "An experimental comparison of a space-time multigrid method with PFASST for a reaction-diffusion problem", Computers & Mathematics with Applications, October 1, 2021,

Yilun Xu, Gang Huang, Jan Balewski, Ravi Naik, Alexis Morvan, Bradley Mitchell, Kasra Nowrouzi, David I. Santiago, Irfan Siddiqi, "QubiC: An Open-Source FPGA-Based Control and Measurement System for Superconducting Quantum Information Processors", IEEE Transactions on Quantum Engineering, 2021, 2:1-11, doi: 10.1109/TQE.2021.3116540

H. Luo, J.W. Demmel, Y. Cho, X. S. Li, Y. Liu, "Non-smooth Bayesian optimization in tuning problems", arxiv-preprint, September 21, 2021,

Marco Siracusa, Emanuele Del Sozzo, Marco Rabozzi, Lorenzo Di Tucci, Samuel Williams, Donatella Sciuto, Marco Domenico Santambrogio, "A Comprehensive Methodology to Optimize FPGA Designs via the Roofline Model", Transactions on Computers (TC), September 2021, doi: 10.1109/TC.2021.3111761

Srivatsan Chakram, Andrew E. Oriani, Ravi K. Naik, Akash V. Dixit, Kevin He, Ankur Agrawal, Hyeokshin Kwon, David I. Schuster, "Seamless High-Q Microwave Cavities for Multimode Circuit Quantum Electrodynamics", Physical Review Letters, 2021, 127:107701, doi: 10.1103/PhysRevLett.127.107701

G Koolstra, N Stevenson, S Barzili, L Burns, K Siva, S Greenfield, W Livingston, A Hashim, RK Naik, JM Kreikebaum, KP O'Brien, DI Santiago, J Dressel, I Siddiqi, "Monitoring fast superconducting qubit dynamics using a neural network", Preprint, August 2021,

Tan Nguyen, Colin MacLean, Marco Siracusa, Douglas Doerfler, Nicholas J. Wright, Samuel Williams, "FPGA‐based HPC accelerators: An evaluation on performance and energy efficiency", CCPE, August 22, 2021, doi: 10.1002/cpe.6570

I. Srivastava, S. A. Roberts, J. T. Clemmer, L. E. Silbert, J. B. Lechman, G. S. Grest, "Jamming of Bidisperse Frictional Spheres", Physical Review Research, August 13, 2021, 3:L032042, doi: 10.1103/PhysRevResearch.3.L032042

Nan Ding, Muaaz Awan, Samuel Williams, "Instruction Roofline: An insightful visual performance model for GPUs", CCPE, August 4, 2021, doi: 10.1002/cpe.6591

Charlene Yang, Yunsong Wang, Thorsten Kurth, Steven Farrell, Samuel Williams, "Hierarchical Roofline Performance Analysis for Deep Learning Applications", Intelligent Computing, LNNS, July 15, 2021, doi: 10.1007/978-3-030-80126-7

Jean Sexton, Zarija Lukic, Ann Almgren, Chris Daley, Brian Friesen, Andrew Myers, and Weiqun Zhang, "Nyx: A Massively Parallel AMR Code for Computational Cosmology", The Journal Of Open Source Software, July 10, 2021,

M. Nakashima, A. Sim, Y. Kim, J. Kim, J. Kim, "Automated Feature Selection for Anomaly Detection in Network Traffic Data", ACM Transactions on Management Information Systems (TMIS), 2021, 12:1-28, doi: 10.1145/3446636

Thomas M Evans, Andrew Siegel, Erik W Draeger,Jack Deslippe, Marianne M Francois, Timothy C Germann,William E Hart, Daniel F Martin, "A survey of software implementations used by application codes in the Exascale Computing Project", The International Journal of High Performance Computing Applications, June 25, 2021, doi: https://doi.org/10.1177/10943420211028940

Élie Genois, Jonathan A. Gross, Agustin Di Paolo, Noah J. Stevenson, Gerwin Koolstra, Akel Hashim, Irfan Siddiqi, Alexandre Blais, "Quantum-tailored machine-learning characterization of a superconducting qubit", Preprint, June 24, 2021,

Robin J Dolleman, Debadi Chakraborty, Daniel R Ladiges, Herre SJ van der Zant, John E Sader, Peter G Steeneken, "Squeeze-film effect on atomically thin resonators in the high-pressure limit", Submitted to Nano Letters, June 24, 2021,

Yang Liu, Pieter Ghysels, Lisa Claus, Xiaoye Sherry Li, "Sparse Approximate Multifrontal Factorization with Butterfly Compression for High Frequency Wave Equations", SIAM J. Sci. Comput., June 22, 2021,

Devarshi Ghoshal, Ludovico Bianchi, Abdelilah Essiari, Michael Beach, Drew Paine, Lavanya Ramakrishnan, "Science Capsule - Capturing the Data Life Cycle", Journal of Open Source Software, 2021, 6:2484, doi: 10.21105/joss.02484

Weiqun Zhang, Andrew Myers, Kevin Gott, Ann Almgren and John Bell, "AMReX: Block-Structured Adaptive Mesh Refinement for Multiphysics Applications", The International Journal of High Performance Computing Applications, June 12, 2021,

David McCallen, Houjun Tang, Suiwen Wu, Eric Eckert, Junfei Huang, N Anders Petersson, "Coupling of regional geophysics and local soil-structure models in the EQSIM fault-to-structure earthquake simulation framework", The International Journal of High Performance Computing Applications, May 25, 2021, doi: 10.1177/10943420211019118

L. Fedeli, A. Sainte-Marie, N. Zaim, M. Thevenet, J. L. Vay, A. Myers, F. Quere, and H. Vincenti, "Probing strong-field QED with Doppler-boosted petawatt-class lasers", Physical Review Letters, May 10, 2021,

Tamsin L. Edwards, Sophie Nowicki, Ben Marzeion, Regine Hock, Heiko Goelzer, Hélène Seroussi, Nicolas C. Jourdain, Donald A. Slater, Fiona E. Turner, Christopher J. Smith, Christine M. McKenna, Erika Simon, Ayako Abe-Ouchi, Jonathan M. Gregory, Eric Larour, William H. Lipscomb, Antony J. Payne, Andrew Shepherd, Cécile Agosta, Patrick Alexander, Torsten Albrecht, Brian Anderson, Xylar Asay-Davis, Andy Aschwanden, Alice Barthel, Andrew Bliss, Reinhard Calov, Christopher Chambers, Nicolas Champollion, Youngmin Choi, Richard Cullather, Joshua Cuzzone, Christophe Dumas, Denis Felikson, Xavier Fettweis, Koji Fujita, Benjamin K. Galton-Fenzi, Rupert Gladstone, Nicholas R. Golledge, Ralf Greve, Tore Hattermann, Matthew J. Hoffman, Angelika Humbert, Matthias Huss, Philippe Huybrechts, Walter Immerzeel, Thomas Kleiner, Philip Kraaijenbrink, Sébastien Le clec’h, Victoria Lee, Gunter R. Leguy, Christopher M. Little, Daniel P. Lowry, Jan-Hendrik Malles, Daniel F. Martin, Fabien Maussion, Mathieu Morlighem, James F. O’Neill, Isabel Nias, Frank Pattyn, Tyler Pelle, Stephen F. Price, Aurélien Quiquet, Valentina Radić, Ronja Reese, David R. Rounce, Martin Rückamp, Akiko Sakai, Courtney Shafer, Nicole-Jeanne Schlegel, Sarah Shannon, Robin S. Smith, Fiammetta Straneo, Sainan Sun, Lev Tarasov, Luke D. Trusel, Jonas Van Breedam, Roderik van de Wal, Michiel van den Broeke, Ricarda Winkelmann, Harry Zekollari, Chen Zhao, Tong Zhang, Thomas Zwinger, "Projected land ice contributions to twenty-first-century sea level rise", Nature, May 5, 2021, 593:74-82, doi: 10.1038/s41586-021-03302-y

David McCallen, Anders Petersson, Arthur Rodgers, Arben Pitarka, Mamun Miah, Floriana Petrone, Bjorn Sjogreen, Norman Abrahamson, Houjun Tang, "EQSIM—A multidisciplinary framework for fault-to-structure earthquake simulations on exascale computers part I: Computational models and workflow", Earthquake Spectra, May 1, 2021, 37:707-735, doi: 10.1177/8755293020970982

D. A. Agarwal, J. Damerow, C. Varadharajan, D. S. Christianson, G. Z. Pastorello, Y.-W. Cheah, L. Ramakrishnan, "Balancing the needs of consumers and producers for scientific data collections", Ecological Informatics, 2021, 62:101251, doi: 10.1016/j.ecoinf.2021.101251

Sean Peisert, "Trustworthy Scientific Computing", Communications of the ACM (CACM), May 2021, doi: 10.1145/3457191

T. Groves, N. Ravichandrasekaran, B. Cook, N. Keen, D. Trebotich, N. Wright, B. Alverson, D. Roweth, K. Underwood, "Not All Applications Have Boring Communication Patterns: Profiling Message Matching with BMM", Concurrency and Computation: Practice and Experience, April 26, 2021, doi: 0.1002/cpe.6380

J. Galen Wang, Roseanna N. Zia, "Vitrification is a spontaneous non-equilibrium transition driven by osmotic pressure", Journal of Physics: Condensed Matter, April 23, 2021, doi: 10.1088/1361-648x/abeec0

Sherwood Richers, Don E. Willcox, Nicole M. Ford, and Andrew Myers, "Particle-in-cell simulation of the neutrino fast flavor instabilit", Physical Review D, April 20, 2021,

Jordan Musser, Ann S Almgren, William D Fullmer, Oscar Antepara, John B Bell, Johannes Blaschke, Kevin Gott, Andrew Myers, Roberto Porcu, Deepak Rangarajan, Michele Rosso, Weiqun Zhang, and Madhava Syamlal, "MFIX:Exa: A Path Towards Exascale CFD-DEM Simulations", The International Journal of High Performance Computing Applications, April 16, 2021,

Marco Pritoni, Drew Paine, Gabriel Fierro, Cory Mosiman, Michael Poplawski, Joel Bender, Jessica Granderson, "Metadata Schemas and Ontologies for Building Energy Applications: A Critical Review and Use Case Analysis", Energies, April 6, 2021, doi: 10.3390/en14072024

Digital and intelligent buildings are critical to realizing efficient building energy operations and a smart grid. With the increasing digitalization of processes throughout the life cycle of buildings, data exchanged between stakeholders and between building systems have grown significantly. However, a lack of semantic interoperability between data in different systems is still prevalent and hinders the development of energy-oriented applications that can be reused across buildings, limiting the scalability of innovative solutions. Addressing this challenge, our review paper systematically reviews metadata schemas and ontologies that are at the foundation of semantic interoperability necessary to move toward improved building energy operations. The review finds 40 schemas that span different phases of the building life cycle, most of which cover commercial building operations and, in particular, control and monitoring systems. The paper’s deeper review and analysis of five popular schemas identify several gaps in their ability to fully facilitate the work of a building modeler attempting to support three use cases: energy audits, automated fault detection and diagnosis, and optimal control. Our findings demonstrate that building modelers focused on energy use cases will find it difficult, labor intensive, and costly to create, sustain, and use semantic models with existing ontologies. This underscores the significant work still to be done to enable interoperable, usable, and maintainable building models. We make three recommendations for future work by the building modeling and energy communities: a centralized repository with a search engine for relevant schemas, the development of more use cases, and better harmonization and standardization of schemas in collaboration with industry to facilitate their adoption by stakeholders addressing varied energy-focused use cases.

Fabio Massacci, Trent Jaeger, Sean Peisert, "SolarWinds and the Challenges of Patching: Can We Ever Stop Dancing With the Devil?", IEEE Security & Privacy, April 2021, 14-19, doi: 10.1109/MSEC.2021.3050433

Sean Peisert, Bruce Schneier, Hamed Okhravi, Fabio Massacci, Terry Benzel, Carl Landwehr, Mohammad Mannan, Jelena Mirkovic, Atul Prakash, James Bret Michael, "Perspectives on the SolarWinds Incident", IEEE Security & Privacy, April 2021, 7-13, doi: 10.1109/MSEC.2021.3051235

Daniel R. Ladiges, Sean P. Carney, Andrew Nonaka, Katherine Klymko, Guy C. Moore, Alejandro L. Garcia, Sachin R. Natesh, Aleksandar Donev, John B. Bell, "A Discrete Ion Stochastic Continuum Overdamped Solvent Algorithm for Modeling Electrolytes", Physical Review Fluids, April 1, 2021, 6(4):044309,

Karol Kowalski, Raymond Bair, Nicholas P. Bauman, Jeffery S. Boschen, Eric J. Bylaska, Jeff Daily, Wibe A. de Jong, Thom Dunning, Niranjan Govind, Robert J. Harrison, Murat Keceli, Kristopher Keipert, Sriram Krishnamoorthy, Suraj Kumar, Erdal Mutlu, Bruce Palmer, Ajay Panyala, Bo Peng, Ryan M. Richard, T. P. Straatsma, Peter Sushko, Edward F. Valeev, Marat Valiev, Hubertus J. J. van Dam, Jonathan M. Waldrop, David B. Williams-Young, Chao Yang, Marcin Zalewski, Theresa L. Windus, "From NWChem to NWChemEx: Evolving with the Computational Chemistry Landscape", Chemical Reviews, March 31, 2021, doi: 10.1021/acs.chemrev.0c00998

Georgios Tzimpragos, Jennifer Volk, Dilip Vasudevan, Nestan Tsiskaridze, George Michelogiannakis, Advait Madhavan, John Shalf, Timothy Sherwood, "Temporal Computing With Superconductors", IEEE MIcro, March 2021, 41:71-79, doi: 10.1109/MM.2021.3066377

Yang Liu, Xin Xing, Han Guo, Eric Michielssen, Pieter Ghysels, Xiaoye Sherry Li, "Butterfly factorization via randomized matrix-vector multiplications", SIAM J. Sci. Comput., March 9, 2021,

Thijs Steel, Daan Camps, Karl Meerbergen, Raf Vandebril, "A Multishift, Multipole Rational QZ Method with Aggressive Early Deflation", SIAM Journal on Matrix Analysis and Applications, February 19, 2021, 42:753-774, doi: 10.1137/19M1249631

In the article “A Rational QZ Method” by D. Camps, K. Meerbergen, and R. Vandebril [SIAM J. Matrix Anal. Appl., 40 (2019), pp. 943--972], we introduced rational QZ (RQZ) methods. Our theoretical examinations revealed that the convergence of the RQZ method is governed by rational subspace iteration, thereby generalizing the classical QZ method, whose convergence relies on polynomial subspace iteration. Moreover the RQZ method operates on a pencil more general than Hessenberg---upper triangular, namely, a Hessenberg pencil, which is a pencil consisting of two Hessenberg matrices. However, the RQZ method can only be made competitive to advanced QZ implementations by using crucial add-ons such as small bulge multishift sweeps, aggressive early deflation, and optimal packing. In this paper we develop these techniques for the RQZ method. In the numerical experiments we compare the results with state-of-the-art routines for the generalized eigenvalue problem and show that the presented method is competitive in terms of speed and accuracy.

