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2019 Publications

2020

B. Weinger, J. Kim, A. Sim, M. Nakashima, N. Moustafa, K. Wu, "Enhancing IoT Anomaly Detection Performance for Federated Learning", The 16th IEEE International Conference on Mobility, Sensing and Networking (IEEE MSN 2020), 2020,

J. Kim, A. Sim, J. Kim, K. Wu, "Botnets Detection Using Recurrent Variational Autoencoder", IEEE Global Communications Conference (Globecom 2020), 2020,

I. Monga, C. Guok, J. MacAuley, A. Sim, H. Newman, J. Balcas, P. DeMar, L. Winkler, T. Lehman, X. Yang, "SDN for End-to-end Networked Science at the Exascale", Future Generation Computer Systems, 2020, doi: 10.1016/j.future.2020.04.018

Brett Weinger, Alex Sim (Advisor), John Wu (Advisor), Jinoh Kim (Advisor), "Enhancing IoT Anomaly Detection Performance for Federated Learning", International Conference for High Performance Computing, Networking, Storage and Analysis (SC’20), ACM Student Research Competition (SRC), 2020,

Ignacio Losada Carreño, Raksha Ramakrishna, Anna Scaglione, Daniel Arnold, Ciaran Roberts, Sy-Toan Ngo, Sean Peisert, David Pinney, "SODA: An Irradiance-Based Tool to Generate Sub-Minute Solar Power Stochastic Time Series", Proceedings of the IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), IEEE, November 2020,

Ciaran Roberts Sy-Toan Ngo, Alexandre Milesi, Sean Peisert, Daniel Arnold, Shammya Saha, Anna Scaglione, Nathan Johnson, Anton Kocheturov, Dmitriy Fradkin, "Deep Reinforcement Learning for DER Cyber-Attack Mitigation", Proceedings of the IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), IEEE, November 2020,

Sun, S., Pattyn, F., Simon, E., Albrecht, T., Cornford, S., Calov, R., . . . Zhang, T., "Antarctic ice sheet response to sudden and sustained ice-shelf collapse (ABUMIP)", Journal of Glaciology, September 14, 2020, 1-14, doi: 10.1017/jog.2020.67

Drew Paine, Devarshi Ghoshal, Lavanya Ramakrishnan, "Experiences with a Flexible User Research Process to Build Data Change Tools", Journal of Open Research Software, September 1, 2020, doi: 10.5334/jors.284

Scientific software development processes are understood to be distinct from commercial software development practices due to uncertain and evolving states of scientific knowledge. Sustaining these software products is a recognized challenge, but under-examined is the usability and usefulness of such tools to their scientific end users. User research is a well-established set of techniques (e.g., interviews, mockups, usability tests) applied in commercial software projects to develop foundational, generative, and evaluative insights about products and the people who use them. Currently these approaches are not commonly applied and discussed in scientific software development work. The use of user research techniques in scientific environments can be challenging due to the nascent, fluid problem spaces of scientific work, varying scope of projects and their user communities, and funding/economic constraints on projects.

In this paper, we reflect on our experiences undertaking a multi-method user research process in the Deduce project. The Deduce project is investigating data change to develop metrics, methods, and tools that will help scientists make decisions around data change. There is a lack of common terminology since the concept of systematically measuring and managing data change is under explored in scientific environments. To bridge this gap we conducted user research that focuses on user practices, needs, and motivations to help us design and develop metrics and tools for data change. This paper contributes reflections and the lessons we have learned from our experiences. We offer key takeaways for scientific software project teams to effectively and flexibly incorporate similar processes into their projects.

Oguz Selvitopi*, Saliya Ekanayake*, Giulia Guidi, Georgios Pavlopoulos, Ariful Azad, Aydın Buluç, "Distributed Many-to-Many Protein Sequence Alignment Using Sparse Matrices", Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC’20)., 2020,

(*:joint first authors)

Drew Paine, Devarshi Ghoshal, Lavanya Ramakrishnan, "Investigating Scientific Data Change with User Research Methods", August 20, 2020, LBNL LBNL-2001347,

Scientific datasets are continually expanding and changing due to fluctuations with instruments, quality assessment and quality control processes, and modifications to software pipelines. Datasets include minimal information about these changes or their effects requiring scientists manually assess modifications through a number of labor intensive and ad-hoc steps. The Deduce project is investigating data change to develop metrics, methods, and tools that will help scientists systematically identify and make decisions around data changes. Currently, there is a lack of understanding, and common practices, for identifying and evaluating changes in datasets since systematically measuring and managing data change is under explored in scientific work. We are conducting user research to address this need by exploring scientist's conceptualizations, behaviors, needs, and motivations when dealing with changing datasets. Our user research utilizes multiple methods to produce foundational, generative insights and evaluate research products produced by our team. In this paper, we detail our user research process and outline our findings about data change that emerge from our studies. Our work illustrates how scientific software teams can push beyond just usability testing user interfaces or tools to better probe the underlying ideas they are developing solutions to address.

Sean Peisert, Isolating Insecurely: A Call to Arms for the Security and Privacy Community During the Time of COVID-19, IEEE Security and Privacy, Pages: 4-7 August 2020, doi: 10.1109/MSEC.2020.2992316

Gustavo Chavez, Elizaveta Rebrova, Yang Liu, Pieter Ghysels, Xiaoye Sherry Li, "Scalable and memory-efficient kernel ridge regression", 34th IEEE International Parallel and Distributed Processing Symposium, July 14, 2020,

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", Submitted to Journal of Chemical Physics, July 7, 2020,

Yang Liu, Eric Michielssen, "Parallel fast time-domain integral-equation methods for transient electromagnetic analysis", Parallel Algorithms in Computational Science and Engineering, ( July 7, 2020)

Leen Alawieh, Jonathan Goodman, John B. Bell, "Iterative construction of Gaussian process surrogate models for Bayesian inference", Journal of Statistical Planning and Inference, 2020,

Yang Liu, Pieter Ghysels, Lisa Claus, Xiaoye Sherry Li, "Sparse Approximate Multifrontal Factorization with Butterfly Compression for High Frequency Wave Equations", arxiv-preprint, July 1, 2020,

S. Kim, A. Sim, K. Wu, S. Byna, Y. Son, H. Eom, "Towards HPC I/O performance prediction through large-scale log analysis", ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC 2020), 2020, doi: 10.1145/3369583.3392678

Nan Ding, Victor W. Lee, Wei Xue, Weimin Zheng, "APMT: an automatic hardware counter-based performance modeling tool for HPC applications", CCF Transactions on High Performance Computing, June 24, 2020,

J. Kim, A. Sim, J. Kim, K. Wu, J. Hahm, "Transfer Learning Approach for Botnet Detection based on Recurrent Variational Autoencoder", the 3rd ACM International Workshop on System and Network Telemetry and Analysis (SNTA) 2020, in conjunction with The 29th ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC), 2020, doi: 10.1145/3391812.3396273

M. Nakashima, A. Sim, J. Kim, "Evaluation of Deep Learning Models for Network PerformancePrediction for Scientific Facilities", the 3rd ACM International Workshop on System and Network Telemetry and Analysis (SNTA) 2020, in conjunction with The 29th ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC), 2020, doi: 10.1145/3391812.3396272

J. Bang, C. Kim, K. Wu, A. Sim, S. Byna, S. Kim, H. Eom, "HPC Workload Characterization using Feature Selection and Clustering", the 3rd ACM International Workshop on System and Network Telemetry and Analysis (SNTA) 2020, in conjunction with The 29th ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC), 2020, doi: 10.1145/3391812.3396270

S. Bhandari, A. K. Kukreja, A. Lazar, A. Sim, K. Wu, "Feature Selection and Tree-based Classification for Wireless Intrusion Detection", the 3rd ACM International Workshop on System and Network Telemetry and Analysis (SNTA) 2020, in conjunction with The 29th ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC), 2020, doi: 10.1145/3391812.3396274

G. R. Ghosal, D. Ghosal, A. Sim, A. V. Thakur, K. Wu, "A Deep Deterministic Policy Gradient Based Network Scheduler For Deadline-Driven Data Transfers", International Federation for Information Processing (IFIP) Networking Conference (NETWORKING 2020), 2020,

E. Motheau, J. Wakefield, "Investigation of finite-volume methods to capture shocks and turbulence spectra in compressible flows", Commun. in Appl. Math. and Comput. Sci, 15-1 (2020), 1--36, June 3, 2020,

Jonathan R Madsen, Muaaz G Awan, Hugo Brunie, Jack Deslippe, Rahul Gayatri, Leonid Oliker, Yunsong Wang, Charlene Yang, Samuel Williams, "TiMemory: Modular Performance Analysis for HPC", International Supercomputing Conference (ISC), June 2020,

Amir Kamil, John Bachan, Scott B. Baden, Dan Bonachea, Rob Egan, Paul Hargrove, Steven Hofmeyr, Mathias Jacquelin, Kathy Yelick, UPC++: An Asynchronous RMA/RPC Library for Distributed C++ Applications, Argonne Leadership Computing Facility (ALCF) Webinar Series, May 27, 2020,

UPC++ is a C++ library providing classes and functions that support Partitioned Global Address Space (PGAS) programming. The UPC++ API offers low-overhead one-sided RMA communication and Remote Procedure Calls (RPC), along with futures and promises. These constructs enable the programmer to express dependencies between asynchronous computations and data movement. UPC++ supports the implementation of simple, regular data structures as well as more elaborate distributed data structures where communication is fine-grained, irregular, or both. The library’s support for asynchrony enables the application to aggressively overlap and schedule communication and computation to reduce wait times.

UPC++ is highly portable and runs on platforms from laptops to supercomputers, with native implementations for HPC interconnects. As a C++ library, it interoperates smoothly with existing numerical libraries and on-node programming models (e.g., OpenMP, CUDA).

In this webinar, hosted by DOE’s Exascale Computing Project and the ALCF, we will introduce basic concepts and advanced optimization techniques of UPC++. We will discuss the UPC++ memory and execution models and walk through basic algorithm implementations. We will also look at irregular applications and show how they can take advantage of UPC++ features to optimize their performance.

