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1. Report on 2023 CyberTraining PI Meeting, 26-27 September 2023

Author

Fox, Geoffrey, Thomas, Mary P, Bhatia, Sajal, Brazil, Marisa, Gasparini, Nicole M, Merwade, Venkatesh Mohan, Neeman, Henry J., Carver, Jeff, Casanova, Henri, Chaudhary, Vipin, Colbry, Dirk, Crosby, Lonnie, Dewan, Prasun, Eisma, Jessica, Irfan, Ahmed, Kaehey, Kate, Liu, Qianqian, Ni, Zhen, Prasad, Sushil, Qasem, Apan, Saule, Erik, Sundaravadivel, Prabha, and Tomko, Karen

Subjects
Computer Science - Cryptography and Security
Abstract

This document describes a two-day meeting held for the Principal Investigators (PIs) of NSF CyberTraining grants. The report covers invited talks, panels, and six breakout sessions. The meeting involved over 80 PIs and NSF program managers (PMs). The lessons recorded in detail in the report are a wealth of information that could help current and future PIs, as well as NSF PMs, understand the future directions suggested by the PI community. The meeting was held simultaneously with that of the PIs of the NSF Cyberinfrastructure for Sustained Scientific Innovation (CSSI) program. This co-location led to two joint sessions: one with NSF speakers and the other on broader impact. Further, the joint poster and refreshment sessions benefited from the interactions between CSSI and CyberTraining PIs., Comment: 38 pages, 3 main sections and 2 Appendix sections, 2 figures, 19 tables; updated version: author corrections

Published
2023

2. Machine Learning in High Energy Physics Community White Paper

Author

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

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

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

Published
2018

3. GooFit 2.0

Author

Schreiner, Henry, Hasse, Christoph, Hittle, Bradley, Pandey, Himadri, Sokoloff, Michael, and Tomko, Karen

Subjects
Computer Science - Mathematical Software, High Energy Physics - Experiment, Physics - Data Analysis, Statistics and Probability
Abstract

The GooFit package provides physicists a simple, familiar syntax for manipulating probability density functions and performing fits, and is highly optimized for data analysis on NVIDIA GPUs and multithreaded CPU backends. GooFit was updated to version 2.0, bringing a host of new features. A completely revamped and redesigned build system makes GooFit easier to install, develop with, and run on virtually any system. Unit testing, continuous integration, and advanced logging options are improving the stability and reliability of the system. Developing new PDFs now uses standard CUDA terminology and provides a lower barrier for new users. The system now has built-in support for multiple graphics cards or nodes using MPI, and is being tested on a wide range of different systems. GooFit also has significant improvements in performance on some GPU architectures due to optimized memory access. Support for time-dependent four-body amplitude analyses has also been added., Comment: Submitted to the ACAT 2017 proceedings, 6 pages

Published
2017

4. Enabling Continuous Testing of HPC Systems Using ReFrame

Author

Karakasis, Vasileios, Manitaras, Theofilos, Rusu, Victor Holanda, Sarmiento-Pérez, Rafael, Bignamini, Christopher, Kraushaar, Matthias, Jocksch, Andreas, Omlin, Samuel, Peretti-Pezzi, Guilherme, Augusto, João P. S. C., Friesen, Brian, He, Yun, Gerhardt, Lisa, Cook, Brandon, You, Zhi-Qiang, Khuvis, Samuel, Tomko, Karen, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Juckeland, Guido, editor, and Chandrasekaran, Sunita, editor

Published
2020
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5. Designing Non-blocking Personalized Collectives with Near Perfect Overlap for RDMA-Enabled Clusters

Author

Subramoni, Hari, Awan, Ammar Ahmad, Hamidouche, Khaled, Pekurovsky, Dmitry, Venkatesh, Akshay, Chakraborty, Sourav, Tomko, Karen, Panda, Dhabaleswar K., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Kunkel, Julian M., editor, and Ludwig, Thomas, editor

Published
2015
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6. Creating intelligent cyberinfrastructure for democratizing AI.

Author

Panda, Dhabaleswar K., Chaudhary, Vipin, Fosler‐Lussier, Eric, Machiraju, Raghu, Majumdar, Amit, Plale, Beth, Ramnath, Rajiv, Sadayappan, Ponnuswamy, Savardekar, Neelima, and Tomko, Karen

Subjects
CYBERINFRASTRUCTURE, ARTIFICIAL intelligence, ANIMAL ecology, CLASSROOM environment
Abstract

Artificial intelligence (AI) has the potential for vast societal and economic gain; yet applications are developed in a largely ad hoc manner, lacking coherent, standardized, modular, and reusable infrastructures. The NSF‐funded Intelligent CyberInfrastructure with Computational Learning in the Environment AI Institute ("ICICLE") aims to fundamentally advance edge‐to‐center, AI‐as‐a‐Service, achieved through intelligent cyberinfrastructure (CI) that spans the edge‐cloud‐HPC computing continuum, plug‐and‐play next‐generation AI and intelligent CI services, and a commitment to design for broad accessibility and widespread benefit. This design is foundational to the institute's commitment to democratizing AI. The institute's CI activities are informed by three high‐impact domains: animal ecology, digital agriculture, and smart foodsheds. The institute's workforce development and broadening participation in computing efforts reinforce the institute's commitment to democratizing AI. ICICLE seeks to serve as the national nexus for AI and intelligent CI, and welcomes engagement across its wide set of programs. [ABSTRACT FROM AUTHOR]