J-L Vay, Ann Almgren, LD Amorim, John Bell, L Fedeli, L Ge, K Gott, DP Grote, M Hogan, A Huebl, R Jambunathan, R Lehe, A Myers, C Ng, M Rowan, O Shapoval, M Thevenet, H Vincenti, E Yang, N Zaim, W Zhang, Y Zhao and E Zoni, "Modeling of a chain of three plasma accelerator stages with the WarpX electromagnetic PIC code on GPUs", Physics of Plasmas, February 9, 2021,

Donghun Koo, Jaehwan Lee, Jialin Liu, Eun-Kyu Byun, Jae-Hyuck Kwak, Glenn K Lockwood, Soonwook Hwang, Katie Antypas, Kesheng Wu, Hyeonsang Eom, "An empirical study of I/O separation for burst buffers in HPC systems", Journal of Parallel and Distributed Computing, 2021, 148:96-108, doi: 10.1016/j.jpdc.2020.10.007

M Tristram, AJ Banday, KM Górski, R Keskitalo, CR Lawrence, KJ Andersen, RB Barreiro, J Borrill, HK Eriksen, R Fernandez-Cobos, TS Kisner, E Martínez-González, B Partridge, D Scott, TL Svalheim, H Thommesen, IK Wehus, "Planck constraints on the tensor-to-scalar ratio", Astronomy and Astrophysics, 2021, 647, doi: 10.1051/0004-6361/202039585

N Aghanim, Y Akrami, M Ashdown, J Aumont, C Baccigalupi, M Ballardini, AJ Banday, RB Barreiro, N Bartolo, S Basak, R Battye, K Benabed, JP Bernard, M Bersanelli, P Bielewicz, JJ Bock, JR Bond, J Borrill, FR Bouchet, F Boulanger, M Bucher, C Burigana, RC Butler, E Calabrese, JF Cardoso, J Carron, A Challinor, HC Chiang, J Chluba, LPL Colombo, C Combet, D Contreras, BP Crill, F Cuttaia, P De Bernardis, G De Zotti, J Delabrouille, JM Delouis, E DI Valentino, JM DIego, O Doré, M Douspis, A Ducout, X Dupac, S Dusini, G Efstathiou, F Elsner, TA Enßlin, HK Eriksen, Y Fantaye, M Farhang, J Fergusson, R Fernandez-Cobos, F Finelli, F Forastieri, M Frailis, AA Fraisse, E Franceschi, A Frolov, S Galeotta, S Galli, K Ganga, RT Génova-Santos, M Gerbino, T Ghosh, J González-Nuevo, KM Górski, S Gratton, A Gruppuso, JE Gudmundsson, J Hamann, W Handley, FK Hansen, D Herranz, SR Hildebrandt, E Hivon, Z Huang, AH Jaffe, WC Jones, A Karakci, E Keihänen, R Keskitalo, K Kiiveri, J Kim, TS Kisner, L Knox, N Krachmalnicoff, M Kunz, H Kurki-Suonio, G Lagache, JM Lamarre, A Lasenby, M Lattanzi, CR Lawrence, M Le Jeune, P Lemos, J Lesgourgues, F Levrier, A Lewis, M Liguori, "Erratum: Planck 2018 results: VI. Cosmological parameters (Astronomy and Astrophysics (2020) 641 (A6) DOI: 10.1051/0004-6361/201833910)", Astronomy and Astrophysics, 2021, 652, doi: 10.1051/0004-6361/201833910e

Hannah Klion, Paul C. Duffell, Daniel Kasen, Eliot Quataert, "The effect of jet-ejecta interaction on the viewing angle dependence of kilonova light curves", Monthly Notices of the RAS, 2021, 502:865-875, doi: 10.1093/mnras/stab042

Hamish A. Carr, Gunther H. Weber, Christopher M. Sewell, Oliver R\ ubel, Patricia Fasel, James P. Ahrens, "Scalable Contour Tree Computation by Data Parallel Peak Pruning", Transactions on Visualization and Computer Graphics, 2021, 27:2437--2454, doi: 10.1109/TVCG.2019.2948616

Hamish Carr, Oliver Rübel, Gunther H. Weber, James Ahrens, "Optimization and Augmentation for Data Parallel Contour Trees", IEEE Transactions on Visualization and Computer Graphics, 2021, doi: 10.1109/TVCG.2021.3064385

Robbie Sadre, Colin Ophus, Anstasiia Butko, Gunther H Weber, "Deep Learning Segmentation of Complex Features in Atomic-Resolution Phase Contrast Transmission Electron Microscopy Images", Microscopy and Microanalysis, 2021, doi: 10.1017/S1431927621000167

Akash V Dixit, Srivatsan Chakram, Kevin He, Ankur Agrawal, Ravi K Naik, David I Schuster, Aaron Chou, "Searching for dark matter with a superconducting qubit", Physical Review Letters, 2021, 126:141302, doi: 10.1103/PhysRevLett.126.141302

Alexis Morvan, VV Ramasesh, MS Blok, JM Kreikebaum, K O’Brien, L Chen, BK Mitchell, RK Naik, DI Santiago, I Siddiqi, "Qutrit randomized benchmarking", Physical Review Letters, 2021, 126:210504, doi: 10.1103/PhysRevLett.126.210504

Nazanin Jafari, Oguz Selvitopi, Cevdet Aykanat, "Fast shared-memory streaming multilevel graph partitioning", Journal of Parallel and Distributed Computing, January 2021, 147:140-151, doi: https://doi.org/10.1016/j.jpdc.2020.09.004

Conference Paper

2022

Paul H. Hargrove, Dan Bonachea, "GASNet-EX RMA Communication Performance on Recent Supercomputing Systems", 5th Annual Parallel Applications Workshop, Alternatives To MPI+X (PAW-ATM'22), November 2022, doi: 10.25344/S40C7D

Partitioned Global Address Space (PGAS) programming models, typified by systems such as Unified Parallel C (UPC) and Fortran coarrays, expose one-sided Remote Memory Access (RMA) communication as a key building block for High Performance Computing (HPC) applications. Architectural trends in supercomputing make such programming models increasingly attractive, and newer, more sophisticated models such as UPC++, Legion and Chapel that rely upon similar communication paradigms are gaining popularity.

GASNet-EX is a portable, open-source, high-performance communication library designed to efficiently support the networking requirements of PGAS runtime systems and other alternative models in emerging exascale machines. The library is an evolution of the popular GASNet communication system, building upon 20 years of lessons learned. We present microbenchmark results which demonstrate the RMA performance of GASNet-EX is competitive with MPI implementations on four recent, high-impact, production HPC systems. These results are an update relative to previously published results on older systems. The networks measured here are representative of hardware currently used in six of the top ten fastest supercomputers in the world, and all of the exascale systems on the U.S. DOE road map.

Damian Rouson, Dan Bonachea, "Caffeine: CoArray Fortran Framework of Efficient Interfaces to Network Environments", Proceedings of the Eighth Annual Workshop on the LLVM Compiler Infrastructure in HPC (LLVM-HPC2022), Dallas, Texas, USA, IEEE, November 2022, doi: 10.25344/S4459B

This paper provides an introduction to the CoArray Fortran Framework of Efficient Interfaces to Network Environments (Caffeine), a parallel runtime library built atop the GASNet-EX exascale networking library. Caffeine leverages several non-parallel Fortran features to write type- and rank-agnostic interfaces and corresponding procedure definitions that support parallel Fortran 2018 features, including communication, collective operations, and related services. One major goal is to develop a runtime library that can eventually be considered for adoption by LLVM Flang, enabling that compiler to support the parallel features of Fortran. The paper describes the motivations behind Caffeine's design and implementation decisions, details the current state of Caffeine's development, and previews future work. We explain how the design and implementation offer benefits related to software sustainability by lowering the barrier to user contributions, reducing complexity through the use of Fortran 2018 C-interoperability features, and high performance through the use of a lightweight communication substrate.

Ayaz Akram, Venkatesh Akella, Sean Peisert, Jason Lowe-Power, "SoK: Limitations of Confidential Computing via TEEs for High-Performance Compute Systems", Proceedings of the 2022 IEEE International Symposium on Secure and Private Execution Environment Design (SEED), September 2022,

Yize Chen, Yuanyuan Shi, Daniel Arnold, Sean Peisert, "SAVER: Safe Learning-Based Controller for Real-Time Voltage Regulation", Proceedings of the 2022 IEEE Power Engineering Society (PES) General Meeting, Denver, CO, July 2022,

R. Han, A. Sim, K. Wu, I. Monga, C. Guok, F. Würthwein, D. Davila, J. Balcas, H. Newman, "Access Trends of In-network Cache for Scientific Data", 5th ACM International Workshop on ​System and Network Telemetry and Analysis (SNTA), in conjunction with The 31st ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC), 2022, doi: 10.1145/3526064.3534110

J. Bellavita, A. Sim, K. Wu, I. Monga, C. Guok, F. Würthwein, D. Davila, "Studying Scientific Data Lifecycle in On-demand Distributed Storage Caches", 5th ACM International Workshop on ​System and Network Telemetry and Analysis (SNTA) 2022, in conjunction with The 31st ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC), 2022, doi: 10.1145/3526064.3534111

R. Shao, J. Kim A. Sim, K. Wu, "Predicting Slow Connections in Scientific Computing", 5th ACM International Workshop on ​System and Network Telemetry and Analysis (SNTA) 2022, in conjunction with The 31st ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC), 2022, doi: 10.1145/3526064.3534112

J. Kim, M. Cafaro, J. Chou, A. Sim, "SNTA’22: The 5th Workshop on Systems and Network Telemetry and Analytics", In the proceedings of The 31st ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC'22), 2022, doi: 10.1145/3502181.3535108

Runzhou Han, Suren Byna, Houjun Tang, Bin Dong, and Mai Zheng,, "PROV-IO: An I/O-Centric Provenance Framework for Scientific Data on HPC Systems", HPDC 2022, June 23, 2022,

Meriam Gay Bautista, Patricia Gonzalez-Guerrero, Darren Lyles, Kylie Huch, George Michelogiannakis, "Superconducting Digital DIT Butterfly Unit for Fast Fourier Transform Using Race Logic", 2022 20th IEEE Interregional NEWCAS Conference (NEWCAS), IEEE, June 2022, 441-445,

Daniel Arnold, Sy-Toan Ngo, Ciaran Roberts, Yize Chen, Anna Scaglione, Sean Peisert, "Adam-based Augmented Random Search for Control Policies for Distributed Energy Resource Cyber Attack Mitigation", Proceedings of the 2022 American Control Conference (ACC), June 2022,

K. Ibrahim, L. Oliker,, "Preprocessing Pipeline Optimization for Scientific Deep-Learning Workloads", IPDPS 22, June 3, 2022,

Y. Ma, F. Rusu, K. Wu, A. Sim, "Adaptive Optimization for Sparse Data on Heterogeneous GPUs", 4th Workshop on Scalable Deep Learning over Parallel And Distributed Infrastructures (ScaDL 2022), in conjunction with the 36th IEEE International Parallel & Distributed Processing Symposium, 2022, doi: 10.1109/IPDPSW55747.2022.00177

K. Wang, S. Lee, J. Balewski, A. Sim, P. Nugent, A. Agrawal, A. Choudhary, K. Wu, W-K. Liao, "Using Multi-resolution Data to Accelerate Neural Network Training in Scientific Applications", 22nd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2022), 2022, doi: 10.1109/CCGrid54584.2022.00050

George Michelogiannakis, Madeleine Glick, John Shalf, Keren Bergman, "Photonics as a means to implement intra-rack resource disaggregation", Proceedings Volume 12027, Metro and Data Center Optical Networks and Short-Reach Links V, March 2022, doi: https://doi.org/10.1117/12.2607317

Patricia Gonzalez-Guerrero, Meriam Gay Bautista, Darren Lyles, George Michelogiannakis, "Temporal and SFQ Pulse-Streams Encoding for Area-Efficient Superconducting Accelerators", 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS ’22), ACM, February 2022,

X. Zhu, Y. Liu, P. Ghysels, D. Bindal, X. S. Li, "GPTuneBand: multi-task and multi-fidelity Bayesian optimization for autotuning large-scale high performance computing applications", SIAM PP, February 23, 2022,

2021

Y. Cho, J. W. Demmel, X. S. Li, Y. Liu, H. Luo, "Enhancing autotuning capability with a history database", IEEE 14th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC), December 20, 2021,

J. Bang, C. Kim, K. Wu, A. Sim, S. Byna, H. Sung, H. Eom, "An In-Depth I/O Pattern Analysis in HPC Systems", IEEE International Conference on High Performance Computing, Data & Analytics (HiPC2021), 2021, doi: 10.1109/HiPC53243.2021.00056