Event page

Bin Dong, Veronica Rodriguez Tribaldos, Xin Xing, Suren Byna, Jonathan Ajo-Franklin, and Kesheng Wu, "DASSA: Parallel DAS Data Storage and Analysis for Subsurface Event Detection", IPDPS, May 15, 2020,

Q. Kang, A. Sim, P. Nugent, S. Lee, W.K. Liao, A, Agrawal, A. Choudhary, K. Wu, "Predicting Resource Requirement in Intermediate Palomar Transient Factory Workflow", the 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2020), 2020, doi: 10.1109/CCGrid49817.2020.00-31

H. Sung, J. Bang, C. Kim, H. Kim, A. Sim, G. K. Lockwood, H. Eom, "BBOS: Efficient HPC Storage Management via Burst Buffer Over-Subscription", the 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2020), 2020, doi: 10.1109/CCGrid49817.2020.00-79

Shao-Jun Dong, Chao Wang, Yong-Jian Han, Chao Yang and Lixin He, "Stable diagonal stripes in the t–J model at nhbar = 1/8 doping from fPEPS calculations", npj Quantum Materials, May 8, 2020, 5:28, doi: https://doi.org/10.1038/s41535-020-0226-4

S. B. Kachuck, D. F. Martin, J. N. Bassis, S. F. Price, "Rapid viscoelastic deformation slows marine ice sheet instability at Pine Island Glacier", Geophysical Research Letters, May 7, 2020, 47, doi: 10.1029/2019GL086446

S. B. Kachuck, D. F. Martin, J. N. Bassis, S. F. Price, "Rapid viscoelastic deformation slows marine ice sheet instability at Pine Island Glacier", Geophysical Research Letters, May 7, 2020, 47, doi: 10.1029/2019GL086446

Bogdan Copos, Sean Peisert, "Catch Me If You Can: Using Power Analysis to Identify HPC Activity", arXiv:2005.03135 [cs.CR], May 6, 2020,

Daniel F. Martin, Stephen L. Cornford, Esmond G Ng, Effect of Improved Bedrock Geometry on Antarctic Vulnerability to Regional Ice Shelf Collapse, European Geosciences Union 2020 General Assembly, May 5, 2020,

Andrew Wells, James Parkinson, Daniel F Martin, Three-dimensional convection, phase change, and solute transport in mushy sea ice, European Geosciences Union 2020 General Assembly,, May 4, 2020,

C. T. Kelley, J. Bernholc, E. L. Briggs, S. Hamilton, L. Lin and C. Yang, "Mesh Independence of the Generalized Davidson Algorithm", Journal of Computational Physics, May 1, 2020, 409:109322, doi: https://doi.org/10.1016/j.jcp.2020.109322

H. Masia-Roig, J. A. Smiga, D. Budker, V. Dumont, Z. Grujic, D. Kim, D. F. Jackson Kimball, V. Lebedev, M. Monroy, S. Pustelny, T. Scholtes, P. C. Segura, Y. K. Semertzidis, Y. Chang Shin, J. E. Stalnaker, I. Sulai, A. Weis, A. Wickenbrock, "Analysis method for detecting topological defect dark matter with a global magnetometer network", Physics of the Dark Universe, Volume 28, 100494, May 2020, doi: 10.1016/j.dark.2020.100494

Alberto Zeni, Giulia Guidi, Marquita Ellis, Nan Ding, Marco D. Santambrogio, Steven Hofmeyr, Aydın Buluç, Leonid Oliker, Katherine Yelick, "LOGAN: High-Performance GPU-Based X-Drop Long-Read Alignment", 34th IEEE International Parallel and Distributed Processing Symposium (IPDPS20), 2020,

Kai-Hsin Liou, Chao Yang and James R.Chelikowsky, "Scalable Implementation of Polynomial Filering for Density Functional Theory Calculation in PARSEC", Computer Physics Communications, April 28, 2020, In press, doi: https://doi.org/10.1016/j.cpc.2020.107330

M. R. Wilczynska, J. K. Webb, M. Bainbridge, S. E. I. Bosman, J. D. Barrow, R. F. Carswell, M. P. Dabrowski, V. Dumont, A. C. Leite, C. Lee, K. Leszczynska, J. Liske, K. Marosek, C. J. A. P. Martins, D. Milakovic, P. Molaro, L. Pasquini, "Four direct measurements of the fine-structure constant 13 billion years ago", Science Advances, Volume 6, No. 17, eaay9672, April 24, 2020, doi: 10.1126/sciadv.aay9672

Christine T. Wolf, Drew Paine, "Sensemaking Practices in the Everyday Work of AI/ML Software Engineering", IEEE/ACM 42nd International Conference on Software Engineering Workshops (ICSEW’20), ACM, April 5, 2020, doi: 10.1145/3387940.3391496

Li Zhou, Chao Yang, Weiguo Gao, Talita Perciano, Karen M. Davies, Nicholas K. Sauter, "Subcellular structure segmentation from cryo-electron tomograms via machine learning", PLOS Journal of Computational Biology, April 2, 2020, submitte, doi: doi: https://doi.org/10.1101/2020.04.09.034025

Georgios Tzimpragos, Dilip Vasudevan, Nestan Tsiskaridze, George Michelogiannakis, Advait Madhavan, Jennifer Volk, John Shalf, Timothy Sherwood, "A Computational Temporal Logic for Superconducting Accelerators", ASPLOS '20: Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems, March 2020,

F. Henneke, L. Lin, C. Vorwerk, C. Draxl, R. Klein and C. Yang, "Fast optical absorption spectra calculations for periodic solid state systems", Communications in Applied Mathematics and Computational Science, March 16, 2020, in press,

John Bachan, Scott B. Baden, Dan Bonachea, Max Grossman, Paul H. Hargrove, Steven Hofmeyr, Mathias Jacquelin, Amir Kamil, Brian van Straalen, "UPC++ v1.0 Programmer’s Guide, Revision 2020.3.0", Lawrence Berkeley National Laboratory Tech Report, March 2020, LBNL 2001269, doi: 10.25344/S4P88Z

UPC++ is a C++11 library that provides Partitioned Global Address Space (PGAS) programming. It is designed for writing parallel programs that run efficiently and scale well on distributed-memory parallel computers. The PGAS model is single program, multiple-data (SPMD), with each separate constituent process having access to local memory as it would in C++. However, PGAS also provides access to a global address space, which is allocated in shared segments that are distributed over the processes. UPC++ provides numerous methods for accessing and using global memory. In UPC++, all operations that access remote memory are explicit, which encourages programmers to be aware of the cost of communication and data movement. Moreover, all remote-memory access operations are by default asynchronous, to enable programmers to write code that scales well even on hundreds of thousands of cores.

John Bachan, Dan Bonachea, Amir Kamil, "UPC++ v1.0 Specification, Revision 2020.3.0", Lawrence Berkeley National Laboratory Tech Report, March 12, 2020, LBNL 2001268, doi: 10.25344/S4T01S

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.

D. Camps, R. Van Beeumen and C. Yang, "Quantum Fourier Transform Revisited", March 6, 2020,

Muammar El Khatib, Wibe De Jong, Feature Extraction Using Semi-Supervised Deep Learning., APS March 2020, March 5, 2020,

Features are defined as measurable properties that characterize observed phenomena and represent a key part of machine learning (ML) algorithms. In materials sciences, ML has successfully accelerated atomistic simulations using man-engineered features for tasks such as energy or atomic forces predictions. These features fulfill physics constraints such as rotational and translational invariance, uniqueness and, locality (the sum of local contributions reconstructs a global quantity). However, these ML models are known to perform poorly when operating out of the training set regime because features are not representative of the underlying structure of the data. This could be improved if features are extracted with advanced hybrid architectures e.g. a variational autoencoder that is trained with physics constraints introduced with an external task and a loss function. We will explore how the use of semi-supervised learning techniques can be a powerful tool for the extraction of features for atomistic simulations. All results shown herein can be reproduced with ML4Chem: a free software package for machine learning in chemistry and materials sciences.

Ross Gegan, Christina Mao, Dipak Ghosal, Matt Bishop, Sean Peisert, "Anomaly Detection for Science DMZ Using System Performance Data", Proceedings of the 2020 IEEE International Conference on Computing, Networking and Communications (ICNC 2020), Big Island, HI, February 2020, doi: 10.1109/ICNC47757.2020.9049695

Nan Ding, Samuel Williams, Yang Liu, Xiaoye S. Li, "Leveraging One-Sided Communication for Sparse Triangular Solvers", 2020 SIAM Conference on Parallel Processing for Scientific Computing, February 14, 2020,

Levermann, A., Winkelmann, R., Albrecht, T., Goelzer, H., Golledge, N. R., Greve, R., Huybrechts, P., Jordan, J., Leguy, G., Martin, D., Morlighem, M., Pattyn, F., Pollard, D., Quiquet, A., Rodehacke, C., Seroussi, H., Sutter, J., Zhang, T., Van Breedam, J., Calov, R., DeConto, R., Dumas, C., Garbe, J., Gudmundsson, G. H., Hoffman, M. J., Humbert, A., Kleiner, T., Lipscomb, W. H., Meinshausen, M., Ng, E., Nowicki, S. M. J., Perego, M., Price, S. F., Saito, F., Schlegel, N.-J., Sun, S., van de Wal, R. S. W, "Projecting Antarctica’s contribution to future sea level rise from basal ice shelf melt using linear response functions of 16 ice sheet models (LARMIP-2)", Earth System Dynamics, February 14, 2020, 11:35–76, doi: 10.5194/esd-11-35-2020

Drew Paine, Charlotte P. Lee, "Coordinative Entities: Forms of Organizing in Data Intensive Science", Journal of Computer Supported Cooperative Work, February 11, 2020, doi: 10.1007/s10606-020-09372-2

Yang Liu, Xin Xing, Han Guo, Eric Michielssen, Pieter Ghysels, Xiaoye Sherry Li, "Butterfly factorization via randomized matrix-vector multiplications", arxiv e-preprint, February 9, 2020,

Revathi Jambunathan, Deborah Levin, "A Self-Consistent Open Boundary Condition for Fully Kinetic Plasma Thruster Plume Simulations", IEEE Transactions on Plasma Science, February 7, 2020, doi: 10.1109/TPS.2020.2968887

Amir Kamil, John Bachan, Scott B. Baden, Dan Bonachea, Rob Egan, Paul Hargrove, Steven Hofmeyr, Mathias Jacquelin, Kathy Yelick, UPC++: A PGAS/RPC Library for Asynchronous Exascale Communication in C++, Exascale Computing Project (ECP) Annual Meeting 2020, February 6, 2020,

UPC++ is a C++ library providing classes and functions that support Partitioned Global Address Space (PGAS) programming. The UPC++ API offers low-overhead one-sided RMA communication and Remote Procedure Calls (RPC), along with futures and promises. These constructs enable the programmer to express dependencies between asynchronous computations and data movement. UPC++ supports the implementation of simple, regular data structures as well as more elaborate distributed data structures where communication is fine-grained, irregular, or both. The library’s support for asynchrony enables the application to aggressively overlap and schedule communication and computation to reduce wait times.

UPC++ is highly portable and runs on platforms from laptops to supercomputers, with native implementations for HPC interconnects. As a C++ library, it interoperates smoothly with existing numerical libraries and on-node programming models (e.g., OpenMP, CUDA).

In this tutorial we will introduce basic concepts and advanced optimization techniques of UPC++. We will discuss the UPC++ memory and execution models and walk through basic algorithm implementations. We will also look at irregular applications and show how they can take advantage of UPC++ features to optimize their performance.