Published
2024
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7. Designing Scalable Graph500 Benchmark with Hybrid MPI+OpenSHMEM Programming Models

Author

Jose, Jithin, Potluri, Sreeram, Tomko, Karen, Panda, Dhabaleswar K., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Kunkel, Julian Martin, editor, Ludwig, Thomas, editor, and Meuer, Hans Werner, editor

Published
2013
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8. Lightning Talks of EduHPC 2022

Author

Qasem, Apan, primary, Anzt, Hartwig, additional, Ayguade, Eduard, additional, Cahill, Katharine, additional, Canal, Ramon, additional, Chan, Jany, additional, Fosler-Lussier, Eric, additional, Gobel, Fritz, additional, Jain, Arpan, additional, Koch, Marcel, additional, Kuzak, Mateusz, additional, Llosa, Josep, additional, Machiraju, Raghu, additional, Martorell, Xavier, additional, Nayak, Pratik, additional, Oottikkal, Shameema, additional, Ostasz, Marcin, additional, Panda, Dhabaleswar K., additional, Pleiter, Dirk, additional, Ramnath, Rajiv, additional, Sancho, Maria-Ribera, additional, Sclocco, Alessio, additional, Shafi, Aamir, additional, Spreeuw, Hanno, additional, Subramoni, Hari, additional, and Tomko, Karen, additional

Published
2022
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10. A Python upgrade to the GooFit package for parallel fitting

Author

Schreiner Henry, Pandey Himadri, D Sokoloff Michael, Hittle Bradley, Tomko Karen, and Hasse Christoph

Subjects
Physics, QC1-999
Abstract

The GooFit highly parallel fitting package for GPUs and CPUs has been substantially upgraded in the past year. Python bindings have been added to allow simple access to the fitting configuration, setup, and execution. A Python tool to write custom GooFit code given a (compact and elegant) MINT3/AmpGen amplitude description allows the corresponding C++ code to be written quickly and correctly. New PDFs have been added. The most recent release was built on top of the December 2017 2.0 release that added easier builds, new platforms, and a more robust and efficient underlying function evaluation engine.

Published
2019
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11. Lightning Talks of EduHPC 2022

Author

Qasem, Apan, Anzt, Hartwig, Ayguade, Eduard, Cahill, Katharine, Canal, Ramon, Chan, Jany, Fosler-Lussier, Eric, Goebel, Fritz, Jain, Arpan, Koch, Marcel, Kuzak, Mateusz, Llosa, Josep, Machiraju, Raghu, Martorell, Xavier, Nayak, Pratik, Oottikkal, Shameema, Ostasz, Marcin, Panda, Dhabaleswar K., Pleiter, Dirk, Ramnath, Rajiv, Sancho, Maria-Ribera, Sclocco, Alessio, Shafi, Aamir, Spreeuw, Hanno, Subramoni, Hari, Tomko, Karen, Qasem, Apan, Anzt, Hartwig, Ayguade, Eduard, Cahill, Katharine, Canal, Ramon, Chan, Jany, Fosler-Lussier, Eric, Goebel, Fritz, Jain, Arpan, Koch, Marcel, Kuzak, Mateusz, Llosa, Josep, Machiraju, Raghu, Martorell, Xavier, Nayak, Pratik, Oottikkal, Shameema, Ostasz, Marcin, Panda, Dhabaleswar K., Pleiter, Dirk, Ramnath, Rajiv, Sancho, Maria-Ribera, Sclocco, Alessio, Shafi, Aamir, Spreeuw, Hanno, Subramoni, Hari, and Tomko, Karen

Abstract

The lightning talks at EduHPC provide an opportunity to share early results and insights on parallel and distributed computing (PDC) education and training efforts. The four lightning talks at EduHPC 2022 cover a range of topics in broadening PDC education: (i) curriculum development efforts for the European Masters in HPC program, (ii) bootcamps for CI professionals who support the running of AI workloads on HPC systems, (iii) a GPU programming course following the Carpentries model and (iv) peer-review assignments to help students write efficient parallel algorithms within sustainable software libraries., QC 20230508

Published
2022
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15. Machine Learning in High Energy Physics Community White Paper

Author

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

Abstract

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

Published
2021

17. Enhanced reliability of finite-state machines in FPGA through efficient fault detection and correction

Author

Tiwari, Anurag and Tomko,Karen A.

Subjects
Digital integrated circuits -- Analysis, Random access memory -- Analysis, Programmable logic array, RAM, Business, Electronics, Electronics and electrical industries
Abstract

Synchronous random access memory (SRAM) based field programmable gate arrays (FPGA) are subjected to ion radiation in many operating environments. Following the trend of shrinking device feature size and increasing die area, newer are more susceptible to radiation-induced errors.