S. Lee, Q. Kang, K. Wang, J. Balewski, A. Sim, A. Agrawal, A. Choudhary, P. Nugent, K. Wu, W-K. Liao, "Asynchronous I/O Strategy for Large-Scale Deep Learning Applications", IEEE International Conference on High Performance Computing, Data & Analytics (HiPC2021), 2021, doi: 10.1109/HiPC53243.2021.00046

A. Lazar, L. Jin, C. Brown, C. A. Spurlock, A. Sim, K. Wu, "Performance of the Gold Standard and Machine Learning in Predicting Vehicle Transactions", the 3rd International Workshop on Big Data Tools, Methods, and Use Cases for Innovative Scientific Discovery (BTSD 2021), 2021, doi: 10.1109/BigData52589.2021.9671286

James R. Clavin, Yue Huang, Xin Wang, Pradeep M. Prakash, Sisi Duan, Jianwu Wang, Sean Peisert, "A Framework for Evaluating BFT", Proceedings of the IEEE International Conference on Parallel and Distributed Systems (ICPADS), IEEE, December 2021,

Wei Zhang, Suren Byna, Hyogi Sim, Sangkeun Lee, Sudharshan Vazhkudai, and Yong Chen,, "Exploiting User Activeness for Data Retention in HPC Systems", International Conference for High Performance Computing, Networking, Storage and Analysis (SC '21), November 21, 2021, doi: https://doi.org/10.1145/3458817.3476201

Cong Xu, Suparna Bhattacharya, Martin Foltin, Suren Byna, and Paolo Faraboschi, "Data-Aware Storage Tiering for Deep Learning", 6th International Parallel Data Systems Workshop (PDSW) 2021, held in conjunction with SC21, November 21, 2021,

Houjun Tang, Bing Xie, Suren Byna, Phillip Carns, Quincey Koziol, Sudarsun Kannan, Jay Lofstead, and Sarp Oral,, "SCTuner: An Auto-tuner Addressing Dynamic I/O Needs on Supercomputer I/O Sub-systems", 6th International Parallel Data Systems Workshop (PDSW) 2021, held in conjunction with SC21, November 21, 2021,

Daniel Waters, Colin A. MacLean, Dan Bonachea, Paul H. Hargrove, "Demonstrating UPC++/Kokkos Interoperability in a Heat Conduction Simulation (Extended Abstract)", Parallel Applications Workshop, Alternatives To MPI+X (PAW-ATM), November 2021, doi: 10.25344/S4630V


We describe the replacement of MPI with UPC++ in an existing Kokkos code that simulates heat conduction within a rectangular 3D object, as well as an analysis of the new code’s performance on CUDA accelerators. The key challenges were packing the halos in Kokkos data structures in a way that allowed for UPC++ remote memory access, and streamlining synchronization costs. Additional UPC++ abstractions used included global pointers, distributed objects, remote procedure calls, and futures. We also make use of the device allocator concept to facilitate data management in memory with unique properties, such as GPUs. Our results demonstrate that despite the algorithm’s good semantic match to message passing abstractions, straightforward modifications to use UPC++ communication deliver vastly improved performance and scalability in the common case. We find the one-sided UPC++ version written in a natural way exhibits good performance, whereas the message-passing version written in a straightforward way exhibits performance anomalies. We argue this represents a productivity benefit for one-sided communication models.

Franz Poeschel, Juncheng E, William F. Godoy, Norbert Podhorszki, Scott Klasky, Greg Eisenhauer, Philip E. Davis, Lipeng Wan, Ana Gainaru, Junmin Gu, Fabian Koller, René Widera, Michael Bussmann, Axel Huebl, "Transitioning from file-based HPC workflows to streaming data pipelines with openPMD and ADIOS2", Smoky Mountains Computational Sciences and Engineering Conference (SMC2021), 2021,

Amir Kamil, Dan Bonachea, "Optimization of Asynchronous Communication Operations through Eager Notifications", Parallel Applications Workshop, Alternatives To MPI+X (PAW-ATM), November 2021, doi: 10.25344/S42C71


UPC++ is a C++ library implementing the Asynchronous Partitioned Global Address Space (APGAS) model. We propose an enhancement to the completion mechanisms of UPC++ used to synchronize communication operations that is designed to reduce overhead for on-node operations. Our enhancement permits eager delivery of completion notification in cases where the data transfer semantics of an operation happen to complete synchronously, for example due to the use of shared-memory bypass. This semantic relaxation allows removing significant overhead from the critical path of the implementation in such cases. We evaluate our results on three different representative systems using a combination of microbenchmarks and five variations of the the HPCChallenge RandomAccess benchmark implemented in UPC++ and run on a single node to accentuate the impact of locality. We find that in RMA versions of the benchmark written in a straightforward manner (without manually optimizing for locality), the new eager notification mode can provide up to a 25% speedup when synchronizing with promises and up to a 13.5x speedup when synchronizing with conjoined futures. We also evaluate our results using a graph matching application written with UPC++ RMA communication, where we measure overall speedups of as much as 11% in single-node runs of the unmodified application code, due to our transparent enhancements.

V. Dumont, C. Garner, A. Trivedi, C. Jones, V. Ganapati, J. Mueller, T. Perciano, M. Kiran, and M. Day, "HYPPO: A Surrogate-Based Multi-Level Parallelism Tool for Hyperparameter Optimization", 2021 IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments (MLHPC), November 15, 2021,

Devarshi Ghoshal, Ludovico Bianchi, Abdelilah Essiari, Drew Paine, Sarah Poon, Michael Beach, Alpha N'Diaye, Patrick Huck, Lavanya Ramakrishnan, "Science Capsule: Towards Sharing and Reproducibility of Scientific Workflows", 2021 IEEE Workshop on Workflows in Support of Large-Scale Science (WORKS), November 15, 2021, doi: 10.1109/WORKS54523.2021.00014

Workflows are increasingly processing large volumes of data from scientific instruments, experiments and sensors. These workflows often consist of complex data processing and analysis steps that might include a diverse ecosystem of tools and also often involve human-in-the-loop steps. Sharing and reproducing these workflows with collaborators and the larger community is critical but hard to do without the entire context of the workflow including user notes and execution environment. In this paper, we describe Science Capsule, which is a framework to capture, share, and reproduce scientific workflows. Science Capsule captures, manages and represents both computational and human elements of a workflow. It automatically captures and processes events associated with the execution and data life cycle of workflows, and lets users add other types and forms of scientific artifacts. Science Capsule also allows users to create `workflow snapshots' that keep track of the different versions of a workflow and their lineage, allowing scientists to incrementally share and extend workflows between users. Our results show that Science Capsule is capable of processing and organizing events in near real-time for high-throughput experimental and data analysis workflows without incurring any significant performance overheads.

Khaled Z. Ibrahim, Tan Nguyen, Hai Ah Nam, Wahid Bhimji, Steven Farrell, Leonid Oliker, Michael Rowan, Nicholas J. Wright, Samuel Williams, "Architectural Requirements for Deep Learning Workloads in HPC Environments", (BEST PAPER), Performance Modeling, Benchmarking, and Simulation (PMBS), November 2021,

Tan Nguyen, Erich Strohmaier, John Shalf, "Facilitating CoDesign with Automatic Code Similarity Learning", 7th Workshop on the LLVM Compiler Infrastructure in HPC (LLVM-HPC), November 14, 2021,

M Kiran, B Mohammed, Q Du, D Wang, S Shen, R Wilcox, "Controlling Laser Beam Combining via an Active Reinforcement Learning Algorithm", Advanced Solid State Lasers 2021, Washington, DC United States, October 4, 2021,

Md Abdul M Faysal, Shaikh Arifuzzaman, Cy Chan, Maximilian Bremer, Doru Popovici, John Shalf, "HyPC-Map: A Hybrid Parallel Community Detection Algorithm Using Information-Theoretic Approach", HPEC, September 20, 2021,

Bo Fang, Daoce Wang, Sian Jin, Quincey Koziol, Zhao Zhang, Qiang Guan, Suren Byna, Sriram Krishnamoorthy, and Dingwen Tao,, "Characterizing Impacts of Storage Faults on HPC Applications: A Methodology and Insights", IEEE Cluster 2021, September 1, 2021,

Tommaso Buvoli, Michael Minion, "IMEX Runge-Kutta Parareal for Non-diffusive Equations", Springer Proceedings in Mathematics & Statistics, August 25, 2021,

Sebastian Götschel, Michael Minion, Daniel Ruprecht, Robert Speck, "Twelve Ways To Fool The Masses When Giving Parallel-In-Time Results Authors", Springer Proceedings in Mathematics & Statistics, August 25, 2021,

Meriam Gay Bautista, Zhi Jackie Yao, Anastasiia Butko, Mariam Kiran, Mekena Metcalf, "Towards Automated Superconducting Circuit Calibration using Deep Reinforcement Learning", 2021 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), Tampa, FL, USA, IEEE, August 23, 2021, pp. 462-46, doi: 10.1109/ISVLSI51109.2021.00091

Meriam Gay Bautista, Patricia Gonzalez-Guerrero, Darren Lyles, George Michelogiannakis, "Superconducting Shuttle-flux Shift Buffer for Race Logic", 2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), August 2021,

Nan Ding, Yang Liu, Samuel Williams, Xiaoye S. Li, "A Message-Driven, Multi-GPU Parallel Sparse Triangular Solver", SIAM Conference on Applied and Computational Discrete Algorithms (ACDA21), July 19, 2021,

Devarshi Ghoshal, Drew Paine, Gilberto Pastorello, Abdelrahman Elbashandy, Dan Gunter, Oluwamayowa Amusat, Lavanya Ramakrishnan, "Experiences with Reproducibility: Case Studies from Scientific Workflows", (P-RECS'21) Proceedings of the 4th International Workshop on Practical Reproducible Evaluation of Computer Systems, ACM, June 21, 2021, doi: 10.1145/3456287.3465478

Reproducible research is becoming essential for science to ensure transparency and for building trust. Additionally, reproducibility provides the cornerstone for sharing of methodology that can improve efficiency. Although several tools and studies focus on computational reproducibility, we need a better understanding about the gaps, issues, and challenges for enabling reproducibility of scientific results beyond the computational stages of a scientific pipeline. In this paper, we present five different case studies that highlight the reproducibility needs and challenges under various system and environmental conditions. Through the case studies, we present our experiences in reproducing different types of data and methods that exist in an experimental or analysis pipeline. We examine the human aspects of reproducibility while highlighting the things that worked, that did not work, and that could have worked better for each of the cases. Our experiences capture a wide range of scenarios and are applicable to a much broader audience who aim to integrate reproducibility in their everyday pipelines.

A. Lazar, A. Sim, K. Wu, "GPU-based Classification for Wireless Intrusion Detection", 4th ACM International Workshop on ​System and Network Telemetry and Analysis (SNTA 2021), 2021, doi: 10.1145/3452411.3464445

Y. Wang, K. Wu, A. Sim, S. Yoo, S. Misawa, "Access Patterns of Disk Cache for Large Scientific Archive", 4th ACM International Workshop on ​System and Network Telemetry and Analysis (SNTA 2021), 2021, doi: 10.1145/3452411.3464444

E. Copps, H. Zhang, A. Sim, K. Wu, I. Monga, C. Guok, F. Würthwein, D. Davila, E. Fajardo, "Analyzing scientific data sharing patterns with in-network data caching", 4th ACM International Workshop on ​System and Network Telemetry and Analysis (SNTA 2021), 2021, doi: 10.1145/3452411.3464441

Ayaz Akram, Venkatesh Akella, Sean Peisert, Jason Lowe-Power,, "Enabling Design Space Exploration for RISC-V Secure Compute Environments", Proceedings of the Fifth Workshop on Computer Architecture Research with RISC-V (CARRV), (co-located with ISCA 2021), June 17, 2021,

E. W. Bethel, C. Heinemann, and T. Perciano, "Performance Tradeoffs in Shared-memory Platform Portable Implementations of a Stencil Kernel", Eurographics Symposium on Parallel Graphics and Visualization, June 14, 2021,

Bing Xie, Houjun Tang, Suren Byna, Jesse Hanley, Quincey Koziol, Tonglin Li, Sarp Oral,, "Battle of the Defaults: Extracting Performance Characteristics of HDF5 under Production Load", CCGrid 2021, May 31, 2021,

Ciaran Roberts, Sy-Toan Ngo, Alexandre Milesi, Anna Scaglione, Sean Peisert, Daniel Arnold, "Deep Reinforcement Learning for Mitigating Cyber-Physical DER Voltage Unbalance Attacks”", Proceedings of the 2021 American Control Conference (ACC), May 2021, doi: 10.23919/ACC50511.2021.9482815

Serges Love Teutu Talla, Isabelle Kemajou-Brown, Cy Chan, Bin Wang, "A Binary Multi-Subsystems Transportation Networks Estimation using Mobiliti Data", 2021 American Control Conference (ACC), May 25, 2021,

Maximilian Bremer, John Bachan, Cy Chan, and Clint Dawson, "Speculative Parallel Execution for Local Timestepping", 2021 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, May 21, 2021,

Md Taufique Hussain, Oguz Selvitopi, Aydin Buluç, Ariful Azad, "Communication-Avoiding and Memory-Constrained Sparse Matrix-Matrix Multiplication at Extreme Scale", 2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS), May 2021, doi: 10.1109/IPDPS49936.2021.00018

Y. Ma, F. Ruso, A. Sim, K. Wu, "Adaptive Stochastic Gradient Descent for Deep Learning on Heterogeneous CPU+GPU Architectures", Heterogeneity in Computing Workshop (HCW 2021), in conjunction with the 35th IEEE International Parallel & Distributed Processing Symposium (IPDPS), 2021, doi: 10.1109/IPDPSW52791.2021.00012