Event page

Brandon Runnels, Vinamra Agrawal, Weiqun Zhang, Ann Almgren, "Massively parallel finite difference elasticity using a block-structured adaptive mesh refinement with a geometric multigrid solver", submitted for publication, 2020,

Samuel Williams, Introduction to the Roofline Model, ECP Annual Meeting, February 2020,

Charlene Yang, Hierarchical Roofline Analysis on GPUs, ECP Annual Meeting, February 2020,

Samuel Williams, Roofline on GPUs (Advanced Topics), ECP Annual Meeting, February 2020,

Charlene Yang, Hierarchical Roofline Analysis on CPUs, ECP Annual Meeting, February 2020,

Jack Deslippe, Guiding Optimization with the Roofline Model, ECP Annual Meeting, February 2020,

Amir Kamil, John Bachan, Dan Bonachea, Paul H. Hargrove, Erich Strohmaier and Daniel Waters, "UPC++: Asynchronous RMA and RPC Communication for Exascale Applications", Poster at Exascale Computing Project (ECP) Annual Meeting 2020, February 2020,

Nicholas Z. Liu, Daniel R. Ladiges, Jason Nassios, John E. Sader, "Acoustic flows in a slightly rarefied gas", Physical Review Fluids, February 4, 2020, 5:043401,

Paul H. Hargrove, Dan Bonachea, "GASNet-EX: RMA and Active Message Communication for Exascale Programming Models", Poster at Exascale Computing Project (ECP) Annual Meeting 2020, February 2020,

Suren Byna, M. Scot Breitenfeld, Bin Dong, Quincey Koziol, Elena Pourmal, Dana Robinson, Jerome Soumagne, Houjun Tang, Venkatram Vishwanath, and Richard Warren, "ExaHDF5: Delivering Efficient Parallel I/O on Exascale Computing Systems", Journal of Computer Science and Technology 2020, 35(1): 145-160, February 2, 2020,

DOI: 10.1007/s11390-020-9822-9

review

Sergi Molins, Cyprien Soulaine, Nikolaos I. Prasianakis, Aida Abbasi, Philippe Poncet, Anthony J. C. Ladd, Vitalii Starchenko, Sophie Roman, David Trebotich, Hamdi Tchelepi, Carl I. Steefel, "Simulation of mineral dissolution at the pore scale with evolving fluid-solid interfaces: review of approaches and benchmark problem set", Computational Geosciences, January 23, 2020, doi: 10.1007/s10596-019-09903-x

F. Alexander, A. Almgren, J. Bell, A. Bhattacharjee, J. Chen, P. Colella, D. Daniel, J. DeSlippe, L. Diachin, E. Draeger, A. Dubey, T. Dunning, T. Evans, I. Foster, M. Francois, T. Germann, M. Gordon, S. Habib, M. Halappanavar, S. Hamilton, W. Hart, Z. Huang, A. Hungerford, D. Kasen, P. Kent, T. Kolev, D. Kothe, A. Kronfeld, Y. Luo, P. Mackenzie, D. McCallen, B. Messer, S. Mniszewski, C. Oehmen, A. Perazzo, D. Perez, D. Richard, W. Rider, R. Rieben, K. Roche, A. Siegel, M. Sprague, C. Steefel, R. Stevens, M. Syamlal, M. Taylor, J. Turner, J.-L. Vay, A. Voter, T. Windus and K. Yelick, "Exascale applications: skin in the game", Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2020,

Katherine Yelick, Aydın Buluç, Muaaz Awan, Ariful Azad, Benjamin Brock, Rob Egan, Saliya Ekanayake, Marquita Ellis, Evangelos Georganas, Giulia Guidi, Steven Hofmeyr, Oguz Selvitopi, Cristina Teodoropol, Leonid Oliker, "The parallelism motifs of genomic data analysis", Philosophical Transactions of The Royal Society A: Mathematical, Physical and Engineering Sciences, 2020,

L. Jin, A. Lazar, J. Sears, A. Todd, A. Sim, K. Wu, C. A. Spurlock, "Life Course as a Contextual System to Investigate the Effects of Life Events, Gender, and Generation on Travel Mode Use", Transportation Research Board (TRB) 99th Annual Meeting, 2020,

James R.G. Parkinson, Daniel F. Martin, Andrew J. Wells, Richard F. Katz, "Modelling binary alloy solidification with adaptive mesh refinement", Journal of Computational Physics: X, January 7, 2020, 5, doi: 10.1016/j.jcpx.2019.100043

R. Van Beeumen, G. D. Kahanamoku-Meyer, N. Y. Yao and C. Yang, "A scalable matrix-free iterative eigensolver for studying many-body localization", HPCAsia2020: Proceedings of the International Conference on High Performance Computing in Asia-Pacific Region, ACM, January 7, 2020, 179-187, doi: 10.1145/3368474.3368497

Anna Giannakou, Dipankar Dwivedi, Sean Peisert, "A Machine Learning Approach for Packet Loss Prediction in ScienceFlows", Future Generation Computer Systems, January 2020, 102:190-197, doi: 10.1016/j.future.2019.07.053

2019

Reinhard Gentz, Sean Peisert, "An Examination and Survey of Random Bit Flips and Scientific Computing", Trusted CI Report, December 20, 2019,

D. Fan, A. Nonaka, A.S. Almgren, A. Harpole, M. Zingale, "MAESTROeX: A Massively Parallel Low Mach Number Astrophysical Solver", The Astrophysical Journal, December 19, 2019,

Timothy T Duignan, Gregory Schenter, John L Fulton, Thomas Huthwelker, Mahalingam Balasubramanian, Mirza Galib, Marcel Baer, Jan Wilhelm, Jürg Hutter, Mauro Del Ben, Xiu Song Zhao, Christopher Jay Mundy, "Quantifying the Hydration Structure of Sodium and Potassium Ions: Taking Additional Steps on Jacob's Ladder", Physical Chemistry Chemical Physics, December 19, 2019, doi: 10.1039/C9CP06161D

D. Fan, A. Nonaka, A. S. Almgren, D. E. Willcox, A. Harpole, and M. Zingale, "MAESTROeX: A Massively Parallel Low Mach Number Astrophysical Solver", Journal of Open Source Software, December 19, 2019,

Richard Warren, Jerome Soumagne, Jingqing Mu, Houjun Tang, Suren Byna, Bin Dong, Quincey Koziol, "Analysis in the Data Path of an Object-centric Data Management System", 26th IEEE International Conference on High Performance Computing, Data, and Analytics (HiPC 2019), December 18, 2019,

Houjun Tang, Suren Byna, Stephen Bailey, Zarija Lukic, Jialin Liu, Quincey Koziol, Bin Dong, "Tuning Object-centric Data Management Systems for Large Scale Scientific Applications", 26th IEEE International Conference on High Performance Computing, Data, and Analytics (HiPC 2019), December 18, 2019,

Wei Zhang, Suren Byna, Chenxu Niu, Yong Chen, "Exploring Metadata Search Essentials for Scientific Data Management", 26th IEEE International Conference on High Performance Computing, Data, and Analytics (HiPC 2019), December 17, 2019,

Amir Kamil, John Bachan, Scott B. Baden, Dan Bonachea, Paul Hargrove, Steven Hofmeyr, Kathy Yelick, UPC++ Tutorial, National Energy Research Scientific Computing Center (NERSC), December 16, 2019,

This event was a repeat of the tutorial delivered on November 1, but with the restoration of the hands-on component which was omitted due to uncertainty surrounding the power outage at NERSC.

UPC++ is a C++11 library providing classes and functions that support Partitioned Global Address Space (PGAS) programming. UPC++ provides mechanisms for low-overhead one-sided communication, moving computation to data through remote-procedure calls, and expressing dependencies between asynchronous computations and data movement. It is particularly well-suited for implementing elaborate distributed data structures where communication is irregular or fine-grained. The UPC++ interfaces are designed to be composable and similar to those used in conventional C++. The UPC++ programmer can expect communication to run at close to hardware speeds.

In this tutorial we introduced basic concepts and advanced optimization techniques of UPC++. We discussed the UPC++ memory and execution models and walked through implementing basic algorithms in UPC++. We also discussed irregular applications and how to take advantage of UPC++ features to optimize their performance. The tutorial included hands-on exercises with basic UPC++ constructs. Registrants were given access to run their UPC++ exercises on NERSC’s Cori (currently the #14 fastest computer in the world).

Event page

tutorial 2019 12

 

Schneider, Joseph D.; Domann, John P.; Panduranga, M. K.; Tiwari, Sidhant; Shirazi, Paymon; Yao, Zhi Jackie; Sennott, Casey; Shahan, David; Selvin, Skyler; McKnight, Geoff, et al., "Experimental demonstration and operating principles of a multiferroic antenna", Journal, December 14, 2019, 126:224104, doi: 10.1063/1.5126047

Yao, Zhi Jackie; Tiwari, Sidhant; Lu, Ting; Rivera, Jesse; Luong, Kevin Q. T.; Candler, Robert N.; Carman, Gregory P.; Wang, Yuanxun Ethan, "Modeling of multiple dynamics in the radiation of bulk acoustic wave (BAW) antennas", Journal, December 13, 2019, 5:7-20, doi: 10.1109/JMMCT.2019.2959596

Hans Johansen, Daniel Martin, Esmond Ng, "High-resolution Treatment of Topography and Grounding Line Dynamics in BISICLES", AGU 2019 Fall Meeting, December 13, 2019,

Daniel F. Martin, James Parkinson, Andrew Wells, Richard Katz, "3D convection, phase change, and solute transport in mushy sea ice", AGU 2019 Fall Meeting, December 12, 2019,

D. Ghosal, S. Shukla, A. Sim, A. V. Thakur, K. Wu, "A Reinforcement Learning Based Network Scheduler For Deadline-Driven Data Transfers", IEEE Global Communications Conference (GLOBECOM 2019), 2019, doi: 10.1109/GLOBECOM38437.2019.9013255

Q. Kang, A. Agrawal, A. Choudhary, A. Sim, K. Wu, R. Kettimuthu, P. Beckman, Z. Liu, W-K Liao, "Spatiotemporal Real-Time Anomaly Detection for Supercomputing Systems", Workshop on Big Data Predictive Maintenance using Artificial Intelligence, in conjunction with IEEE International Conference on Big Data (Big Data), 2019, doi: 10.1109/BigData47090.2019.9006046

A. Lazar, A. Ballow, L. Jin, C. A. Spurlock, A. Sim, K. Wu, "Machine Learning for Prediction of Mid to LongTerm Habitual Transportation Mode Use", International Workshop on Big Data Tools, Methods, and Use Cases for Innovative Scientific Discovery (BTSD), in conjunction with the IEEE International Conference on Big Data (Big Data), 2019, doi: 10.1109/BigData47090.2019.9006411

B. Cetin, A. Lazar, J. Kim, A. Sim, K. Wu, "Federated Wireless Network Intrusion Detection", IEEE International Conference on Big Data (Big Data), 2019, doi: 10.1109/BigData47090.2019.9005507

P. Linton, W. Melodia, A. Lazar, D. Agarwal, L. Bianchi, D. Ghoshal, K. Wu, G. Pastorello, L. Ramakrishnan, "Identifying Time Series Similarity in Large-Scale Earth System Datasets", International Workshop on Big Data Tools, Methods, and Use Cases for Innovative Scientific Discovery (BTSD), in conjunction with the IEEE International Conference on Big Data (Big Data), 2019,

D. Ghoshal, K. Wu, E. Pouyoul, E. Strohmaier, "Analysis and Prediction of Data Transfer Throughput for Data-Intensive Workloads", The Third IEEE International Workshop on Benchmarking, Performance Tuning and Optimization for Big Data Applications (BPOD 2019), in conjunction with the IEEE International Conference on Big Data (Big Data), 2019,

Samuel Kachuck, Daniel Martin, Jeremy Bassis, Stephen Price, "Rapid viscoelastic deformation slows marine ice sheet instability at Pine Island Glacier", AGU 2019 Fall Meeting, December 10, 2019,