Ayaz Akram, Anna Giannakou, Venkatesh Akella, Jason Lowe-Power, Sean Peisert, "Performance Analysis of Scientific Computing Workloads on General Purpose TEEs", Proceedings of the 35th IEEE International Parallel & Distributed Processing Symposium (IPDPS), IEEE, May 2021, doi: 10.1109/IPDPS49936.2021.00115

George Michelogiannakis, Darren Lyles, Patricia Gonzalez-Guerrero, Meriam Bautista, Dilip Vasudevan, Anastasiia Butko, "SRNoC: A Statically-Scheduled Circuit-Switched Superconducting Race Logic NoC", IEEE International Parallel and Distributed Processing Symposium (IPDPS), May 2021,

Giulia Guidi, Marquita Ellis, Daniel Rokhsar, Katherine Yelick, Aydın Buluç, "BELLA: Berkeley Efficient Long-Read to Long-Read Aligner and Overlapper", SIAM Conference on Applied and Computational Discrete Algorithms (ACDA21), 2021, doi: 10.1101/464420

Douglas Doerfler, Farzad Fatollahi-Fard, Colin MacLean, Tan Nguyen, Samuel Williams, Nicholas J. Wright, Marco Siracusa, "Experiences Porting the SU3_Bench Microbenchmark to the Intel Arria 10 and Xilinx Alveo U280 FPGAs", International Workshop on OpenCL (iWOCL), April 2021, doi: 10.1145/3456669.3456671

George Michelogiannakis, Min Yeh Teh, Madeleine Glick, John Shalf, Keren Bergman, "Maximizing the impact of emerging photonic switches at the system level", SPIE 11692, Optical Interconnects XXI, 116920Z, March 2021,

Y. Liu, W. M. Sid-Lakhdar, O. Marques, X. Zhu, C. Meng, J. W. Demmel, X. S. Li, "GPTune: multitask learning for autotuning exascale applications", PPoPP, February 17, 2021, doi: 10.1145/3437801.3441621

Tuowen Zhao, Mary Hall, Hans Johansen, Samuel Williams, "Improving Communication by Optimizing On-Node Data Movement with Data Layout", PPoPP, February 2021,

Y Segawa, H Hirose, D Kaneko, M Hasegawa, S Adachi, P Ade, MAOA Faúndez, Y Akiba, K Arnold, J Avva, C Baccigalupi, D Barron, D Beck, S Beckman, F Bianchini, D Boettger, J Borrill, J Carron, S Chapman, K Cheung, Y Chinone, K Crowley, A Cukierman, T De Haan, M Dobbs, R Dunner, HE Bouhargani, T Elleflot, J Errard, G Fabbian, S Feeney, C Feng, T Fujino, N Galitzki, N Goeckner-Wald, J Groh, G Hall, N Halverson, T Hamada, M Hazumi, C Hill, L Howe, Y Inoue, J Ito, G Jaehnig, O Jeong, N Katayama, B Keating, R Keskitalo, S Kikuchi, T Kisner, N Krachmalnicoff, A Kusaka, AT Lee, D Leon, E Linder, LN Lowry, A Mangu, F Matsuda, Y Minami, J Montgomery, M Navaroli, H Nishino, J Peloton, ATP Pham, D Poletti, G Puglisi, C Raum, CL Reichardt, C Ross, M Silva-Feaver, P Siritanasak, R Stompor, A Suzuki, O Tajima, S Takakura, S Takatori, D Tanabe, GP Teply, C Tsai, C Verges, B Westbrook, Y Zhou, "Method for rapid performance validation of large TES bolometer array for POLARBEAR-2A using a coherent millimeter-wave source", AIP Conference Proceedings, 2021, 2319, doi: 10.1063/5.0038197

Jean Luca Bez, Houjun Tang, Bing Xie, David Williams-Young, Rob Latham, Rob Ross, Sarp Oral, Suren Byna, "I/O Bottleneck Detection and Tuning: Connecting the Dots using Interactive Log Analysis", 2021 IEEE/ACM Sixth International Parallel Data Systems Workshop (PDSW), January 1, 2021, 15-22, doi: 10.1109/PDSW54622.2021.00008

Tonglin Li, Suren Byna, Quincey Koziol, Houjun Tang, Jean Luca Bez, Qiao Kang, "h5bench: HDF5 I/O Kernel Suite for Exercising HPC I/O Patterns", Cray User Group (CUG) 2021, January 1, 2021,

MG Awan, S Hofmeyr, R Egan, N Ding, A Buluc, J Deslippe, L Oliker, K Yelick, "Accelerating Large Scale de novo Metagenome Assembly Using GPUs", International Conference for High Performance Computing, Networking, Storage and Analysis, SC, January 1, 2021, doi: 10.1145/3458817.3476212

O Selvitopi, B Brock, I Nisa, A Tripathy, K Yelick, A Buluç, "Distributed-memory parallel algorithms for sparse times tall-skinny-dense matrix multiplication", Proceedings of the International Conference on Supercomputing, January 2021, 431--442, doi: 10.1145/3447818.3461472

G Guidi, M Ellis, A Buluç, K Yelick, D Culler, "10 years later: Cloud computing is closing the performance gap", ICPE 2021 - Companion of the ACM/SPEC International Conference on Performance Engineering, January 1, 2021, 41--48, doi: 10.1145/3447545.3451183

Book

2022

Paolo Calafiura and others, Artificial Intelligence for High Energy Physics, edited by Paolo Calafiura, David Rousseau, Kazuhiro Terao, (World Scientific: March 1, 2022) doi: 10.1142/12200

Bin Dong, Kesheng Wu, Suren Byna, User-Defined Tensor Data Analysis, SpringerBrief, (January 1, 2022)

Screen Shot 2022 06 24 at 1.24.03 PM

Book Chapter

2022

E. Wes Bethel, Burlen Loring, Utkarsh Ayachit, P. N. Duque, Nicola Ferrier, Joseph Insley, Junmin Gu, Kress, Patrick O’Leary, Dave Pugmire, Silvio Rizzi, Thompson, Will Usher, Gunther H. Weber, Brad Whitlock, Wolf, Kesheng Wu, "Proximity Portability and In Transit, M-to-N Data Partitioning and Movement in SENSEI", In Situ Visualization for Computational Science, ( 2022) doi: 10.1007/978-3-030-81627-8_20

E. Wes Bethel, Burlen Loring, Utkarsh Ayatchit, David Camp, P. N. Duque, Nicola Ferrier, Joseph Insley, Junmin Gu, Kress, Patrick O’Leary, David Pugmire, Silvio Rizzi, Thompson, Gunther H. Weber, Brad Whitlock, Matthew Wolf, Kesheng Wu, "The SENSEI Generic In Situ Interface: Tool and Processing Portability at Scale", In Situ Visualization for Computational Science, ( 2022) doi: 10.1007/978-3-030-81627-8_13

2021

Jan-Tobias Sohns, Gunther H. Weber, Christoph Garth, "Distributed Task-Parallel Topology-Controlled Volume Rendering", Topological Methods in Data Analysis and Visualization VI: Theory, Algorithms, and Applications, (Springer International Publishing: 2021) Pages: 55-69 doi: 10.1007/978-3-030-83500-2_4

Presentation/Talk

2023

H-C. Yang, L. Jin, A. Lazar, A. Todd-Blick, A. Sim, K. Wu, Q. Chen, C. A. Spurlock, Gender Gaps in Mode Usage, Vehicle Ownership, and Spatial Mobility When Entering Parenthood: A Life Course Perspective, Transportation Research Board 102nd Annual Meeting,, 2023,

2022

Dan Bonachea, Paul H. Hargrove, An Introduction to GASNet-EX for Chapel Users, 9th Annual Chapel Implementers and Users Workshop (CHIUW 2022), June 10, 2022,

Have you ever typed "export CHPL_COMM=gasnet"? If you’ve used Chapel with multi-locale support on a system without "Cray" in the model name, then you’ve probably used GASNet. Did you ever wonder what GASNet is? What GASNet should mean to you? This talk aims to answer those questions and more. Chapel has system-specific implementations of multi-locale communication for Cray-branded systems including the Cray XC and HPE Cray EX lines. On other systems, Chapel communication uses the GASNet communication library embedded in third-party/gasnet. In this talk, that third-party will introduce itself to you in the first person.

Video Presentation

JaeHyuk Kwack, ROOFLINE PERFORMANCE ANALYSIS W/ INTEL ADVISOR ON INTEL CPUS & GPUS, ECP Annual Meeting, May 2022,

Neil Mehta, Roofline on NVIDIA at NERSC, ECP Annual Meeting, May 2022,

Samuel Williams, Introduction to the Roofline Model, ECP Annual Meeting, May 2022,

George Michelogiannakis, Madeleine Glick, John Shalf, Keren Bergman, Photonics as a Means to Implement Intra-rack Resource Disaggregation, SPIE photonics west, March 2022,

Patricia Gonzalez-Guerrero, Meriam Gay Bautista, Darren Lyles, George Michelogiannakis, Temporal and SFQ Pulse-Streams Encoding for Area-Efficient Superconducting Accelerators, 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS ’22), February 2022,

John Wu, Ben Brown, Paolo Calafiura, Quincey Koziol, Dongeun Lee, Alex Sim, Devesh Tiwari, Support for In-Flight Data Analyses in Scientific Workflows, DOE ASCR Workshop on the Management and Storage of Scientific Data, 2022, doi: 10.2172/1843500

John Wu, Bin Dong, Alex Sim, Automating Data Management Through Unified Runtime Systems, DOE ASCR Workshop on the Management and Storage of Scientific Data, 2022, doi: 10.2172/1843500

L. Jin, A. Lazar, C. Brown, Q. Chen, A. Sim, K. Wu, S. Ravulaparthy, V. Garikapati, C. A. Spurlock, What Makes You Hold on to That Old Car? Joint Insights from Machine Learning and Multinomial Logit on Vehicle-level Transaction Decisions, Transportation Research Board 101st Annual Meeting, 2022,

2021

Daniel F. Martin, Stephen L. Cornford, Esmond G. Ng, Impact of Improved Bedrock Geometry and Basal Friction Relations on Antarctic Vulnerability to Regional Ice Shelf Collapse, Americal Geophysical Union Fall Meeting, December 15, 2021,

Anne M. Felden, Daniel F. Martin, Esmond G. Ng, SUHMO: An SUbglacial Hydrology MOdel based on the Chombo AMR framework, American Geophysical Union Fall Meeting, December 13, 2021,

Katherine A. Yelick, Amir Kamil, Damian Rouson, Dan Bonachea, Paul H. Hargrove, UPC++: An Asynchronous RMA/RPC Library for Distributed C++ Applications (SC21), Tutorial at the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC21), November 15, 2021,

UPC++ is a C++ library supporting Partitioned Global Address Space (PGAS) programming. UPC++ offers low-overhead one-sided Remote Memory Access (RMA) and Remote Procedure Calls (RPC), along with future/promise-based asynchrony to express dependencies between computation and asynchronous data movement. UPC++ supports simple/regular data structures as well as more elaborate distributed applications where communication is fine-grained and/or irregular. UPC++ provides a uniform abstraction for one-sided RMA between host and GPU/accelerator memories anywhere in the system. UPC++'s support for aggressive asynchrony enables applications to effectively overlap communication and reduce latency stalls, while the underlying GASNet-EX communication library delivers efficient low-overhead RMA/RPC on HPC networks.

This tutorial introduces UPC++, covering the memory and execution models and basic algorithm implementations. Participants gain hands-on experience incorporating UPC++ features into application proxy examples. We examine a few UPC++ applications with irregular communication (metagenomic assembler and COVID-19 simulation) and describe how they utilize UPC++ to optimize communication performance.

Suren Byna, Houjun Tang, and Quincey Koziol,, Automatic and Transparent Scientific Data Management with Object Abstractions, PASC 2021, in a Minisymposium on "Data Movement Orchestration on HPC Systems", July 31, 2021,

George Michelogiannakis, SRNoC: A Statically-Scheduled Circuit-Switched Superconducting Race Logic NoC, IEEE International Parallel and Distributed Processing Symposium, May 2021,

Jonathan Madsen, Roofline Instrumentation with TiMemory, ECP Annual Meeting, April 2021,

Khaled Ibrahim, Roofline on GPUs (advanced topics), ECP Annual Meeting, April 2021,

Jonathan Madsen, Roofline Model using NSight Compute, ECP Annual Meeting, April 2021,

Samuel Williams, Roofline Analysis on NVIDIA GPUs, ECP Annual Meeting, April 2021,

Samuel Williams, Introduction to the Roofline Model, ECP Annual Meeting, April 2021,

Dan Bonachea, GASNet-EX: A High-Performance, Portable Communication Library for Exascale, Berkeley Lab – CS Seminar, March 10, 2021,

Partitioned Global Address Space (PGAS) models, pioneered by languages such as Unified Parallel C (UPC) and Co-Array Fortran, expose one-sided communication as a key building block for High Performance Computing (HPC) applications. Architectural trends in supercomputing make such programming models increasingly attractive, and newer, more sophisticated models such as UPC++, Legion and Chapel that rely upon similar communication paradigms are gaining popularity.

GASNet-EX is a portable, open-source, high-performance communication library designed to efficiently support the networking requirements of PGAS runtime systems and other alternative models in future exascale machines. The library is an evolution of the popular GASNet communication system, building on 20 years of lessons learned. We describe several features and enhancements that have been introduced to address the needs of modern runtimes and exploit the hardware capabilities of emerging systems. Microbenchmark results demonstrate the RMA performance of GASNet-EX is competitive with several MPI implementations on current systems. GASNet-EX provides communication services that help to deliver speedups in HPC applications written using the UPC++ library, enabling new science on pre-exascale systems. 