Chaincy Kuo, Daniel Feldman, Daniel Martin, "Quantification of seasonal heat retention by sea-ice: calculations from analytic surface-energy balance", AGU Fall Meeting 2019, December 9, 2019,

Amir Teshome Wonjiga, Louis Rilling, Christine Morin, Sean Peisert, "Blockchain as a Trusted Component in Cloud SLA Verification", Proceedings of the International Workshop on Cloud, IoT and Fog Security (CIFS), co-located with the 12th IEEE/ACM International Conference on Utility and Cloud Computing (UCC), Auckland, New Zealand, December 2019, 93-100, doi: 10.1145/3368235.3368872

M Kiran, B Mohammed and N. Krishnaswamy, "DeepRoute: Herding Elephant and Mice Flows with Reinforcement Learning", 2nd IFIP International Conference on Machine Learning for Networking (MLN'2019), December 2, 2019,

Yong Shi, Daniel R. Ladiges, John E. Sader, "Origin of spurious oscillations in lattice Boltzmann simulations of oscillatory noncontinuum gas flows", Physical Review E, November 25, 2019, 100,

Tirthak Patel, Suren Byna, Glenn K. Lockwood, Devesh Tiwari, "Revisiting I/O Behavior in Large-Scale Storage Systems: The Expected and the Unexpected", Supercomputing 2019 (SC19), November 24, 2019, doi: 10.1145/3295500.3356183

Tuowen Zhao, Mary Hall, Samuel Williams, Hans Johansen, "Exploiting Reuse and Vectorization in Blocked Stencil Computations on CPUs and GPUs", Supercomputing (SC), November 2019,

George Michelogiannakis, Yiwen Shen, Min Yeh Teh, Xian Meng, Benjamin Aivazi, Taylor Groves, John Shalf, Madeleine Glick, Manya Ghobadi, Larry Dennison, Keren Bergman, "Bandwidth Steering in HPC Using Silicon Nanophotonics", SC19: The International Conference for High Performance Computing, Networking, Storage, and Analysis, November 2019,

George Michelogiannakis, Bandwidth Steering in HPC Using Silicon Nanophotonics, SC19: The International Conference for High Performance Computing, Networking, Storage, and Analysis, November 20, 2019,

Donghe Kang, Oliver Rübel, Suren Byna, Spyros Blanas, "Comparison of Array Management Library Performance - A Neuroscience Use Case", SC19 Poster, November 20, 2019,

L. Jin, A. Lazar, J. Sears, A. Todd, A. Sim, K. Wu, C. A. Spurlock, Life course as a contextual system to investigate the effects of life events, gender and generation on travel mode usage, The Behavior, Energy & Climate Change Conference (BECC), 2019,

Wei Zhang, Suren Byna, Houjun Tang, Brody Williams, Yong Chen, "MIQS: Metadata Indexing and erying Service for Self-Describing File Formats", The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC19), November 19, 2019,

Houjun Tang, Quincey Koziol, Suren Byna, John Mainzer, Tonglin Li, "Enabling Transparent Asynchronous I/O using Background Threads", 2019 IEEE/ACM Fourth International Parallel Data Systems Workshop (PDSW 2019), November 19, 2019, doi: DOI 10.1109/PDSW49588.2019.00006

Megha Agarwal, Divyansh Singhvi, Preeti Malakar, Suren Byna, "Active Learning-based Automatic Tuning and Prediction of Parallel I/O Performance", 2019 IEEE/ACM Fourth International Parallel Data Systems Workshop (PDSW), November 19, 2019, doi: DOI 10.1109/PDSW49588.2019.00007

Glenn K. Lockwood, Shane Snyder, Suren Byna, Philip Carns, Nicholas J. Wright, "Understanding Data Motion in the Modern HPC Data Center", 2019 IEEE/ACM Fourth International Parallel Data Systems Workshop (PDSW), November 19, 2019, doi: DOI 10.1109/PDSW49588.2019.00012

P. Linton, W. Melodia, A. Lazar, D. Agarwal, L. Bianchi, D. Ghoshal, K. Wu, G. Pastorello, L. Ramakrishnan, "Identifying Time Series Similarity in Large-Scale Earth System Datasets", The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC19), 2019,

Nan Ding, Samuel Williams, "An Instruction Roofline Model for GPUs", Performance Modeling, Benchmarking, and Simulation (PMBS), BEST PAPER AWARD, November 18, 2019,

Nan Ding, Samuel Williams, An Instruction Roofline Model for GPUs, Performance Modeling, Benchmarking, and Simulation (PMBS), BEST PAPER AWARD, November 18, 2019,

Benjamin A. Brock, Yuxin Chen, Jiakun Yan, John Owens, Aydın Buluç, Katherine Yelick, "RDMA vs. RPC for Implementing Distributed Data Structures", Proceedings of the 2019 IEEE/ACM 9th Workshop on Irregular Applications: Architectures and Algorithms (IA3), Denver, CO, USA, IEEE, November 18, 2019, doi: 10.1109/IA349570.2019.00009

Distributed data structures are key to implementing scalable applications for scientific simulations and data analysis. In this paper we look at two implementation styles for distributed data structures: remote direct memory access (RDMA) and remote procedure call (RPC). We focus on operations that require individual accesses to remote portions of a distributed data structure, e.g., accessing a hash table bucket or distributed queue, rather than global operations in which all processors collectively exchange information. We look at the trade-offs between the two styles through microbenchmarks and a performance model that approximates the cost of each. The RDMA operations have direct hardware support in the network and therefore lower latency and overhead, while the RPC operations are more expressive but higher cost and can suffer from lack of attentiveness from the remote side. We also run experiments to compare the real-world performance of RDMA- and RPC-based data structure operations with the predicted performance to evaluate the accuracy of our model, and show that while the model does not always precisely predict running time, it allows us to choose the best implementation in the examples shown. We believe this analysis will assist developers in designing data structures that will perform well on current network architectures, as well as network architects in providing better support for this class of distributed data structures.

Paul H. Hargrove, Dan Bonachea, "Efficient Active Message RMA in GASNet Using a Target-Side Reassembly Protocol (Extended Abstract)", IEEE/ACM Parallel Applications Workshop, Alternatives To MPI+X (PAW-ATM), Lawrence Berkeley National Laboratory Technical Report, November 17, 2019, LBNL 2001‍238, doi: 10.25344/S4PC7M

GASNet is a portable, open-source, high-performance communication library designed to efficiently support the networking requirements of PGAS runtime systems and other alternative models on future exascale machines. This paper investigates strategies for efficient implementation of GASNet’s “AM Long” API that couples an RMA (Remote Memory Access) transfer with an Active Message (AM) delivery.
We discuss several network-level protocols for AM Long and propose a new target-side reassembly protocol. We present a microbenchmark evaluation on the Cray XC Aries network hardware. The target-side reassembly protocol on this network improves AM Long end-to-end latency by up to 33%, and the effective bandwidth by up to 49%, while also enabling asynchronous source completion that drastically reduces injection overheads.
The improved AM Long implementation for Aries is available in GASNet-EX release v2019.9.0 and later.

Khaled Ibrahim, Samuel Williams, Leonid Oliker, "Performance Analysis of GPU Programming Models using the Roofline Scaling Trajectories", International Symposium on Benchmarking, Measuring and Optimizing (Bench), BEST PAPER AWARD, November 2019,

B Mohammed, M Kiran, N Krishnaswamy, "DeepRoute on Chameleon: Experimenting with Large-scale Reinforcement Learning and SDN on Chameleon Testbed", IEEE 27th International Conference on Network Protocols (ICNP), IEEE, November 14, 2019, 1-2,

George Papadimitriou, Mariam Kiran, Cong Wang, Anirban Mandal, Ewa Deelman, "Training Classifiers to Identify TCP Signatures in Scientific Workflows", INDIS, SC19, November 14, 2019,

Dan Bonachea, Paul H. Hargrove, "GASNet-EX: A High-Performance, Portable Communication Library for Exascale", LNCS 11882: Proceedings of Languages and Compilers for Parallel Computing (LCPC'18), edited by Hall M., Sundar H., November 2019, 11882:138-158, doi: 10.1007/978-3-030-34627-0_11

Partitioned Global Address Space (PGAS) models, typified by such languages 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 upon over 15 years of lessons learned. We describe and evaluate several features and enhancements that have been introduced to address the needs of modern client systems. Microbenchmark results demonstrate the RMA performance of GASNet-EX is competitive with several MPI-3 implementations on current HPC systems.

Christine T. Wolf, Julia Bullard, Stacy Wood, Amelia Acker, Drew Paine, Charlotte P. Lee, "Mapping the “How” of Collaborative Action: Research Methods for Studying Contemporary Sociotechnical Processes", CSCW '19: Conference Companion Publication of the 2019 on Computer Supported Cooperative Work and Social Computing, November 10, 2019, doi: 10.1145/3311957.3359441

Process has been a concern since the beginning of CSCW. Developments in sociotechnical landscapes raise new challenges for studying processes (e.g., massive online communities bringing together vast crowds; Big Data technologies connecting many through the flow of data). This re-opens questions about how we study, document, conceptualize, and design to support processes in complex, contemporary sociotechnical systems. This one-day workshop will bring together researchers to discuss the CSCW community’s unique focus and methodological toolkit for studying process and workflow; provide a collaborative space for the improvement and extension of research projects within this space; and catalyze a network of scholars with expertise and interest in addressing challenging methodological questions around studying process in contemporary, sociotechnical systems.

Mahdi Jamei, Raksha Ramakrishna, Teklemariam Tesfay, Reinhard Gentz, Ciaran Roberts, Anna Scaglione, Sean Peisert, "Phasor Measurement Units Optimal Placement and Performance Limits for Fault Localization", IEEE Journal on Selected Areas in Communications (J-SAC), Special Issue on Communications and Data Analytics in Smart Grid, November 6, 2019, 38(1):180-192, doi: 10.1109/jsac.2019.2951971

J. Balcas, H. Newman, M. Spiropulu, X. Yang, T. Lehman, I. Monga, C. Guok, J. MacAuley, A. Sim, P. Demar, "SDN for End-to-End Networking at Exascale", the 24th International Conference on Computing in High Energy and Nuclear Physics (CHEP2019), 2019,

Mark Adams, Stephen Cornford, Daniel Martin, Peter McCorquodale, "Composite matrix construction for structured grid adaptive mesh refinement", Computer Physics Communications, November 2019, 244:35-39, doi: 10.1016/j.cpc.2019.07.006

Pooria Mohammadiyaghni, George Michelogiannakis, Paul V. Gratz, "SpecLock: Speculative Lock Forwarding", International Conference on Computer Design (ICCD), November 2019,

L. Yang, Z. Wen, C. Yang and Y. Zhang, "`Block Algorithms with Augmented Rayleigh-Ritz Projections for Large-Scale Eigenpair Computation", Journal of Computational Mathematics, November 1, 2019, 37:889-915, doi: 10.4208/jcm.1910-m2019-0034

Amir Kamil, John Bachan, Scott B. Baden, Dan Bonachea, Rob Egan, Paul Hargrove, Steven Hofmeyr, Mathias Jacquelin, Kathy Yelick, UPC++ Tutorial, National Energy Research Scientific Computing Center (NERSC), November 1, 2019,

UPC++ is a C++11 library providing classes and functions that support Partitioned Global Address Space (PGAS) programming. UPC++ provides mechanisms for low-overhead one-sided communication, moving computation to data through remote-procedure calls, and expressing dependencies between asynchronous computations and data movement. It is particularly well-suited for implementing elaborate distributed data structures where communication is irregular or fine-grained. The UPC++ interfaces are designed to be composable and similar to those used in conventional C++. The UPC++ programmer can expect communication to run at close to hardware speeds.

In this tutorial we will introduce basic concepts and advanced optimization techniques of UPC++. We will discuss the UPC++ memory and execution models and walk through implementing basic algorithms in UPC++. We will also look at irregular applications and how to take advantage of UPC++ features to optimize their performance.

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Alexandra Ballow, Alina Lazar (Advisor), Alex Sim (Advisor), Kesheng Wu (Advisor), "Handling Missing Values in Joint Sequence Analysis", ACM Richard Tapia Celebration of Diversity in Computing (TAPIA 2019), ACM Student Research Competition (SRC), First place winner, 2019,

Thomas W. Edgar, Aditya Ashok, Garret E. Seppala, K.M. Arthur-Durrett, M. Engels, Reinhard Gentz, Sean Peisert, "An Automated Disruption-Tolerant Key Management Framework for Critical Systems", Journal of Information Warfare, October 8, 2019, 18(4):85-124, doi: https://www.jinfowar.com/journal/volume-18-issue-4/automated-disruption-tolerant-device-authentication-key-management-framework-critical-systems

Jung Heon Song, Marcos L\ opez de Prado, Horst D. Simon, Kesheng Wu, Extracting Signals from High-Frequency Trading with Digital Signal Processing Tools, The Journal of Financial Data Science, Pages: 124--138 2019, doi: 10.3905/jfds.2019.1.4.124

T. Hernandez, R. Van Beeumen, M. Caprio and C. Yang, "A greedy algorithm for computing eigenvalues of a symmetric matrix", submitted, October 1, 2019,

Reinhard Gentz, Sean Peisert, Joshua Boverhof, Daniel Gunter, "SPARCS: Stream-Processing Architecture applied in Real-time Cyber-physical Security", Proceedings of the 15th IEEE International Conference on e-Science (eScience), San Diego, CA, IEEE, September 2019, doi: 10.1109/eScience.2019.00028

Reinhard Gentz, Héctor García Martin, Edward Baidoo, Sean Peisert, "Workflow Automation in Liquid Chromatography Mass Spectrometry", Proceedings of the 15th IEEE International Conference on e-Science (eScience), San Diego, CA, IEEE, September 2019, doi: 10.1109/eScience.2019.00095

Bin Wang, Rongxin Yin, Doug Black and Cy Chan, "Multistage and decentralized operations of electric vehicles within the California demand response markets", Decision Making Applications in Modern Power Systems, (Academic Press, Elsevier: September 21, 2019) Pages: 411-439 doi: https://doi.org/10.1016/B978-0-12-816445-7.00016-5

John Bachan, Scott Baden, Dan Bonachea, Paul Hargrove, Steven Hofmeyr, Mathias Jacquelin, Amir Kamil, Brian van Straalen, "UPC++ v1.0 Programmer’s Guide, Revision 2019.9.0", Lawrence Berkeley National Laboratory Tech Report, September 2019, LBNL 2001236, doi: 10.25344/S4V30R

UPC++ is a C++11 library that provides Partitioned Global Address Space (PGAS) programming. It is designed for writing parallel programs that run efficiently and scale well on distributed-memory parallel computers. The PGAS model is single program, multiple-data (SPMD), with each separate constituent process having access to local memory as it would in C++. However, PGAS also provides access to a global address space, which is allocated in shared segments that are distributed over the processes. UPC++ provides numerous methods for accessing and using global memory. In UPC++, all operations that access remote memory are explicit, which encourages programmers to be aware of the cost of communication and data movement. Moreover, all remote-memory access operations are by default asynchronous, to enable programmers to write code that scales well even on hundreds of thousands of cores.

John Bachan, Scott Baden, Dan Bonachea, Paul Hargrove, Steven Hofmeyr, Mathias Jacquelin, Amir Kamil, Brian van Straalen, "UPC++ v1.0 Specification, Revision 2019.9.0", Lawrence Berkeley National Laboratory Tech Report, September 14, 2019, LBNL 2001237, doi: 10.25344/S4ZW2C

UPC++ is a C++11 library providing classes and functions that support Partitioned Global Address Space (PGAS) programming. We are revising the library under the auspices of the DOE’s Exascale Computing Project, to meet the needs of applications requiring PGAS support. UPC++ is intended for implementing elaborate distributed data structures where communication is irregular or fine-grained. The UPC++ interfaces for moving non-contiguous data and handling memories with different optimal access methods are composable and similar to those used in conventional C++. The UPC++ programmer can expect communication to run at close to hardware speeds. The key facilities in UPC++ are global pointers, that enable the programmer to express ownership information for improving locality, one-sided communication, both put/get and RPC, futures and continuations. Futures capture data readiness state, which is useful in making scheduling decisions, and continuations provide for completion handling via callbacks. Together, these enable the programmer to chain together a DAG of operations to execute asynchronously as high-latency dependencies become satisfied.

S Werner, P Fotouhi, X Xiao, M Fariborz, SJB Yoo, G Michelogiannakis, D Vasudevan, "3D photonics as enabling technology for deep 3D DRAM stacking", Proceedings of the International Symposium on Memory Systems - MEMSYS 19, ACM Press, September 2019, doi: 10.1145/3357526.3357559

Bin Wang, Cy Chan, Divya Somasi, Jane Macfarlane, Eric Rask, "Data-Driven Energy Use Estimation in Large Scale Transportation Networks", Proceedings of the 2nd ACM/EIGSCC Symposium on Smart Cities and Communities - SCC '19, ACM Press, September 10, 2019,

Revathi Jambunathan, Andrew Myers, Donald Willcox, Jean-Luc Vay, Ann Almgren, Diana Amorim, John Bell, Kevin Gott, Axel Huebl, Remi Lehe, Micahel Rowan, Olga Shapoval, Maxence Thevenet, Weiqun Zhang, "WarpX: Towards exascale modeling of pulsar magnetospheres", Connecting Micro and Macro Scales: Acceleration, Reconnection, and Dissipation in Astrophysical Plasmas, September 9, 2019,

Doru Thom Popovici, Devangi N. Parikh, Daniele G. Spampinato, Tze Meng Low, "Exploiting Symmetries of Small Prime-Sized DFTs", PPAM 2019, 2019,

Melissa Stockman, Dipankar Dwivedi, Reinhard Gentz, Sean Peisert, "Detecting Programmable Logic Controller Code Using Machine Learning", International Journal of Critical Infrastructure Protection, September 2019, vol. 26,, doi: 10.1016/j.ijcip.2019.100306

Timur Takhtaganov, Zarija Lukić, Juliane Mueller, Dmitriy Morozov, "Cosmic Inference: Constraining Parameters With Observations and Highly Limited Number of Simulations", Astrophysical Journal (in review), 2019,

Charlene Yang, Thorsten Kurth, Samuel Williams, "Hierarchical Roofline analysis for GPUs: Accelerating performance optimization for the NERSC-9 Perlmutter system", Concurrency and Computation: Practice and Experience (CCPE), August 2019, doi: 10.1002/cpe.5547

M. Zingale, M.P. Katz, J.B. Bell, M.L. Minion, A.J. Nonaka, W. Zhang, "Improved Coupling of Hydrodynamics and Nuclear Reactions via Spectral Deferred Corrections", August 14, 2019,

Knut Sverdrup, Ann S. Almgren, Nikolaos Nikiforakis, "An embedded boundary approach for efficient simulations of viscoplastic fluids in three dimensions", August 10, 2019,

L. Esclapez, V. Ricchiuti, J.B. Bell, M.S. Day, "A spectral deferred correction strategy for low Mach number flows subject to electric fields", August 10, 2019,

D. R. Ladiges, A. J. Nonaka, J. B. Bell, A. L. Garcia, "On the Suppression and Distortion of Non-Equilibrium Fluctuations by Transpiration", Physics of Fluids, August 10, 2019,

A. Donev, A. J. Nonaka, C. Kim, A. L. Garcia, J. B. Bell, "Fluctuating hydrodynamics of electrolytes at electroneutral scales", August 10, 2019,

N. T. Wimer, M. S. Day, C. Lapointe, A. S. Makowiecki, J. F. Glusman, J. W. Daily, G. B. Rieker, P. E. Hamlington, "High-resolution numerical simulations of a large-scale helium plume using adaptive mesh refinement", August 10, 2019,

M. T. Henry de Frahan, S. Yellapantula, R. King, M. S. Day, R. W. Grout, "Deep learning for presumed probability density function models", August 10, 2019,

D. Dasgupta, W. Sun, M. Day, A. Aspden, T. Lieuwen, "Analysis of chemical pathways for n-dodecane/air turbulent premixed flames", August 10, 2019,

M. Zingale, K. Eiden, Y. Cavecchi, A. Harpole, J. B. Bell, M. Chang, I. Hawke, M. P. Katz, C.M. Malone, A. J. Nonaka, D. E. Willcox, W. Zhang, "Toward resolved simulations of burning fronts in thermonuclear X-ray bursts", Journal of Physics: Conference Series, 2019, 1225,

A. J. Aspden, M. S. Day, J. B. Bell, "Towards the Distributed Burning Regime in Turbulent Premixed Flames", Journal of Fluid Mechanics, 2019, 871:1-21,

J. Bell, M. Day, J. Goodman, R. Grout, M. Morzfeld, "A Bayesian approach to calibrating hydrogen flame kinetics using many experiments and parameters", Combustion and Flame, 2019,

Mauro Del Ben, Osni Marques, Andrew Canning, "Improved Unconstrained Energy Functional Method for Eigensolvers in Electronic Structure Calculations", Proceedings of the 48th International Conference on Parallel Processing, ACM, 2019, 73, doi: 10.1145/3337821.3337914

Benjamin Brock, Aydın Buluç, Katherine Yelick, "BCL: A Cross-Platform Distributed Data Structures Library", ICPP 2019: Proceedings of the 48th International Conference on Parallel Processing, Kyoto, Japan, Association for Computing Machinery, August 2019, doi: 10.1145/3337821.3337912

One-sided communication is a useful paradigm for irregular parallel applications, but most one-sided programming environments, including MPI's one-sided interface and PGAS programming languages, lack application-level libraries to support these applications. We present the Berkeley Container Library, a set of generic, cross-platform, high-performance data structures for irregular applications, including queues, hash tables, Bloom filters and more. BCL is written in C++ using an internal DSL called the BCL Core that provides one-sided communication primitives such as remote get and remote put operations. The BCL Core has backends for MPI, OpenSHMEM, GASNet-EX, and UPC++, allowing BCL data structures to be used natively in programs written using any of these programming environments. Along with our internal DSL, we present the BCL ObjectContainer abstraction, which allows BCL data structures to transparently serialize complex data types while maintaining efficiency for primitive types. We also introduce the set of BCL data structures and evaluate their performance across a number of high-performance computing systems, demonstrating that BCL programs are competitive with hand-optimized code, even while hiding many of the underlying details of message aggregation, serialization, and synchronization.