George Michelogiannakis, Min Yeh Teh, Madeleine Glick, John Shalf, Keren Bergman, Maximizing The Impact of Emerging Photonic Switches At The System Level, SPIE photonics west, March 2021,

Report

2022

Dan Bonachea, Amir Kamil, "UPC++ v1.0 Specification, Revision 2022.9.0", Lawrence Berkeley National Laboratory Tech Report, September 30, 2022, LBNL 2001480, doi: 10.25344/S4M59P


UPC++ is a C++ library providing classes and functions that support Partitioned Global Address Space (PGAS) programming. The key communication facilities in UPC++ are one-sided Remote Memory Access (RMA) and Remote Procedure Call (RPC). All communication operations are syntactically explicit and default to non-blocking; asynchrony is managed through the use of futures, promises and continuation callbacks, enabling the programmer to construct a graph of operations to execute asynchronously as high-latency dependencies are satisfied. A global pointer abstraction provides system-wide addressability of shared memory, including host and accelerator memories. The parallelism model is primarily process-based, but the interface is thread-safe and designed to allow efficient and expressive use in multi-threaded applications. The interface is designed for extreme scalability throughout, and deliberately avoids design features that could inhibit scalability.

John Bachan, Scott B. Baden, Dan Bonachea, Johnny Corbino, Max Grossman, Paul H. Hargrove, Steven Hofmeyr, Mathias Jacquelin, Amir Kamil, Brian van Straalen, Daniel Waters, "UPC++ v1.0 Programmer’s Guide, Revision 2022.9.0", Lawrence Berkeley National Laboratory Tech Report, September 30, 2022, LBNL 2001479, doi: 10.25344/S4QW26

UPC++ is a C++ library that supports Partitioned Global Address Space (PGAS) programming. It is designed for writing efficient, scalable parallel programs on distributed-memory parallel computers. The key communication facilities in UPC++ are one-sided Remote Memory Access (RMA) and Remote Procedure Call (RPC). The UPC++ control model is single program, multiple-data (SPMD), with each separate constituent process having access to local memory as it would in C++. The PGAS memory model additionally provides one-sided RMA communication to a global address space, which is allocated in shared segments that are distributed over the processes. UPC++ also features Remote Procedure Call (RPC) communication, making it easy to move computation to operate on data that resides on remote processes.

UPC++ was designed to support exascale high-performance computing, and the library interfaces and implementation are focused on maximizing scalability. In UPC++, all communication operations are syntactically explicit, which encourages programmers to consider the costs associated with communication and data movement. Moreover, all communication operations are asynchronous by default, encouraging programmers to seek opportunities for overlapping communication latencies with other useful work. UPC++ provides expressive and composable abstractions designed for efficiently managing aggressive use of asynchrony in programs. Together, these design principles are intended to enable programmers to write applications using UPC++ that perform well even on hundreds of thousands of cores.

Mateusz Pusz, Gašper Ažman, Bengt Gustafsson, Colin MacLean, Corentin Jabot, "Universal Template Parameters", ISO C++ Standard Mailing, September 2022,

This paper proposes a unified model for universal template parameters (UTPs) and dependent names, enabling more comprehensive and consistent template metaprogramming. Universal template parameters allow for a generic apply and other higher-order template metafunctions, including certain type traits.

Emily K. Adams, Daniel Gunter, Ryan Kiser, Mark Krenz, Sean Peisert, Susan Sons, John Zage, "Findings of the 2022 Trusted CI Study on the Security of Operational Technology in NSF Scientific Research", Trusted CI Report, July 15, 2022, doi: doi.org/10.5281/zenodo.6828675

D. Bard, C. Snavely, L. Gerhardt, J. Lee, B. Totzke, K. Antypas, W. Arndt, J. Blaschke, S. Byna, R. Cheema, S. Cholia, M. Day, B. Enders, A. Gaur, A. Greiner, T. Groves, M. Kiran, Q. Koziol, T. Lehman, K. Rowland, C. Samuel, A. Selvarajan, A. Sim, D. Skinner, L. Stephey, R. Thomas, G. Torok, "LBNL Superfacility Project Report", Lawrence Berkeley National Laboratory, 2022, doi: 10.48550/arXiv.2206.11992

John Bachan, Scott B. Baden, Dan Bonachea, Max Grossman, Paul H. Hargrove, Steven Hofmeyr, Mathias Jacquelin, Amir Kamil, Brian van Straalen, Daniel Waters, "UPC++ v1.0 Programmer’s Guide, Revision 2022.3.0", Lawrence Berkeley National Laboratory Tech Report, March 2022, LBNL 2001453, doi: 10.25344/S41C7Q


UPC++ is a C++ library that supports Partitioned Global Address Space (PGAS) programming. It is designed for writing efficient, scalable parallel programs on distributed-memory parallel computers. The key communication facilities in UPC++ are one-sided Remote Memory Access (RMA) and Remote Procedure Call (RPC). The UPC++ control model is single program, multiple-data (SPMD), with each separate constituent process having access to local memory as it would in C++. The PGAS memory model additionally provides one-sided RMA communication to a global address space, which is allocated in shared segments that are distributed over the processes. UPC++ also features Remote Procedure Call (RPC) communication, making it easy to move computation to operate on data that resides on remote processes.

UPC++ was designed to support exascale high-performance computing, and the library interfaces and implementation are focused on maximizing scalability. In UPC++, all communication operations are syntactically explicit, which encourages programmers to consider the costs associated with communication and data movement. Moreover, all communication operations are asynchronous by default, encouraging programmers to seek opportunities for overlapping communication latencies with other useful work. UPC++ provides expressive and composable abstractions designed for efficiently managing aggressive use of asynchrony in programs. Together, these design principles are intended to enable programmers to write applications using UPC++ that perform well even on hundreds of thousands of cores.

Dan Bonachea, Amir Kamil, "UPC++ v1.0 Specification, Revision 2022.3.0", Lawrence Berkeley National Laboratory Tech Report, March 2022, LBNL 2001452, doi: 10.25344/S4530J


UPC++ is a C++ library providing classes and functions that support Partitioned Global Address Space (PGAS) programming. The key communication facilities in UPC++ are one-sided Remote Memory Access (RMA) and Remote Procedure Call (RPC). All communication operations are syntactically explicit and default to non-blocking; asynchrony is managed through the use of futures, promises and continuation callbacks, enabling the programmer to construct a graph of operations to execute asynchronously as high-latency dependencies are satisfied. A global pointer abstraction provides system-wide addressability of shared memory, including host and accelerator memories. The parallelism model is primarily process-based, but the interface is thread-safe and designed to allow efficient and expressive use in multi-threaded applications. The interface is designed for extreme scalability throughout, and deliberately avoids design features that could inhibit scalability.

2021

Andrew Adams, Kay Avila, Elisa Heymann, Mark Krenz, Jason R. Lee, Barton Miller, Sean Peisert, "Guide to Securing Scientific Software", Trusted CI Report, December 14, 2021, doi: 10.5281/zenodo.5777646

John Bachan, Scott B. Baden, Dan Bonachea, Max Grossman, Paul H. Hargrove, Steven Hofmeyr, Mathias Jacquelin, Amir Kamil, Brian van Straalen, Daniel Waters, "UPC++ v1.0 Programmer’s Guide, Revision 2021.9.0", Lawrence Berkeley National Laboratory Tech Report, September 2021, LBNL 2001424, doi: 10.25344/S4SW2T


UPC++ is a C++ library that supports Partitioned Global Address Space (PGAS) programming. It is designed for writing efficient, scalable parallel programs on distributed-memory parallel computers. The key communication facilities in UPC++ are one-sided Remote Memory Access (RMA) and Remote Procedure Call (RPC). The UPC++ control model is single program, multiple-data (SPMD), with each separate constituent process having access to local memory as it would in C++. The PGAS memory model additionally provides one-sided RMA communication to a global address space, which is allocated in shared segments that are distributed over the processes. UPC++ also features Remote Procedure Call (RPC) communication, making it easy to move computation to operate on data that resides on remote processes.

UPC++ was designed to support exascale high-performance computing, and the library interfaces and implementation are focused on maximizing scalability. In UPC++, all communication operations are syntactically explicit, which encourages programmers to consider the costs associated with communication and data movement. Moreover, all communication operations are asynchronous by default, encouraging programmers to seek opportunities for overlapping communication latencies with other useful work. UPC++ provides expressive and composable abstractions designed for efficiently managing aggressive use of asynchrony in programs. Together, these design principles are intended to enable programmers to write applications using UPC++ that perform well even on hundreds of thousands of cores.

Andrew Adams, Kay Avila, Elisa Heymann, Mark Krenz, Jason R. Lee, Barton Miller, Sean Peisert, "The State of the Scientific Software World: Findings of the 2021 Trusted CI Software Assurance Annual Challenge Interviews", Trusted CI Report, September 29, 2021,

Dan Bonachea, Amir Kamil, "UPC++ v1.0 Specification, Revision 2021.9.0", Lawrence Berkeley National Laboratory Tech Report, September 2021, LBNL 2001425, doi: 10.25344/S4XK53


UPC++ is a C++ library providing classes and functions that support Partitioned Global Address Space (PGAS) programming. The key communication facilities in UPC++ are one-sided Remote Memory Access (RMA) and Remote Procedure Call (RPC). All communication operations are syntactically explicit and default to non-blocking; asynchrony is managed through the use of futures, promises and continuation callbacks, enabling the programmer to construct a graph of operations to execute asynchronously as high-latency dependencies are satisfied. A global pointer abstraction provides system-wide addressability of shared memory, including host and accelerator memories. The parallelism model is primarily process-based, but the interface is thread-safe and designed to allow efficient and expressive use in multi-threaded applications. The interface is designed for extreme scalability throughout, and deliberately avoids design features that could inhibit scalability.

Dan Bonachea, "UPC++ as_eager Working Group Draft, Revision 2020.6.2", Lawrence Berkeley National Laboratory Tech Report, August 9, 2021, LBNL 2001416, doi: 10.25344/S4FK5R

This draft proposes an extension for a new future-based completion variant that can be more effectively streamlined for RMA and atomic access operations that happen to be satisfied at runtime using purely node-local resources. Many such operations are most efficiently performed synchronously using load/store instructions on shared-memory mappings, where the actual access may only require a few CPU instructions. In such cases we believe it’s critical to minimize the overheads imposed by the UPC++ runtime and completion queues, in order to enable efficient operation on hierarchical node hardware using shared-memory bypass.

The new upcxx::{source,operation}_cx::as_eager_future() completion variant accomplishes this goal by relaxing the current restriction that future-returning access operations must return a non-ready future whose completion is deferred until a subsequent explicit invocation of user-level progress. This relaxation allows access operations that are completed synchronously to instead return a ready future, thereby avoiding most or all of the runtime costs associated with deferment of future completion and subsequent mandatory entry into the progress engine.

We additionally propose to make this new as_eager_future() completion variant the new default completion for communication operations that currently default to returning a future. This should encourage use of the streamlined variant, and may provide performance improvements to some codes without source changes. A mechanism is proposed to restore the legacy behavior on-demand for codes that might happen to rely on deferred completion for correctness.

Finally, we propose a new as_eager_promise() completion variant that extends analogous improvements to promise-based completion, and corresponding changes to the default behavior of as_promise().

Drew Paine, Sarah Poon, Lavanya Ramakrishnan, "Investigating User Experiences with Data Abstractions on High Performance Computing Systems", June 29, 2021, LBNL LBNL-2001374,

Scientific exploration generates expanding volumes of data that commonly require High Performance Computing (HPC) systems to facilitate research. HPC systems are complex ecosystems of hardware and software that frequently are not user friendly. The Usable Data Abstractions (UDA) project set out to build usable software for scientific workflows in HPC environments by undertaking multiple rounds of qualitative user research. Qualitative research investigates how individuals accomplish their work and our interview-based study surfaced a variety of insights about the experiences of working in and with HPC ecosystems. This report examines multiple facets to the experiences of scientists and developers using and supporting HPC systems. We discuss how stakeholders grasp the design and configuration of these systems, the impacts of abstraction layers on their ability to successfully do work, and the varied perceptions of time that shape this work. Examining the adoption of the Cori HPC at NERSC we explore the anticipations and lived experiences of users interacting with this system's novel storage feature, the Burst Buffer. We present lessons learned from across these insights to illustrate just some of the challenges HPC facilities and their stakeholders need to account for when procuring and supporting these essential scientific resources to ensure their usability and utility to a variety of scientific practices.

J. Goings, H. Hu, C. Yang, X. Li, "Reinforcement Learning Configuration Interaction", March 31, 2021,

Dan Bonachea, Amir Kamil, "UPC++ v1.0 Specification, Revision 2021.3.0", Lawrence Berkeley National Laboratory Tech Report, March 31, 2021, LBNL 2001388, doi: 10.25344/S4K881

UPC++ is a C++11 library providing classes and functions that support Partitioned Global Address Space (PGAS) programming. The key communication facilities in UPC++ are one-sided Remote Memory Access (RMA) and Remote Procedure Call (RPC). All communication operations are syntactically explicit and default to non-blocking; asynchrony is managed through the use of futures, promises and continuation callbacks, enabling the programmer to construct a graph of operations to execute asynchronously as high-latency dependencies are satisfied. A global pointer abstraction provides system-wide addressability of shared memory, including host and accelerator memories. The parallelism model is primarily process-based, but the interface is thread-safe and designed to allow efficient and expressive use in multi-threaded applications. The interface is designed for extreme scalability throughout, and deliberately avoids design features that could inhibit scalability.

R. Van Beeumen, L. Perisa, D. Kressner, C. Yang, "A Flexible Power Method for Solving Infinite Dimensional Tensor Eigenvalue Problems", January 30, 2021,

Poster

2022

Katherine Rasmussen, Damian Rouson, Naje George, Dan Bonachea, Hussain Kadhem, Brian Friesen, "Agile Acceleration of LLVM Flang Support for Fortran 2018 Parallel Programming", Research Poster at the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC22), November 2022, doi: 10.25344/S4CP4S

The LLVM Flang compiler ("Flang") is currently Fortran 95 compliant, and the frontend can parse Fortran 2018. However, Flang does not have a comprehensive 2018 test suite and does not fully implement the static semantics of the 2018 standard. We are investigating whether agile software development techniques, such as pair programming and test-driven development (TDD), can help Flang to rapidly progress to Fortran 2018 compliance. Because of the paramount importance of parallelism in high-performance computing, we are focusing on Fortran’s parallel features, commonly denoted “Coarray Fortran.” We are developing what we believe are the first exhaustive, open-source tests for the static semantics of Fortran 2018 parallel features, and contributing them to the LLVM project. A related effort involves writing runtime tests for parallel 2018 features and supporting those tests by developing a new parallel runtime library: the CoArray Fortran Framework of Efficient Interfaces to Network Environments (Caffeine).