Ciaran Roberts, Anna Scaglione, Mahdi Jamei, Reinhard Gentz, Sean Peisert, Emma M. Stewart, Chuck McParland, Alex McEachern, Daniel Arnold, "Learning Behavior of Distribution System Discrete Control Devices for Cyber-Physical Security", IEEE Transaction on Smart Grid, August 1, 2019, 11(1):749-761, doi: 0.1109/TSG.2019.2936016

B Mohammed, N Krishnaswamy, M Kiran, "Multivariate Time-Series Prediction for Traffic in Large WAN Topology", ACM/IEEE Symposium on Architectures for Networking and Communications, August 1, 2019,

Andrew Adams, Kay Avila, Jim Basney, Dana Brunson, Robert Cowles, Jeannette Dopheide, Terry Fleury, Elisa Heymann, Florence Hudson, Craig Jackson, Ryan Kiser, Mark Krenz, Jim Marsteller, Barton P. Miller, Sean Peisert, Scott Russell, Susan Sons, Von Welch, John Zage, "Trusted CI Experiences in Cybersecurity and Service to Open Science", Proceedings of the Conference on Practice and Experience in Advanced Research Computing (PEARC), ACM, July 2019, doi: 10.1145/3332186.3340601

Bin Dong, Patrick Frank Heiner Kilian, Xiaocan Li, Fan Guo, Suren Byna and Kesheng Wu, "Terabyte-scale Particle Data Analysis: An ArrayUDF Case Study", SSDBM 2019, July 23, 2019,

Peiyun Li, Sergii Gridin, K. Burak Ucer, Richard T. Williams, Mauro Del Ben, Andrew Canning, Federico Moretti, Edith Bourret, "Picosecond Absorption Spectroscopy of Excited States in BaBrCl with and without Eu Dopant and Au Codopant", Physical Review Applied, 2019, 12 (1):014035, doi: 10.1103/PhysRevApplied.12.014035

Nan Ding, Samuel Williams, Sherry Li, Yang Liu, "Leveraging One-Sided Communication for Sparse Triangular Solvers", SciDAC19, July 18, 2019,

Samuel Williams, Charlene Yang, Khaled Ibrahim, Thorsten Kurth, Nan Ding, Jack Deslippe, Leonid Oliker, "Performance Analysis using the Roofline Model", SciDAC PI Meeting, July 2019,

J. Choi, A. Sim, Data reduction methods, systems and devices, U.S. Patent No. 10,366,078, 2019,

U.S. Patent No. 10,366,078, “DATA REDUCTION METHODS, SYSTEMS, AND DEVICES”, LBNL IB2013-133.

Hannah E. Ross, Keri L. Dixon, Raghunath Ghara, Ilian T. Iliev, Garrelt Mellema,, "Evaluating the QSO contribution to the 21-cm signal from the Cosmic Dawn", Monthly Notices of the Royal Astronomical Society, July 2019, 487:1101-1119, doi: 10.1093/mnras/stz1220

J. Onorbe, F. B. Davies, Z. Lukić, J. F. Hennawi, D. Sorini, "Inhomogeneous Reionization Models in Cosmological Hydrodynamical Simulations", Monthly Notices of Royal Astronomical Society, 2019, 486:4075, doi: 10.1093/mnras/stz984

A. Harpole, D. Fan, M. P. Katz, A. J. Nonaka, D. E. Willcox, and M. Zingale, "Modelling low Mach number stellar hydrodynamics with MAESTROeX", Proceedings of Astronum 2019, July 1, 2019,

Jongbeen Han, Heemin Kim, Hyeonsang Eom, Jonathan Coignard, Kesheng Wu, Yongseok Son, "Enabling SQL-Query Processing for Ethereum-based Blockchain Systems", WIMS2019, New York, NY, USA, ACM, 2019, 9:1--9:7, doi: 10.1145/3326467.3326479

H. Sung, J. Bang, A. Sim, K. Wu, H. Eom, "Understanding Parallel I/O Performance Trends Under Various HPC Configurations", the 2nd International Workshop on Systems and Network Telemetry and Analytics (SNTA 2019), in conjunction with ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC 2019), 2019, doi: 10.1145/3322798.3329258

M. Jin, Y. Homma, A. Sim, W. Kroeger, K. Wu, "Performance Prediction for Data Transfers in LCLS Workflow", the 2nd International Workshop on Systems and Network Telemetry and Analytics (SNTA 2019), in conjunction with ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC 2019), 2019, doi: 10.1145/3322798.3329254

O. Del Guercio, R. Orozco, A. Sim, K. Wu, "Similarity-based Compression with Multidimensional Pattern Matching", the 2nd International Workshop on Systems and Network Telemetry and Analytics (SNTA 2019), in conjunction with ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC 2019), 2019, doi: 10.1145/3322798.3329252

A. Syal, A. Lazar, J. Kim, K. Wu, A. Sim, "Automatic Detection of Network Traffic Anomalies and Changes", the 2nd International Workshop on Systems and Network Telemetry and Analytics (SNTA 2019), in conjunction with ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC 2019), 2019, doi: 10.1145/3322798.3329255

Marquita Ellis, Giulia Guidi, Aydın Buluç, Leonid Oliker, Katherine Yelick, "diBELLA: Distributed Long Read to Long Read Alignment", 48th International Conference on Parallel Processing (ICPP), June 25, 2019,

Bin Dong, Kesheng Wu, Suren Byna, Houjun Tang, "SLOPE: Structural Locality-aware Programming Model for Composing Array Data Analysis", ISC 2019 ((Acceptance rate:24%),), June 16, 2019,

Vikram Khaire, Michael Walther, Joseph F. Hennawi, Jose Oñorbe, Zarija Lukić, Xavier J. Prochaska, Todd M. Tripp, Joseph N. Burchett, Christian Rodriguez, "The power spectrum of the Lyman-α Forest at z < 0.5", Monthly Notices of the Royal Astronomical Society, 2019, 486:769, doi: 10.1093/mnras/stz344

John Bachan, Scott B. Baden, Steven Hofmeyr, Mathias Jacquelin, Amir Kamil, Dan Bonachea, Paul H. Hargrove, Hadia Ahmed, "UPC++: A High-Performance Communication Framework for Asynchronous Computation", 33rd IEEE International Parallel & Distributed Processing Symposium (IPDPS'19), Rio de Janeiro, Brazil, IEEE, May 2019, doi: 10.25344/S4V88H

UPC++ is a C++ library that supports high-performance computation via an asynchronous communication framework. This paper describes a new incarnation that differs substantially from its predecessor, and we discuss the reasons for our design decisions. We present new design features, including future-based asynchrony management, distributed objects, and generalized Remote Procedure Call (RPC).
We show microbenchmark performance results demonstrating that one-sided Remote Memory Access (RMA) in UPC++ is competitive with MPI-3 RMA; on a Cray XC40 UPC++ delivers up to a 25% improvement in the latency of blocking RMA put, and up to a 33% bandwidth improvement in an RMA throughput test. We showcase the benefits of UPC++ with irregular applications through a pair of application motifs, a distributed hash table and a sparse solver component. Our distributed hash table in UPC++ delivers near-linear weak scaling up to 34816 cores of a Cray XC40. Our UPC++ implementation of the sparse solver component shows robust strong scaling up to 2048 cores, where it outperforms variants communicating using MPI by up to 3.1x.
UPC++ encourages the use of aggressive asynchrony in low-overhead RMA and RPC, improving programmer productivity and delivering high performance in irregular applications.

Elliott Binder, Tze Meng Low, Doru Thom Popovici, "Portable GPU Framework for SNP Comparisons", HiCOMB 2019, 2019,

Revathi Jambunathan, Deborah Levin, "Kinetic, three-dimensional, PIC-DSMC simulations of ion thruster plumes and the backflow region, Part 1: A colocated ion-electron source", (under review), May 20, 2019,

S. Kim, A. Sim, K. Wu, S. Byna, T. Wang, Y. Son, H. Eom, "DCA-IO: A Dynamic I/O Control Scheme for Parallel and Distributed File System", 19th Annual IEEE/ACM International Symposium in Cluster, Cloud, and Grid Computing (CCGrid 2019), 2019, doi: 10.1109/CCGRID.2019.00049

Teng Wang, Suren Byna, Glenn Lockwood, Philip Carns, Shane Snyder, Sunggon Kim, Nicholas Wright, "A Zoom-in Analysis of I/O Logs to Detect Root Causes of I/O Performance Bottlenecks", IEEE/ACM CCGrid 2019, May 14, 2019,

Weiqun Zhang, Ann Almgren, Vince Beckner, John Bell, Johannes Blashke, Cy Chan, Marcus Day, Brian Friesen, Kevin Gott, Daniel Graves, Max P. Katz, Andrew Myers, Tan Nguyen, Andrew Nonaka, Michele Rosso, Samuel Williams, Michael Zingale, "AMReX: a framework for block-structured adaptive mesh refinement", Journal of Open Source Software, May 2019, doi: 10.21105/joss.01370

Tonglin Li, Quincey Koziol, Houjun Tang, Jialin Liu, Suren Byna, "I/O Performance Analysis of Science Applications Using HDF5 File-level Provenance", Cray User Group (CUG) 2019, May 10, 2019,

US Patent app no. 20190138371, “Methods, systems, and devices for accurate signal timing of power component events”

T. Bowen, E. Zhivun, A. Wickenbrock, V. Dumont, S. D. Bale, C. Pankow, G. Dobler, J. S. Wurtele, D. Budker, "A Network of Magnetometers for Multi-Scale Urban Science and Informatics", Geosci. Instrum. Method. Data Syst., Volume 8, Issue 1, Pages 129-138, May 8, 2019, doi: 10.5194/gi-8-129-2019

Charlene Yang, Thorsten Kurth, Samuel Williams, "Hierarchical Roofline Analysis for GPUs: Accelerating Performance Optimization for the NERSC-9 Perlmutter System", Cray User Group (CUG), May 2019,

Jingqing Mu, Jerome Soumagne, Suren Byna, Quincey Koziol, Houjun Tang, Richard Warren, "Interfacing HDF5 with A Scalable Object-centric Storage System on Hierarchical Storage", Cray User Group (CUG) 2019, May 7, 2019,

Wenjing Ma, Yulong Ao, Chao Yang, Samuel Williams, "Solving a trillion unknowns per second with HPGMG on Sunway TaihuLight", Cluster Computing, May 2019, doi: 10.1007/s10586-019-02938-w

M. Mustafa, D. Bard, W. Bhimji, Z. Lukić, R. Al-Rfou, J. Kratochvil, "CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks", Computational Astrophysics and Cosmology, 2019, 6:1, doi: 10.1186/s40668-019-0029-9

Anastasiia Butko, George Michelogiannakis, David Donofrio, John Shalf, "Extending classical processors to support future large scale quantum accelerators", Proceedings of the 16th ACM International Conference on Computing Frontiers Pages, April 2019,

Anastasiia Butko, George Michelogiannakis, David Donofrio, John Shalf, "TIGER: topology-aware task assignment approach using ising machines", Proceedings of the 16th ACM International Conference on Computing Frontiers, April 2019,

Boris Lo, Phillip Colella, "An Adaptive Local Discrete Convolution Method for the Numerical Solution of Maxwell's Equations", Communications in Applied Mathematics and Computational Science, April 26, 2019, 14:105-119, doi: DOI: 10.2140/camcos.2019.14.105

Doru Thom Popovici, Martin D. Schatz, Franz Franchetti, Tze Meng Low, "A Flexible Framework for Parallel Multi-Dimensional DFTs", April 23, 2019,

G Tzimpragos, A Madhavan, D Vasudevan, D Strukov and T Sherwood, "Boosted Race Trees for Low Energy Classification - Best Paper Award", ("Best Paper Award"), ASPLOS 2019, April 2019, doi: 10.1145/3297858.3304036

Francois P. Hamon, Martin Schreiber, Michael L. Minion, "Parallel-in-Time Multi-Level Integration of the Shallow-Water Equations on the Rotating Sphere", April 12, 2019,

Submitted to Journal of Computational Physics

D.F. Martin, H.S. Johansen, P.O. Schwartz, E.G. Ng, "Improved Discretization of Grounding Lines and Calving Fronts using an Embedded-Boundary Approach in BISICLES", European Geosciences Union General Assembly, April 10, 2019,

B. Peng, R. Van Beeumen, D.B. Williams-Young, K. Kowalski, C. Yang, "Approximate Green’s function coupled cluster method employing effective dimension reduction", Journal of Chemical Theory and Computation, 2019, 15:3185-3196, doi: 10.1021/acs.jctc.9b00172

J. Kim, A. Sim, B. Tierney, S. Suh, I. Kim, "Multivariate Network Traffic Analysis using Clustered Patterns", Journal of Computing, April 2019, 101(4):339-361, doi: 10.1007/s00607-018-0619-4

P. Benner, V. Khoromskaia, B. N. Khoromskij and C. Yang, "Computing the density of states for optical spectra of molecules by low-rank and QTT tensor approximation", Journal of Computational Physics, April 1, 2019, 382:221-239, doi: https://doi.org/10.1016/j.jcp.2019.01.011

J. Kim, A. Sim, "A new approach to multivariate network traffic analysis", Journal of Computer Science and Technology, 2019, 34(2):388–402, doi: 10.1007/s11390-019-1915-y

Olivia Del Guercio, Rafael Orozco, Alex Sim, Kesheng Wu, "Multidimensional Compression with Pattern Matching", Data Compression Conference (DCC), 2019, doi: 10.1109/DCC.2019.00079

image

Sergi Molins, David Trebotich, Bhavna Arora, Carl Steefel, Hang Deng, "Multi-scale Model of Reactive Transport in Fractured Media: Diffusion Limitations on Rates", Transport in Porous Media, March 20, 2019, 128:701-721, doi: 10.1007/s11242-019-01266-2

Sean Peisert, Brooks Evans, Michael Liang, Barclay Osborn, David Rusting, David Thurston, Security Without Moats and Walls: Zero-Trust Networking for Enhancing Security in R&E Environments, CENIC Annual Conference, March 19, 2019,

Charlene Yang, Samuel Williams, Performance Analysis of GPU-Accelerated Applications using the Roofline Model, GPU Technology Conference (GTC), March 2019,

Drew Paine, Lavanya Ramakrishnan, "Surfacing Data Change in Scientific Work", iConference 2019, Springer Verlag, March 19, 2019, 15-26, doi: 10.1007/978-3-030-15742-5_2

Mauro Del Ben, H Felipe, Gabriel Antonius, Tonatiuh Rangel, Steven G Louie, Jack Deslippe, Andrew Canning, "Static Subspace Approximation for the Evaluation of G0W0 Quasiparticle Energies within a Sum-Over-Bands Approach", Physical Review B, 2019, 99 (12):125128, doi: 10.1103/PhysRevB.99.125128

John Bachan, Scott Baden, Dan Bonachea, Paul Hargrove, Steven Hofmeyr, Mathias Jacquelin, Amir Kamil, Brian van Straalen, "UPC++ Programmer's Guide, v1.0-2019.3.0", Lawrence Berkeley National Laboratory Tech Report, March 2019, LBNL 2001191, doi: 10.25344/S4F301

UPC++ is a C++11 library that provides Partitioned Global Address Space (PGAS) programming. It is designed for writing parallel programs that run efficiently and scale well on distributed-memory parallel computers. The PGAS model is single program, multiple-data (SPMD), with each separate constituent process having access to local memory as it would in C++. However, PGAS also provides access to a global address space, which is allocated in shared segments that are distributed over the processes. UPC++ provides numerous methods for accessing and using global memory. In UPC++, all operations that access remote memory are explicit, which encourages programmers to be aware of the cost of communication and data movement. Moreover, all remote-memory access operations are by default asynchronous, to enable programmers to write code that scales well even on hundreds of thousands of cores.

John Bachan, Scott Baden, Dan Bonachea, Paul Hargrove, Steven Hofmeyr, Mathias Jacquelin, Amir Kamil, Brian van Straalen, "UPC++ Specification v1.0, Draft 10", Lawrence Berkeley National Laboratory Tech Report, March 15, 2019, LBNL 2001192, doi: 10.25344/S4JS30

UPC++ is a C++11 library providing classes and functions that support Partitioned Global Address Space (PGAS) programming. We are revising the library under the auspices of the DOE’s Exascale Computing Project, to meet the needs of applications requiring PGAS support. UPC++ is intended for implementing elaborate distributed data structures where communication is irregular or fine-grained. The UPC++ interfaces for moving non-contiguous data and handling memories with different optimal access methods are composable and similar to those used in conventional C++. The UPC++ programmer can expect communication to run at close to hardware speeds. The key facilities in UPC++ are global pointers, that enable the programmer to express ownership information for improving locality, one-sided communication, both put/get and RPC, futures and continuations. Futures capture data readiness state, which is useful in making scheduling decisions, and continuations provide for completion handling via callbacks. Together, these enable the programmer to chain together a DAG of operations to execute asynchronously as high-latency dependencies become satisfied.

Babak Behzad, Suren Byna, Prabhat, and Marc Snir, "Optimizing I/O Performance of HPC Applications with Autotuning", ACM Transactions on Parallel Computing (TOPC), February 28, 2019,

Alexandra Ballow, Alina Lazar, Alex Sim, Kesheng Wu, "Joint Sequence Analysis Challenges: How to Handle Missing Values and Mixed Variable Types", SIAM Conference on Computational Science and Engineering (CSE19), 2019,

Tyler Leibengood, Alina Lazar, Alex Sim, Kesheng Wu, "Network Traffic Performance Prediction with Multivariate Clusters in Time Windows", SIAM Conference on Computational Science and Engineering (CSE19), 2019,

Daniel Martin, Modeling Antarctic Ice Sheet Dynamics using Adaptive Mesh Refinement, 2019 SIAM Conference on Computational Science and Engineering, February 26, 2019,

Samuel Williams, Performance Modeling and Analysis, CS267 Lecture, University of California at Berkeley, February 14, 2019,

Sean Peisert, Experiences in Building a Mission-Driven Security R&D Program for Science and Energy, Computer Science Colloquium Seminar, University of California, Davis, February 7, 2019,

George Michelogiannakis, Jeremiah Wilke, Min Yee Teh, Madeleine Glick, John Shalf, Keren Bergman, "Challenges and opportunities in system-level evaluation of photonics", Proceedings Volume 10946, Metro and Data Center Optical Networks and Short-Reach Links II, February 2019, doi: https://doi.org/10.1117/12.2510443

Sean Peisert, Daniel Arnold, Using Physics to Improve Cybersecurity for the Distribution Grid and Distributed Energy Resources, Naval Postgraduate School, February 5, 2019,

Aleksandar Donev, Alejandro L. Garcia, Jean-Philippe Péraud, Andrew J. Nonaka, John B. Bell, "Fluctuating Hydrodynamics and Debye-Hückel-Onsager Theory for Electrolytes", Current Opinion in Electrochemistry, 2019, 13:1 - 10, doi: https://doi.org/10.1016/j.coelec.2018.09.004

M. Walther, J. Onorbe, J. F. Hennawi, Z. Lukić, "New Constraints on IGM Thermal Evolution from the Ly-alpha Forest Power Spectrum", The Astrophysical Journal, 2019, 872:13, doi: 10.3847/1538-4357/aafad1

M Kiran, A Chhabra, "Understanding flows in high-speed scientific networks: A Netflow data study", Future Generation Computer Systems, February 1, 2019, 94:72-79,

Mauro Del Ben, H Felipe, Andrew Canning, Nathan Wichmann, Karthik Raman, Ruchira Sasanka, Chao Yang, Steven G Louie, Jack Deslippe, "Large-Scale GW Calculations on Pre-Exascale HPC Systems", Computer Physics Communications, 2019, 235:187-195, doi: 10.1016/j.cpc.2018.09.003

Beytullah Yildiz, Kesheng Wu, Suren Byna, Arie Shoshanii,, "Parallel membership queries on very large scientific data sets using bitmap indexes", Concurrency and Computation: Practice and Experience, January 28, 2019, 31, doi: https://doi.org/10.1002/cpe.5157

Many scientific applications produce very large amounts of data as advances in hardware fuel computing and experimental facilities. Managing and analyzing massive quantities of scientific data is challenging as data are often stored in specific formatted files, such as HDF5 and NetCDF, which do not offer appropriate search capabilities. In this research, we investigated a special class of search capability, called membership query, to identify whether queried elements of a set are members of an attribute. Attributes that naturally have classification values appear frequently in scientific domains such as category and object type as well as in daily life such as zip code and occupation. Because classification attribute values are discrete and require random data access, performing a membership query on a large scientific data set creates challenges. We applied bitmap indexing and parallelization to membership queries to overcome these challenges. Bitmap indexing provides high performance not only for low cardinality attributes but also for high cardinality attributes, such as floating‐point variables, electric charge, or momentum in a particle physics data set, due to compression algorithms such as Word‐Aligned Hybrid. We conducted experiments, in a highly parallelized environment, on data obtained from a particle accelerator model and a synthetic data set.