J. Bellavita, A. Sim (advisor), K. Wu (advisor), "Predicting Scientific Dataset Popularity Using dCache Logs", ACM/IEEE The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC’22), ACM Student Research Competition (SRC), 2022,

C. Sim, C. Guok (advisor), A. Sim (advisor), K. Wu (advisor), "Data Throughput Performance Trends of Regional Scientific Data Cache", ACM/IEEE The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC’22), ACM Student Research Competition (SRC), 2022,

Paul H. Hargrove, Dan Bonachea, Amir Kamil, Colin A. MacLean, Damian Rouson, Daniel Waters, "UPC++ and GASNet: PGAS Support for Exascale Apps and Runtimes (ECP'22)", Poster at Exascale Computing Project (ECP) Annual Meeting 2022, May 5, 2022,

We present UPC++ and GASNet-EX, distributed libraries which together enable one-sided, lightweight communication such as arises in irregular applications, libraries and frameworks running on exascale systems.

UPC++ is a C++ PGAS library, featuring APIs for Remote Procedure Call (RPC) and for Remote Memory Access (RMA) to host and GPU memories.  The combination of these two features yields performant, scalable solutions to problems of interest within ECP.

GASNet-EX is PGAS communication middleware, providing the foundation for UPC++ and Legion, plus numerous non-ECP clients.  GASNet-EX RMA interfaces match or exceed the performance of MPI-RMA across a variety of pre-exascale systems.

 

A. Pereira, A. Sim, K. Wu, S. Yoo, H. Ito, "Data access pattern analysis for dCache storage system", International Conference on High Performance Computing in Asia-Pacific Region (HPC Asia 2022), 2022,

2021

Samuel Benjamin Kachuck, Morgan Whitcomb, Jeremy N Bassis, Daniel F Martin, and Stephen F Price,, "When are (simulations of) ice shelves stable? Stabilizing forces in fracture-permitting models", AGU Fall Meeting, December 16, 2021,

Courtney Shafer, Daniel F Martin and Esmond G Ng, "Comparing the Shallow-Shelf and L1L2 Approximations using BISICLES in the Context of MISMIP+ with Buttressing Effects", AGU Fall Meeting, December 13, 2021,

J. Cheung, A. Sim, J. Kim, K. Wu, "Performance Prediction of Large Data Transfers", ACM/IEEE The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC21), ACM Student Research Competition (SRC), 2021,

Paul H. Hargrove, Dan Bonachea, Colin A. MacLean, Daniel Waters, "GASNet-EX Memory Kinds: Support for Device Memory in PGAS Programming Models", The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC'21) Research Poster, November 2021, doi: 10.25344/S4P306

Lawrence Berkeley National Lab is developing a programming system to support HPC application development using the Partitioned Global Address Space (PGAS) model. This work includes two major components: UPC++ (a C++ template library) and GASNet-EX (a portable, high-performance communication library). This poster describes recent advances in GASNet-EX to efficiently implement Remote Memory Access (RMA) operations to and from memory on accelerator devices such as GPUs. Performance is illustrated via benchmark results from UPC++ and the Legion programming system, both using GASNet-EX as their communications library.

E. Copps, A. Sim (Advisor), K. Wu (Advisor), "Analyzing scientific data sharing patterns with in-network data caching", ACM Richard Tapia Celebration of Diversity in Computing (TAPIA 2021), ACM Student Research Competition (SRC), 2021,

Paul H. Hargrove, Dan Bonachea, Max Grossman, Amir Kamil, Colin A. MacLean, Daniel Waters, "UPC++ and GASNet: PGAS Support for Exascale Apps and Runtimes (ECP'21)", Poster at Exascale Computing Project (ECP) Annual Meeting 2021, April 2021,

We present UPC++ and GASNet-EX, which together enable one-sided, lightweight communication such as arises in irregular applications, libraries and frameworks running on exascale systems.

UPC++ is a C++ PGAS library, featuring APIs for Remote Memory Access (RMA) and Remote Procedure Call (RPC).  The combination of these two features yields performant, scalable solutions to problems of interest within ECP.

GASNet-EX is PGAS communication middleware, providing the foundation for UPC++ and Legion, plus numerous non-ECP clients.  GASNet-EX RMA interfaces match or exceed the performance of MPI-RMA across a variety of pre-exascale systems

Other

2022

Alina Lazar, others, Accelerating the Inference of the Exa.TrkX Pipeline, 20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research: AI Decoded - Towards Sustainable, Diverse, Performant and Effective Scientific Computing, 2022,

Chun-Yi Wang, others, Reconstruction of Large Radius Tracks with the Exa.TrkX pipeline, 20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research: AI Decoded - Towards Sustainable, Diverse, Performant and Effective Scientific Computing, 2022,

Sunanda Banerjee, others, Detector and Beamline Simulation for Next-Generation High Energy Physics Experiments, 2022 Snowmass Summer Study, 2022,

Meghna Bhattacharya, others, Portability: A Necessary Approach for Future Scientific Software, 2022 Snowmass Summer Study, 2022,

Christopher D. Jones, Kyle Knoepfel, Paolo Calafiura, Charles Leggett, Vakhtang Tsulaia, Evolution of HEP Processing Frameworks, 2022 Snowmass Summer Study, 2022,

Savannah Thais, Paolo Calafiura, Grigorios Chachamis, Gage DeZoort, Javier Duarte, Sanmay Ganguly, Michael Kagan, Daniel Murnane, Mark S. Neubauer, Kazuhiro Terao, Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges, 2022 Snowmass Summer Study, 2022,

H Weierbach, AR Lima, JD Willard, VC Hendrix, DS Christianson, M Lubich, C Varadharajan, Stream Temperature Predictions for River Basin Management in the Pacific Northwest and Mid-Atlantic Regions Using Machine Learning, Water (Switzerland), 2022, doi: 10.3390/w14071032

M Galloway, KJ Andersen, R Aurlien, R Banerji, M Bersanelli, S Bertocco, M Brilenkov, M Carbone, LPL Colombo, HK Eriksen, MK Foss, C Franceschet, U Fuskeland, S Galeotta, S Gerakakis, E Gjerløw, B Hensley, D Herman, M Iacobellis, M Ieronymaki, HT Ihle, JB Jewell, A Karakci, E Keihänen, R Keskitalo, G Maggio, D Maino, M Maris, S Paradiso, B Partridge, M Reinecke, A-S Suur-Uski, TL Svalheim, D Tavagnacco, H Thommesen, DJ Watts, IK Wehus, A Zacchei, BeyondPlanck III. Commander3, 2022,

M Galloway, M Reinecke, KJ Andersen, R Aurlien, R Banerji, M Bersanelli, S Bertocco, M Brilenkov, M Carbone, LPL Colombo, HK Eriksen, MK Foss, C Franceschet, U Fuskeland, S Galeotta, S Gerakakis, E Gjerløw, B Hensley, D Herman, M Iacobellis, M Ieronymaki, HT Ihle, JB Jewell, A Karakci, E Keihänen, R Keskitalo, G Maggio, D Maino, M Maris, S Paradiso, B Partridge, A-S Suur-Uski, TL Svalheim, D Tavagnacco, H Thommesen, DJ Watts, IK Wehus, A Zacchei, BeyondPlanck VIII. Efficient Sidelobe Convolution and Correction through Spin Harmonics, 2022,

TL Svalheim, KJ Andersen, R Aurlien, R Banerji, M Bersanelli, S Bertocco, M Brilenkov, M Carbone, LPL Colombo, HK Eriksen, MK Foss, C Franceschet, U Fuskeland, S Galeotta, M Galloway, S Gerakakis, E Gjerløw, B Hensley, D Herman, M Iacobellis, M Ieronymaki, HT Ihle, JB Jewell, A Karakci, E Keihänen, R Keskitalo, G Maggio, D Maino, M Maris, S Paradiso, B Partridge, M Reinecke, A-S Suur-Uski, D Tavagnacco, H Thommesen, DJ Watts, IK Wehus, A Zacchei, A Zonca, BeyondPlanck X. Bandpass and beam leakage corrections, 2022,

D Herman, B Hensley, KJ Andersen, R Aurlien, R Banerji, M Bersanelli, S Bertocco, M Brilenkov, M Carbone, LPL Colombo, HK Eriksen, MK Foss, C Franceschet, U Fuskeland, S Galeotta, M Galloway, S Gerakakis, E Gjerløw, M Iacobellis, M Ieronymaki, HT Ihle, JB Jewell, A Karakci, E Keihänen, R Keskitalo, G Maggio, D Maino, M Maris, S Paradiso, B Partridge, M Reinecke, A-S Suur-Uski, TL Svalheim, D Tavagnacco, H Thommesen, DJ Watts, IK Wehus, A Zacchei, BeyondPlanck XVI. Limits on Large-Scale Polarized Anomalous Microwave Emission from Planck LFI and WMAP, 2022,

KJ Andersen, R Aurlien, R Banerji, M Bersanelli, S Bertocco, M Brilenkov, M Carbone, LPL Colombo, HK Eriksen, MK Foss, C Franceschet, U Fuskeland, S Galeotta, M Galloway, S Gerakakis, E Gjerløw, B Hensley, D Herman, M Iacobellis, M Ieronymaki, HT Ihle, JB Jewell, A Karakci, E Keihänen, R Keskitalo, G Maggio, D Maino, M Maris, S Paradiso, B Partridge, M Reinecke, A-S Suur-Uski, TL Svalheim, D Tavagnacco, H Thommesen, M Tomasi, DJ Watts, IK Wehus, A Zacchei, BeyondPlanck XIV. Intensity foreground sampling, degeneracies and priors, 2022,

L Collaboration, E Allys, K Arnold, J Aumont, R Aurlien, S Azzoni, C Baccigalupi, AJ Banday, R Banerji, RB Barreiro, N Bartolo, L Bautista, D Beck, S Beckman, M Bersanelli, F Boulanger, M Brilenkov, M Bucher, E Calabrese, P Campeti, A Carones, FJ Casas, A Catalano, V Chan, K Cheung, Y Chinone, SE Clark, F Columbro, G D Alessandro, PD Bernardis, TD Haan, EDL Hoz, MD Petris, SD Torre, P Diego-Palazuelos, T Dotani, JM Duval, T Elleflot, HK Eriksen, J Errard, T Essinger-Hileman, F Finelli, R Flauger, C Franceschet, U Fuskeland, M Galloway, K Ganga, M Gerbino, M Gervasi, RT Génova-Santos, T Ghigna, S Giardiello, E Gjerløw, J Grain, F Grupp, A Gruppuso, JE Gudmundsson, NW Halverson, P Hargrave, T Hasebe, M Hasegawa, M Hazumi, S Henrot-Versillé, B Hensley, LT Hergt, D Herman, E Hivon, RA Hlozek, AL Hornsby, Y Hoshino, J Hubmayr, K Ichiki, T Iida, H Imada, H Ishino, G Jaehnig, N Katayama, A Kato, R Keskitalo, T Kisner, Y Kobayashi, A Kogut, K Kohri, E Komatsu, K Komatsu, K Konishi, N Krachmalnicoff, CL Kuo, L Lamagna, M Lattanzi, AT Lee, C Leloup, F Levrier, E Linder, G Luzzi, J Macias-Perez, B Maffei, D Maino, S Mandelli, E Martínez-González, S Masi, M Massa, S Matarrese, FT Matsuda, T Matsumura, L Mele, M Migliaccio, Y Minami, A Moggi, J Montgomery, L Montier, G Morgante, B Mot, Y Nagano, T Nagasaki, R Nagata, R Nakano, T Namikawa, F Nati, P Natoli, S Nerval, F Noviello, K Odagiri, S Oguri, H Ohsaki, L Pagano, A Paiella, D Paoletti, A Passerini, G Patanchon, F Piacentini, M Piat, G Polenta, D Poletti, T Prouvé, G Puglisi, D Rambaud, C Raum, S Realini, M Reinecke, M Remazeilles, A Ritacco, G Roudil, JA Rubino-Martin, M Russell, H Sakurai, Y Sakurai, M Sasaki, D Scott, Y Sekimoto, K Shinozaki, M Shiraishi, P Shirron, G Signorelli, F Spinella, S Stever, R Stompor, S Sugiyama, RM Sullivan, A Suzuki, TL Svalheim, E Switzer, R Takaku, H Takakura, Y Takase, A Tartari, Y Terao, J Thermeau, H Thommesen, KL Thompson, M Tomasi, M Tominaga, M Tristram, M Tsuji, M Tsujimoto, L Vacher, P Vielva, N Vittorio, W Wang, K Watanuki, IK Wehus, J Weller, B Westbrook, J Wilms, EJ Wollack, J Yumoto, M Zannoni, Probing Cosmic Inflation with the LiteBIRD Cosmic Microwave Background Polarization Survey, 2022,

DJ Watts, M Galloway, HT Ihle, KJ Andersen, R Aurlien, R Banerji, A Basyrov, M Bersanelli, S Bertocco, M Brilenkov, M Carbone, LPL Colombo, HK Eriksen, JR Eskilt, MK Foss, C Franceschet, U Fuskeland, S Galeotta, S Gerakakis, E Gjerløw, B Hensley, D Herman, M Iacobellis, M Ieronymaki, JB Jewell, A Karakci, E Keihänen, R Keskitalo, JGS Lunde, G Maggio, D Maino, M Maris, S Paradiso, B Partridge, M Reinecke, M San, NO Stutzer, A-S Suur-Uski, TL Svalheim, D Tavagnacco, H Thommesen, IK Wehus, A Zacchei, From BeyondPlanck to Cosmoglobe: Preliminary WMAP Q-band analysis, 2022,

S. Dhawan, A. Goobar, M. Smith, J. Johansson, M. Rigault, J. Nordin, R. Biswas, D. Goldstein, P. Nugent, Y. -L. Kim, A. A. Miller, M. J. Graham, M. Medford, M. M. Kasliwal, S. R. Kulkarni, Dmitry A. Duev, E. Bellm, P. Rosnet, R. Riddle, J. Sollerman, The Zwicky Transient Facility Type Ia supernova survey: first data release and results, Monthly Notices of the RAS, Pages: 2228-2241 2022, doi: 10.1093/mnras/stab3093

Yuan Qi Ni, Dae-Sik Moon, Maria R. Drout, Abigail Polin, David J. Sand, Santiago Gonz\ alez-Gait\ an, Sang Chul Kim, Youngdae Lee, Hong Soo Park, D. Andrew Howell, Peter E. Nugent, Anthony L. Piro, Peter J. Brown, Llu\ \is Galbany, Jamison Burke, Daichi Hiramatsu, Griffin Hosseinzadeh, Stefano Valenti, Niloufar Afsariardchi, Jennifer E. Andrews, John Antoniadis, Iair Arcavi, Rachael L. Beaton, K. Azalee Bostroem, Raymond G. Carlberg, S. Bradley Cenko, Sang-Mok Cha, Yize Dong, Avishay Gal-Yam, Joshua Haislip, Thomas W. -S. Holoien, Sean D. Johnson, Vladimir Kouprianov, Yongseok Lee, Christopher D. Matzner, Nidia Morrell, Curtis McCully, Giuliano Pignata, Daniel E. Reichart, Jeffrey Rich, Stuart D. Ryder, Nathan Smith, Samuel Wyatt, Sheng Yang, Infant-phase reddening by surface Fe-peak elements in a normal type Ia supernova, Nature Astronomy, 2022, doi: 10.1038/s41550-022-01603-4

Melissa L. Graham, Christoffer Fremling, Daniel A. Perley, Rahul Biswas, Christopher A. Phillips, Jesper Sollerman, Peter E. Nugent, Sarafina Nance, Suhail Dhawan, Jakob Nordin, Ariel Goobar, Adam Miller, James D. Neill, Xander J. Hall, Matthew J. Hankins, Dmitry A. Duev, Mansi M. Kasliwal, Mickael Rigault, Eric C. Bellm, David Hale, Przemek Mr\ oz, S. R. Kulkarni, Supernova siblings and their parent galaxies in the Zwicky Transient Facility Bright Transient Survey, Monthly Notices of the RAS, Pages: 241-254 2022, doi: 10.1093/mnras/stab3802

MB Simmonds, WJ Riley, DA Agarwal, X Chen, S Cholia, R Crystal-Ornelas, ET Coon, D Dwivedi, VC Hendrix, M Huang, A Jan, Z Kakalia, J Kumar, CD Koven, L Li, M Melara, L Ramakrishnan, DM Ricciuto, AP Walker, W Zhi, Q Zhu, C Varadharajan, Guidelines for Publicly Archiving Terrestrial Model Data to Enhance Usability, Intercomparison, and Synthesis, Data Science Journal, 2022, doi: 10.5334/dsj-2022-003

C Varadharajan, VC Hendrix, DS Christianson, M Burrus, C Wong, SS Hubbard, DA Agarwal, BASIN-3D: A brokering framework to integrate diverse environmental data, Computers and Geosciences, 2022, doi: 10.1016/j.cageo.2021.105024

B Faybishenko, R Versteeg, G Pastorello, D Dwivedi, C Varadharajan, D Agarwal, Challenging problems of quality assurance and quality control (QA/QC) of meteorological time series data, Stochastic Environmental Research and Risk Assessment, Pages: 1049--1062 2022, doi: 10.1007/s00477-021-02106-w

Sean Peisert, Unsafe at Any Clock Speed: the Insecurity of Computer System Design, Implementation, and Operation [From the Editors], IEEE Security & Privacy, Pages: 4-9 January 2022, doi: 10.0.4.85/MSEC.2021.3127086

F Molz, B Faybishenko, D Agarwal, A broad exploration of nonlinear dynamics in microbial systems motivated by chemostat experiments producing deterministic chaos., 2022,

2021

Ammar Haydari, Michael Zhang, Chen-Nee Chuah, Jane Macfarlane, Sean Peisert, Adaptive Differential Privacy Mechanism for Aggregated Mobility Dataset, arXiv preprint arXiv:2112.08487, December 10, 2021,

Yize Chen, Yuanyuan Shi, Daniel Arnold, Sean Peisert, SAVER: Safe Learning-Based Controller for Real-Time Voltage Regulation, arXiv preprint arXiv:2111.15152,, November 30, 2021,

Luca Pion-Tonachini, Kristofer Bouchard, Hector Garcia Martin, Sean Peisert, W. Bradley Holtz, Anil Aswani, Dipankar Dwivedi, Haruko Wainwright, Ghanshyam Pilania, Benjamin Nachman, Babetta L. Marrone, Nicola Falco, Prabhat, Daniel Arnold, Alejandro Wolf-Yadlin, Sarah Powers, Sharlee Climer, Quinn Jackson, Ty Carlson, Michael Sohn, Petrus Zwart, Neeraj Kumar, Amy Justice, Claire Tomlin, Daniel Jacobson, Gos Micklem, Georgios V. Gkoutos, Peter J. Bickel, Jean-Baptiste Cazier, Juliane Müller, Bobbie-Jo Webb-Robertson, Rick Stevens, Mark Anderson, Ken Kreutz-Delgado, Michael W. Mahoney, James B. Brown,, Learning from Learning Machines: a New Generation of AI Technology to Meet the Needs of Science, arXiv preprint arXiv:2111.13786, November 27, 2021,

Sachin Kadam, Anna Scaglione, Nikhil Ravi, Sean Peisert, Brent Lunghino, Aram Shumavon, Optimum Noise Mechanism for Differentially Private Queries in Discrete Finite Sets, arXiv preprint arXiv:2111.11661, November 23, 2021,

Nikhil Ravi, Anna Scaglione, Sachin Kadam, Reinhard Gentz, Sean Peisert, Brent Lunghino, Emmanuel Levijarvi, Aram Shumavon, Differentially Private K-means Clustering Applied to Meter Data Analysis and Synthesis, arXiv preprint arXiv:2112.03801, November 23, 2021,

Nikhil Ravi, Anna Scaglione, Sean Peisert, Colored Noise Mechanism for Differentially Private Clustering, arXiv preprint arXiv:2111.07850, November 15, 2021,

Y. Ma, F. Rusu, K. Wu, A. Sim, Adaptive Elastic Training for Sparse Deep Learning on Heterogeneous Multi-GPU Servers, arXiv preprint arXiv:2110.07029, 2021,

Yize Chen, Daniel Arnold, Yuanyuan Shi, Sean Peisert, Understanding the Safety Requirements for Learning-based Power Systems Operations, arXiv preprint arXiv:2110.04983, October 11, 2021,

C Varadharajan, Z Kakalia, E Alper, EL Brodie, M Burrus, RWH Carroll, D Christianson, W Dong, V Hendrix, M Henderson, S Hubbard, D Johnson, R Versteeg, KH Williams, DA Agarwal, The Colorado East River Community Observatory Data Collection, Hydrological Processes 35(6), 2021, doi: 10.22541/au.161962485.54378235/v1

J. Kim, A. Sim, J. Kim, K, Wu, J. Hahm, Improving Botnet Detection with Recurrent Neural Network and Transfer Learning, arXiv preprint arXiv:2104.12602, 2021,

Ed Younis, Koushik Sen, Katherine Yelick, Costin Iancu, QFAST: Quantum Synthesis Using a Hierarchical Continuous Circuit Space, Bulletin of the American Physical Society, March 2021,

We present QFAST, a quantum synthesis tool designed to produce short circuits and to scale well in practice. Our contributions are: 1) a novel representation of circuits able to encode placement and topology; 2) a hierarchical approach with an iterative refinement formulation that combines "coarse-grained" fast optimization during circuit structure search with a good, but slower, optimization stage only in the final circuit instantiation. When compared against state-of-the-art techniques, although not always optimal, QFAST can reduce circuits for "time-dependent evolution" algorithms, as used by domain scientists, by 60x in depth. On typical circuits, it provides 4x better depth reduction than the widely used Qiskit and UniversalQ compilers. We also show the composability and tunability of our formulation in terms of circuit depth and running time. For example, we show how to generate shorter circuits by plugging in the best available third party synthesis algorithm at a given hierarchy level. Composability enables portability across chip architectures, which is missing from similar approaches.
QFAST is integrated with Qiskit and available at github.com/bqskit.

Akel Hashim, Ravi Naik, Alexis Morvan, Jean-Loup Ville, Brad Mitchell, John Mark Kreikebaum, Marc Davis, Ethan Smith, Costin Iancu, Kevin O Brien, Ian Hincks, Joel Wallman, Joseph V Emerson, David Ivan Santiago, Irfan Siddiqi, Scalable Quantum Computing on a Noisy Superconducting Quantum Processor via Randomized Compiling, Bulletin of the American Physical Society, 2021,

Coherent errors in quantum hardware severely limit the performance of quantum algorithms in an unpredictable manner, and mitigating their impact is necessary for realizing reliable, large-scale quantum computations. Randomized compiling achieves this goal by converting coherent errors into stochastic noise, dramatically reducing unpredictable errors in quantum algorithms and enabling accurate predictions of aggregate performance via cycle benchmarking estimates. In this work, we demonstrate significant performance gains under randomized compiling for both the four-qubit quantum Fourier transform algorithm and for random circuits of variable depth on a superconducting quantum processor. We also validate solution accuracy using experimentally-measured error rates. Our results demonstrate that randomized compiling can be utilized to maximally-leverage and predict the capabilities of modern-day noisy quantum processors, paving the way forward for scalable quantum computing.

G Guidi, M Ellis, A Buluç, KA Yelick, DE Culler, 10 Years Later: Cloud Computing is Closing the Performance Gap., ICPE (Companion), Pages: 41--48 2021,

E Younis, K Sen, K Yelick, C Iancu, QFAST: Conflating Search and Numerical Optimization for Scalable Quantum Circuit Synthesis, Proceedings - 2021 IEEE International Conference on Quantum Computing and Engineering, QCE 2021, Pages: 232--243 2021, doi: 10.1109/QCE52317.2021.00041

M Ellis, A Buluç, K Yelick, Asynchrony versus bulk-synchrony for a generalized N-body problem from genomics, Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP, Pages: 465--466 2021, doi: 10.1145/3437801.3441580

I Nisa, P Pandey, M Ellis, L Oliker, A Buluc, K Yelick, Distributed-memory k-mer counting on GPUs, Proceedings - 2021 IEEE 35th International Parallel and Distributed Processing Symposium, IPDPS 2021, Pages: 527--536 2021, doi: 10.1109/IPDPS49936.2021.00061

G Blelloch, W Dally, M Martonosi, U Vishkin, K Yelick, SPAA 21 panel paper: Architecture-friendly algorithms versus algorithm-friendly architectures, Annual ACM Symposium on Parallelism in Algorithms and Architectures, Pages: 1--7 2021, doi: 10.1145/3409964.3461780

M Norman, V Kellen, S Smallen, B Demeulle, S Strande, E Lazowska, N Alterman, R Fatland, S Stone, A Tan, K Yelick, E Van Dusen, J Mitchell, CloudBank: Managed Services to Simplify Cloud Access for Computer Science Research and Education, ACM International Conference Proceeding Series, 2021, doi: 10.1145/3437359.3465586

M Ellis, A Buluc, K Yelick, Scaling Generalized N-Body Problems, A Case Study from Genomics, ACM International Conference Proceeding Series, 2021, doi: 10.1145/3472456.3472517

Jeremy Hewes, others, Graph Neural Network for Object Reconstruction in Liquid Argon Time Projection Chambers, EPJ Web Conf., Pages: 03054 2021, doi: 10.1051/epjconf/202125103054

Xiangyang Ju, others, Performance of a geometric deep learning pipeline for HL-LHC particle tracking, Eur. Phys. J. C, Pages: 876 2021, doi: 10.1140/epjc/s10052-021-09675-8

Sabrina Amrouche, others, The Tracking Machine Learning challenge : Throughput phase, 2021,

JE Damerow, C Varadharajan, K Boye, EL Brodie, M Burrus, KD Chadwick, R Crystal-Ornelas, H Elbashandy, RJ Eloy Alves, KS Ely, AE Goldman, T Haberman, V Hendrix, Z Kakalia, KM Kemner, AB Kersting, N Merino, F O Brien, Z Perzan, E Robles, P Sorensen, JC Stegen, RL Walls, P Weisenhorn, M Zavarin, D Agarwal, Sample identifiers and metadata to support data management and reuse in multidisciplinary ecosystem sciences, Data Science Journal, 2021, doi: 10.5334/dsj-2021-011

R Crystal-Ornelas, C Varadharajan, B Bond-Lamberty, K Boye, M Burrus, S Cholia, M Crow, J Damerow, R Devarakonda, KS Ely, A Goldman, S Heinz, V Hendrix, Z Kakalia, SC Pennington, E Robles, A Rogers, M Simmonds, T Velliquette, H Weierbach, P Weisenhorn, JN Welch, DA Agarwal, A Guide to Using GitHub for Developing and Versioning Data Standards and Reporting Formats, Earth and Space Science, 2021, doi: 10.1029/2021EA001797

G Puglisi, R Keskitalo, T Kisner, JD Borrill, Simulating Calibration and Beam Systematics for a Future CMB Space Mission with the TOAST Package, Research Notes of the AAS, Pages: 137--137 2021, doi: 10.3847/2515-5172/ac0823