Yu-Hang Tang, Wibe A. de Jong, "Prediction of atomization energy using graph kernel and active learning", The Journal of Chemical Physics, January 25, 2019, 150:044107, doi: 10.1063/1.5078640

Sean Peisert, Building a Mission-Driven, Applied Cybersecurity R&D Program from Scratch, VISA Research, January 23, 2019,

Stefano Marchesini, Anne Sakdinawat, "Shaping Coherent X-rays with Binary Optics", Optics Express Vol. 27, Issue 2, pp. 907-917 (2019), January 21, 2019,

Sebastian Götschel , Michael Minion, "An Efficient Parallel-in-Time Method for Optimization with Parabolic PDEs", SIAM Journal on Scientific Computing, January 21, 2019,

In submission

Oliver Rübel, Andrew Tritt, Benjamin Dichter, Thomas Braun, Nicholas Cain, Nathan Clack, Thomas J. Davidson, Max Dougherty, Jean-Christophe Fillion-Robin, Nile Graddis, Michael Grauer, Justin T. Kiggins, Lawrence Niu, Doruk Ozturk, William Schroeder, Ivan Soltesz, Friedrich T. Sommer, Karel Svoboda, Lydia Ng, Loren M. Frank, Kristofer Bouchard, "NWB:N 2.0: An Accessible Data Standard for Neurophysiology", bioRxiv, January 17, 2019, doi: https://doi.org/10.1101/523035

Scott B. Baden, Paul H. Hargrove, Hadia Ahmed, John Bachan, Dan Bonachea, Steve Hofmeyr, Mathias Jacquelin, Amir Kamil, Brian van Straalen, "Pagoda: Lightweight Communications and Global Address Space Support for Exascale Applications - UPC++", Poster at Exascale Computing Project (ECP) Annual Meeting 2019, January 2019,

Samuel Williams, Introduction to the Roofline Model, Roofline Tutorial, ECP Annual Meeting, January 2019,

Samuel Williams, Roofline on CPU-based Systems, Roofline Tutorial, ECP Annual Meeting, January 2019,

Jack Deslippe, Optimization Use Cases with the Roofline Model, Roofline Tutorial, ECP Annual Meeting, January 2019,

Charlene Yang, Performance Analysis with Roofline on GPUs, Roofline Tutorial, ECP Annual Meeting, January 2019,

George Michelogiannakis, Computation and Communication in a Post Moore’s Law Era, Post Exascale workshop part of HiPEAC conference, January 2019,

Scott B. Baden, Paul H. Hargrove, Dan Bonachea, "Pagoda: Lightweight Communications and Global Address Space Support for Exascale Applications - GASNet-EX", Poster at Exascale Computing Project (ECP) Annual Meeting 2019, January 2019,

M. Emmett, E. Motheau, W. Zhang, M. Minion, J. B. Bell, "A Fourth-Order Adaptive Mesh Refinement Algorithm for the Multicomponent, Reacting Compressible Navier-Stokes Equations", Combustion Theory and Modeling, 2019,

Screen Shot 2019 02 25 at 8.59.45 AM

Daniel F. Martin, Stephen L. Cornford, Antony J. Payne, "Millennial‐scale Vulnerability of the Antarctic Ice Sheet to Regional Ice Shelf Collapse", Geophysical Research Letters, January 9, 2019, doi: 10.1029/2018gl081229

Abstract: 

The Antarctic Ice Sheet (AIS) remains the largest uncertainty in projections of future sea level rise. A likely climate‐driven vulnerability of the AIS is thinning of floating ice shelves resulting from surface‐melt‐driven hydrofracture or incursion of relatively warm water into subshelf ocean cavities. The resulting melting, weakening, and potential ice‐shelf collapse reduces shelf buttressing effects. Upstream ice flow accelerates, causing thinning, grounding‐line retreat, and potential ice sheet collapse. While high‐resolution projections have been performed for localized Antarctic regions, full‐continent simulations have typically been limited to low‐resolution models. Here we quantify the vulnerability of the entire present‐day AIS to regional ice‐shelf collapse on millennial timescales treating relevant ice flow dynamics at the necessary ∼1km resolution. Collapse of any of the ice shelves dynamically connected to the West Antarctic Ice Sheet (WAIS) is sufficient to trigger ice sheet collapse in marine‐grounded portions of the WAIS. Vulnerability elsewhere appears limited to localized responses.

Plain Language Summary:

The biggest uncertainty in near‐future sea level rise (SLR) comes from the Antarctic Ice Sheet. Antarctic ice flows in relatively fast‐moving ice streams. At the ocean, ice flows into enormous floating ice shelves which push back on their feeder ice streams, buttressing them and slowing their flow. Melting and loss of ice shelves due to climate changes can result in faster‐flowing, thinning and retreating ice leading to accelerated rates of global sea level rise.To learn where Antarctica is vulnerable to ice‐shelf loss, we divided it into 14 sectors, applied extreme melting to each sector's floating ice shelves in turn, then ran our ice flow model 1000 years into the future for each case. We found three levels of vulnerability. The greatest vulnerability came from attacking any of the three ice shelves connected to West Antarctica, where much of the ice sits on bedrock lying below sea level. Those dramatic responses contributed around 2m of sea level rise. The second level came from four other sectors, each with a contribution between 0.5‐1m. The remaining sectors produced little to no contribution. We examined combinations of sectors, determining that sectors behave independently of each other for at least a century.

C. Varadharajan, S. Cholia, C. Snavely, V. Hendrix, C. Procopiou, D. Swantek, W. J. Riley, and D. A. Agarwal, "Launching an accessible archive of environmental data", Eos, 100, January 8, 2019, doi: https://doi.org/10.1029/2019EO111263

Mariam Kiran, Anshuman Chhabra, "Understanding flows in high-speed scientific networks: A Netflow data study", Future Generation Computer Science, 2019,

E. Y. Hsiao, M. M. Phiilips, G. H. Marion, R. P., N. Morrell, D. J. Sand, C. R. Burns, C., P. Hoeflich, M. D. Stritzinger, S., J. P. Anderson, C. Ashall, C. Baltay, E., D. P. K. Banerjee, S. Davis, T. R. Diamond, G., W. L. Freedman, F. Foerster, L., C. Gall, S. Gonzalez-Gaitan, A., M. Hamuy, S. Holmbo, M. M. Kasliwal, K., S. Kumar, C. Lidman, J. Lu, P. E., S. Perlmutter, S. E. Persson, A. L., D. Rabinowitz, M. Roth, S. D. Ryder, B. P., M. Shahbandeh, N. B. Suntzeff, F. Taddia, S. Uddin, L. Wang, Carnegie Supernova Project-II: The Near-infrared Spectroscopy Program, Publications of the ASP, Pages: 014002 2019, doi: 10.1088/1538-3873/aae961

M. M. Phillips, C. Contreras, E. Y. Hsiao, N., C. R. Burns, M. Stritzinger, C. Ashall, W. L., P. Hoeflich, S. E. Persson, A. L., N. B. Suntzeff, S. A. Uddin, J. Anais, E., L. Busta, A. Campillay, S. Castell\ on, C., T. Diamond, C. Gall, C. Gonzalez, S., K. Krisciunas, M. Roth, J. Ser\ on, F., S. Torres, J. P. Anderson, C. Baltay, G., L. Galbany, A. Goobar, E. Hadjiyska, M., M. Kasliwal, C. Lidman, P. E. Nugent, S., D. Rabinowitz, S. D. Ryder, B. P. Schmidt, B. J. Shappee, E. S. Walker, "Carnegie Supernova Project-II: Extending the Near-infrared Hubble Diagram for Type Ia Supernovae to z\nbsp\sim\nbsp0.1", Publications of the ASP, 2019, 131:014001, doi: 10.1088/1538-3873/aae8bd

O Karslıoğlu, M Gehlmann, J Müller, S Nemšák, JA Sethian, A Kaduwela, H Bluhm, C Fadley, "An Efficient Algorithm for Automatic Structure Optimization in X-ray Standing-Wave Experiments", Journal of Electron Spectroscopy and Related Phenomena, January 1, 2019,

J. Müller, M. Day, "Surrogate Optimization of Computationally Expensive Black-Box Problems with Hidden Constraints", INFORMS Journal on Computing, 2019, 31:633-845,

W. Langhans, J. Mueller, W.D. Collins, "Optimization of the Eddy-Diffusivity/Mass-Flux shallow cumulus and boundary-layer parametrization using surrogate models", Journal of Advances in Modeling Earth Systems (JAMES), Vol 11, Issue 2,, 2019,

Catherine A Watkinson, Sambit K. Giri, Hannah E. Ross, Keri L. Dixon, Ilian T. Iliev, Garrelt Mellema, Jonathan R. Pritchard, "The 21-cm bispectrum as a probe of non-Gaussianities due to X-ray heating", Monthly Notices of the Royal Astronomical Society, January 2019, 482:2653-2669, doi: 10.1093/mnras/sty2740

Y. Liu, W. Sid-Lakhdar, E. Rebrova, P. Ghysels, X. Sherry Li, "A parallel hierarchical blocked adaptive cross approximation algorithm", The International Journal of High Performance Computing Applications, January 1, 2019,

R. Oguz Selvitopi, Gunduz Vehbi Demirci, Ata Turk, Cevdet Aykanat, "Locality-aware and load-balanced static task scheduling for MapReduce", Future Generation Computer Systems (FGCS), January 2019, 90:49-61, doi: https://doi.org/10.1016/j.future.2018.06.035

Victor Yu, William Dawson, Alberto Garcia, Ville Havu, Ben Hourahine, William Huhn, Mathias Jacquelin, Weile Jia, Murat Keceli, Raul Laasner, others, Large-Scale Benchmark of Electronic Structure Solvers with the ELSI Infrastructure, Bulletin of the American Physical Society, 2019,

M. Del Ben, F.H. da Jornada, A. Canning, N. Wichmann, K. Raman, R. Sasanka, C. Yang, S.G. Louie, J. Deslippe, "Large-scale GW calculations on pre-exascale HPC systems", Computer Physics Communications, 2019, 235:187-195, doi: 10.1016/j.cpc.2018.09.003

Alina Lazar, Ling Jin, C Anna Spurlock, Kesheng Wu, Alex Sim, Annika Todd, "Evaluating the Effects of Missing Values and Mixed Data Types on Social Sequence Clustering Using t-SNE Visualization", Journal of Data and Information Quality (JDIQ), 2019, 11:7, doi: 10.1145/3301294

Sambit Shukla, Dipak Ghosal, Kesheng Wu, Alex Sim, Matthew Farrens, "Co-optimizing Latency and Energy for IoT services using HMP servers in Fog Clusters", 2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC), 2019, 121--128, doi: 10.1109/FMEC.2019.8795353

Francois P. Hamon, Martin Schreiber, Michael L. Minion, "Multi-Level Spectral Deferred Corrections Scheme for the Shallow Water Equations on the Rotating Sphere", Journal of Computational Physics, January 1, 2019, 376:435-454,

D Vasudevan, G Michclogiannakis, D Donofrio, J Shalf, "PARADISE - Post-Moore Architecture and Accelerator Design Space Exploration Using Device Level Simulation and Experiments", 2019 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), IEEE, January 2019, doi: 10.1109/ispass.2019.00022

W Cui, G Tzimpragos, Y Tao, J Mcmahan, D Dangwal, N Tsiskaridze, G Michelogiannakis, DP Vasudevan, T Sherwood, "Language Support for Navigating Architecture Design in Closed Form", ACM Journal on Emerging Technologies in Computing Systems, January 2019, 16:1--28, doi: 10.1145/3360047

Daan Camps, Karl Meerbergen, Raf Vandebril, "A rational QZ method", SIAM J. Matrix Anal. Appl., 2019, 40:943--972, doi: 10.1137/18M1170480

Daan Camps, Karl Meerbergen, Raf Vandebril, "An implicit filter for rational Krylov using core transformations", Linear Algebra Appl., 2019, 561:113--140, doi: 10.1016/j.laa.2018.09.021

Pole swapping methods for the eigenvalue problem - Rational QR algorithms, Daan Camps, 2019,

Daan Camps, Nicola Mastronardi, Raf Vandebril, Paul Van Dooren, "Swapping 2 × 2 blocks in the Schur and generalized Schur form", Journal of Computational and Applied Mathematics, 2019, doi: https://doi.org/10.1016/j.cam.2019.05.022

Junmin Gu, Burlen Loring, Kesheng Wu, E. Wes Bethel, HDF5 As a Vehicle for in Transit Data Movement, ISAV 19, Pages: 39--43 2019, doi: 10.1145/3364228.3364237