M Tristram, AJ Banday, KM Górski, R Keskitalo, CR Lawrence, KJ Andersen, RB Barreiro, J Borrill, LPL Colombo, HK Eriksen, R Fernandez-Cobos, TS Kisner, E Martínez-González, B Partridge, D Scott, TL Svalheim, IK Wehus, Improved limits on the tensor-to-scalar ratio using BICEP and Planck, 2021,

Abigail Polin, Peter Nugent, Daniel Kasen, Nebular Models of Sub-Chandrasekhar Mass Type Ia Supernovae: Clues to the Origin of Ca-rich Transients, Astrophysical Journal, Pages: 65 2021, doi: 10.3847/1538-4357/abcccc

C. Frohmaier, C. R. Angus, M. Vincenzi, M. Sullivan, M. Smith, P. E. Nugent, S. B. Cenko, A. Gal-Yam, S. R. Kulkarni, N. M. Law, R. M. Quimby, From core collapse to superluminous: the rates of massive stellar explosions from the Palomar Transient Factory, Monthly Notices of the RAS, Pages: 5142-5158 2021, doi: 10.1093/mnras/staa3607

S. Yang, J. Sollerman, T. -W. Chen, E. C. Kool, R. Lunnan, S. Schulze, N. Strotjohann, A. Horesh, M. Kasliwal, T. Kupfer, A. A. Mahabal, F. J. Masci, P. Nugent, D. A. Perley, R. Riddle, B. Rusholme, Y. Sharma, Is supernova SN 2020faa an iPTF14hls look-alike?, Astronomy and Astrophysics, Pages: A22 2021, doi: 10.1051/0004-6361/202039440

Nora L. Strotjohann, Eran O. Ofek, Avishay Gal-Yam, Rachel Bruch, Steve Schulze, Nir Shaviv, Jesper Sollerman, Alexei V. Filippenko, Ofer Yaron, Christoffer Fremling, Jakob Nordin, Erik C. Kool, Dan A. Perley, Anna Y. Q. Ho, Yi Yang, Yuhan Yao, Maayane T. Soumagnac, Melissa L. Graham, Cristina Barbarino, Leonardo Tartaglia, Kishalay De, Daniel A. Goldstein, David O. Cook, Thomas G. Brink, Kirsty Taggart, Lin Yan, Ragnhild Lunnan, Mansi Kasliwal, Shri R. Kulkarni, Peter E. Nugent, Frank J. Masci, Philippe Rosnet, Scott M. Adams, Igor Andreoni, Ashot Bagdasaryan, Eric C. Bellm, Kevin Burdge, Dmitry A. Duev, Alison Dugas, Sara Frederick, Samantha Goldwasser, Matthew Hankins, Ido Irani, Viraj Karambelkar, Thomas Kupfer, Jingyi Liang, James D. Neill, Michael Porter, Reed L. Riddle, Yashvi Sharma, Phil Short, Francesco Taddia, Anastasios Tzanidakis, Jan van Roestel, Richard Walters, Zhuyun Zhuang, Bright, Months-long Stellar Outbursts Announce the Explosion of Interaction-powered Supernovae, Astrophysical Journal, Pages: 99 2021, doi: 10.3847/1538-4357/abd032

J. Johansson, A. Goobar, S. H. Price, A. Sagu\ es Carracedo, L. Della Bruna, P. E. Nugent, S. Dhawan, E. M\ ortsell, S. Papadogiannakis, R. Amanullah, D. Goldstein, S. B. Cenko, K. De, A. Dugas, M. M. Kasliwal, S. R. Kulkarni, R. Lunnan, Spectroscopy of the first resolved strongly lensed Type Ia supernova iPTF16geu, Monthly Notices of the RAS, Pages: 510-520 2021, doi: 10.1093/mnras/staa3829

Chelsea E. Harris, Laura Chomiuk, Peter. E. Nugent, Tumbling Dice: Radio Constraints on the Presence of Circumstellar Shells around Type Ia Supernovae with Impact Near Maximum Light, Astrophysical Journal, Pages: 23 2021, doi: 10.3847/1538-4357/abe940

Charlotte Ward, Suvi Gezari, Sara Frederick, Erica Hammerstein, Peter Nugent, Sjoert van Velzen, Andrew Drake, Abigail Garc\ \ia-P\ erez, Immaculate Oyoo, Eric C. Bellm, Dmitry A. Duev, Matthew J. Graham, Mansi M. Kasliwal, Stephen Kaye, Ashish A. Mahabal, Frank J. Masci, Ben Rusholme, Maayane T. Soumagnac, Lin Yan, AGNs on the Move: A Search for Off-nuclear AGNs from Recoiling Supermassive Black Holes and Ongoing Galaxy Mergers with the Zwicky Transient Facility, Astrophysical Journal, Pages: 102 2021, doi: 10.3847/1538-4357/abf246

Michael S. Medford, Peter Nugent, Danny Goldstein, Frank J. Masci, Igor Andreoni, Ron Beck, Michael W. Coughlin, Dmitry A. Duev, Ashish A. Mahabal, Reed L. Riddle, Removing Atmospheric Fringes from Zwicky Transient Facility i-band Images using Principal Component Analysis, Publications of the ASP, Pages: 064503 2021, doi: 10.1088/1538-3873/abfe9d

Steve Schulze, Ofer Yaron, Jesper Sollerman, Giorgos Leloudas, Amit Gal, Angus H. Wright, Ragnhild Lunnan, Avishay Gal-Yam, Eran O. Ofek, Daniel A. Perley, Alexei V. Filippenko, Mansi M. Kasliwal, Shrinivas R. Kulkarni, James D. Neill, Peter E. Nugent, Robert M. Quimby, Mark Sullivan, Nora Linn Strotjohann, Iair Arcavi, Sagi Ben-Ami, Federica Bianco, Joshua S. Bloom, Kishalay De, Morgan Fraser, Christoffer U. Fremling, Assaf Horesh, Joel Johansson, Patrick L. Kelly, Nikola Kne\vzevi\ c, Sladjana Kne\vzevi\ c, Kate Maguire, Anders Nyholm, Sem\ eli Papadogiannakis, Tanja Petrushevska, Adam Rubin, Lin Yan, Yi Yang, Scott M. Adams, Filomena Bufano, Kelsey I. Clubb, Ryan J. Foley, Yoav Green, Jussi Harmanen, Anna Y. Q. Ho, Isobel M. Hook, Griffin Hosseinzadeh, D. Andrew Howell, Albert K. H. Kong, Rubina Kotak, Thomas Matheson, Curtis McCully, Dan Milisavljevic, Yen-Chen Pan, Dovi Poznanski, Isaac Shivvers, Sjoert van Velzen, Kars K. Verbeek, The Palomar Transient Factory Core-collapse Supernova Host-galaxy Sample. I. Host-galaxy Distribution Functions and Environment Dependence of Core-collapse Supernovae, Astrophysical Journal Supplement, Pages: 29 2021, doi: 10.3847/1538-4365/abff5e

C. Ashall, J. Lu, E. Y. Hsiao, P. Hoeflich, M. M. Phillips, L. Galbany, C. R. Burns, C. Contreras, K. Krisciunas, N. Morrell, M. D. Stritzinger, N. B. Suntzeff, F. Taddia, J. Anais, E. Baron, P. J. Brown, L. Busta, A. Campillay, S. Castell\ on, C. Corco, S. Davis, G. Folatelli, F. F\ orster, W. L. Freedman, C. Gonzal\ ez, M. Hamuy, S. Holmbo, R. P. Kirshner, S. Kumar, G. H. Marion, P. Mazzali, T. Morokuma, P. E. Nugent, S. E. Persson, A. L. Piro, M. Roth, F. Salgado, D. J. Sand, J. Seron, M. Shahbandeh, B. J. Shappee, Carnegie Supernova Project: The First Homogeneous Sample of Super-Chandrasekhar-mass/2003fg-like Type Ia Supernovae, Astrophysical Journal, Pages: 205 2021, doi: 10.3847/1538-4357/ac19ac

J. Johansson, S. B. Cenko, O. D. Fox, S. Dhawan, A. Goobar, V. Stanishev, N. Butler, W. H. Lee, A. M. Watson, U. C. Fremling, M. M. Kasliwal, P. E. Nugent, T. Petrushevska, J. Sollerman, L. Yan, J. Burke, G. Hosseinzadeh, D. A. Howell, C. McCully, S. Valenti, Near-infrared Supernova Ia Distances: Host Galaxy Extinction and Mass-step Corrections Revisited, Astrophysical Journal, Pages: 237 2021, doi: 10.3847/1538-4357/ac2f9e

J Müller, B Faybishenko, D Agarwal, S Bailey, C Jiang, Y Ryu, C Tull, L Ramakrishnan, Assessing data change in scientific datasets, Concurrency and Computation: Practice and Experience, 2021, doi: 10.1002/cpe.6245

K Yelick, D Agarwal, D Bard, J Shalf, A Almgren, W Bhimji, B Brown, J Carter, B Jong, D Doerfler, D Donofrio, C Guok, C Iancu, M Kiran, S Li, P Nugent, M Prabhat, L Ramakrishnan, D Vasudevan, N Wright, H Cademartori, K Antypas, K Kincade, 2019 Computing Sciences Strategic Plan, 2021, doi: 10.2172/1827673

SL Brantley, T Wen, DA Agarwal, JG Catalano, PA Schroeder, K Lehnert, C Varadharajan, J Pett-Ridge, M Engle, AM Castronova, RP Hooper, X Ma, L Jin, K McHenry, E Aronson, AR Shaughnessy, LA Derry, J Richardson, J Bales, EM Pierce, The future low-temperature geochemical data-scape as envisioned by the U.S. geochemical community, Computers and Geosciences, 2021, doi: 10.1016/j.cageo.2021.104933

Ankur K. Gupta, Benjamin C. Gamoke, Krishnan Raghavachari, Interaction–Deletion: A Composite Energy Method for the Optimization of Molecular Systems Selectively Removing Specific Nonbonded Interactions, The Journal of Physical Chemistry A, Pages: 4668-4682 2021, doi: 10.1021/acs.jpca.1c02918

Brad Mitchell, Ravi Naik, Alexis Morvan, Akel Hashim, John Mark Kreikebaum, David Santiago, Irfan Siddiqi, Calibration of the Cross-Resonance Gate using Closed-Loop Optimal Control, Bulletin of the American Physical Society, 2021,

Gerwin Koolstra, Noah Stevenson, Karthik Siva, William Livingston, Ravi Naik, John Steinmetz, Debmalya Das, Andrew Jordan, David Santiago, Irfan Siddiqi, Diagnosing Gate Errors in Superconducting Qubits Using Continuous Measurements (Experiment), Bulletin of the American Physical Society, 2021,

Ravi Naik, Brad Mitchell, Akel Hashim, John Mark Kreikebaum, David Santiago, Irfan Siddiqi, Contextual Characterization of the Cross-Resonance Gate on a Multi-Qubit Superconducting Quantum Processor, Bulletin of the American Physical Society, 2021,

Robin Blume-Kohout, Susan Clark, Akel Hashim, Craig Hogle, Daniel Lobser, Ravi Naik, Timothy Proctor, Kenneth Rudinger, David Santiago, Irfan Siddiqi, others, Simultaneous Gate Set Tomography, Bulletin of the American Physical Society, 2021,

Joachim Cohen, Agustin Di Paolo, Larry Chen, Trevor Chistolini, John Mark Kreikebaum, Long Nguyen, Ravi Naik, David Santiago, Irfan Siddiqi, Alexandre Blais, Novel two-qubit gates for the light fluxonium qubit, Bulletin of the American Physical Society, 2021,

Alexis Morvan, Vinay Ramasesh, Machiel Blok, John Mark Kreikebaum, Kevin O Brien, Larry Chen, Ravi Naik, Brad Mitchell, David Santiago, Irfan Siddiqi, Qutrit Randomized Benchmarking on a Transmon Quantum Processor, Bulletin of the American Physical Society, 2021,

John Steinmetz, Debmalya Das, Gerwin Koolstra, Noah Stevenson, Karthik Siva, William Livingston, Ravi Naik, David Santiago, Irfan Siddiqi, Andrew Jordan, Diagnosing Errors in Qubit Gates Using Continuous Measurements (Theory), Bulletin of the American Physical Society, 2021,

Noah Stevenson, Gerwin Koolstra, Karthik Siva, Ravi Naik, William Livingston, Shiva Lotfallahzadeh Barzili, Justin Dressel, Irfan Siddiqi, Tracking Non-Markovian Quantum Trajectories of a Superconducting Qubit from a Finite-Memory Bath, Bulletin of the American Physical Society, 2021,

Yilun Xu, Gang Huang, Jan Balewski, Ravi K Naik, Alexis Morvan, Brad Mitchell, Kasra Nowrouzi, David I Santiago, Irfan Siddiqi, Automatic Qubit Characterization and Gate Optimization with QubiC, arXiv preprint arXiv:2104.10866, 2021,

Yilun Xu, Gang Huang, Ravi Naik, Alexis Morvan, Kasra Nowrouzi, Brad Mitchell, David Santiago, Irfan Siddiqi, Automatic two-qubit gate calibration with qubic, Bulletin of the American Physical Society, 2021,

Kevin He, Srivatsan Chakram, Akash Dixit, Andrew Oriani, Ravi Naik, Nelson Leung, Hyeokshin Kwon, Riju Banerjee, Wen-Long Ma, Liang Jiang, others, State preparation and tomography in 3D multimode circuit QED, Bulletin of the American Physical Society, 2021,

Jean-Loup Ville, Alexis Morvan, Akel Hashim, Ravi K Naik, Bradley Mitchell, John-Mark Kreikebaum, Kevin P O Brien, Joel J Wallman, Ian Hincks, Joseph Emerson, others, Leveraging Randomized Compiling for the QITE Algorithm, arXiv preprint arXiv:2104.08785, 2021,

David Schuster, Ravi Naik, Srivatsan Chakram, Technologies for long-lived 3d multimode microwave cavities, 2021,