Published
2005

21. Fostering Collaboration Among Organizations in the Research Computing and Data Ecosystem

Author

Broude Geva, Sharon, primary, Brunson, Dana, additional, Cheatham III, Thomas, additional, Deaton, James, additional, Griffioen, James, additional, Hillegas, Curtis W., additional, Jennewein, Douglas M., additional, Krovitz, Gail, additional, Magle, Tobin, additional, Schmitz, Patrick, additional, Tomko, Karen, additional, and Wilgenbusch, James C., additional

Published
2020
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22. SI2-SSI: FAMII: High Performance and Scalable Fabric Analysis, Monitoring and Introspection Infrastructure for HPC and Big Data

Author

Subramoni, Hari, Panda, Dhabaleswar K., and Tomko, Karen

Abstract

As heterogeneous computing (CPUs, GPUs etc.) and , networking (NVLinks, X-Bus etc.) hardware continue to advance, it becomes increasingly essential and challenging to understand the interactions between High-Performance Computing (HPC) and Deep Learning applications/frameworks, the communication middleware they rely on, the underlying communication fabric these high-performance middlewares depend on, and the schedulers that manage HPC clusters. Such understanding will enable application developers/users, system administrators, and middleware developers to maximize the efficiency and performance of individual components that comprise a modern HPC system and solve different grand challenge problems. Moreover, determining the root cause of performance degradation is complex for the domain scientist. The scale of emerging HPC clusters further exacerbates the problem. These issues lead to the following broad challenge: How can we design a tool that enables in-depth understanding of the communication traffic on the interconnect and GPU through tight integration with the MPI runtime at scale?

Published
2020
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23. CSSI Frameworks: Designing Next-Generation MPI Libraries for Emerging Dense GPU Systems

Author

Panda, Dhabaleswar K., Majumdar, Amitava, Barth, Bill, and Tomko, Karen

Subjects
Scalability Performance, Datatypes, Deep Learning, CUDA-aware I/O, NSF-CSSI-2022-Poster, GPU, CUDA-aware MPI, MPI
Abstract

Modern HPC platforms are using multiple CPU, GPUs and high-performance interconnects per node. Unfortunately, state-of-the-art production quality implementations of the popular Message Passing Interface (MPI) programming model do not have the appropriate support to deliver the best performance and scalability for applications (HPC and DL) on such dense GPU systems. The project involves a synergistic and comprehensive research plan, involving computer scientists from OSU and OSC and computational scientists from TACC, SDSC and UCSD. The proposed innovations include: 1) Designing high-performance and scalable communication operations that fully utilize multiple network adapters and advanced in-network computing features for GPU and CPU; 2) Designing novel datatype processing and unified memory management; 3) Designing CUDA-aware I/O; 4) Designing support for containerized environments; and 5) Carrying out integrated evaluation with a set of driving applications. Initial results from this project using the MVAPICH2 MPI library will be presented., NSF Award #: OAC-1931537

Published
2020
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25. Improving the Development Workflow of the SETSM Photogrammetry Software

Author

Khuvis, Samuel, Gardiner, Judith, Howat, Ian, Lehman, Caleb, Myoung-Jong Noh, and Tomko, Karen

Abstract

The Surface Extraction by TIN-based Search-space Minimization (SETSM) software is used to produce digital elevation maps (DEMs) from satellite imagery. SETSM has been used for both the ArcticDEM and REMA projects to produce high-resolution terrain maps for the polar regions. The goal of SETSM is to automatically extract a stereo-photogrammetric DEM from pairs of images without any user-defined or a-priori information and using only the sensor Rational Polynomial Coefficients for geometric constraints. The software is written entirely in C and is only dependent on the libtiff and libgeotiff libraries. The SETSM algorithm constructs a Triangular Irregular Network (TIN) in object-space domain to minimize the necessary search space and it employs the coarse-to-fine and vertical line locus strategies.In this poster we present some of the challenges and solutions involved in optimization of a code that is also concurrently under active development by domain scientists. We discuss the challenges of using continuous integration to move toward automated testing. We also present methods to improve the maintainability and usability of this code. One example, code modularization allows for the testing of different algorithms and makes concurrent development by computer scientists and photogrammetry experts more manageable.

Published
2019
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27. SI2-SSI (2018): FAMII: High-Performance and Scalable Fabric Analysis, Monitoring and Introspection Infrastructure for HPC and Big Data

Author

Subramoni, Hari, Panda, Dhabaleswar K., Tomko, Karen, and Xiaoyi Lu

Abstract

The proposed research aims to create an integrated softwareinfrastructure for high-performance and scalable FabricAnalysis, Monitoring and Introspection for HPC and Big Data.This tool achieves the following objectives: 1) beportable, easy to use and easy to understand, 2) have highperformance and scalable rendering and storage techniquesand, 3) be applicable to the different communication fabricsand programming models that are likely to be used onexisting large HPC systems and emerging exascale systems.The transformative impact of the proposed research anddevelopment effort is to design a comprehensive analysis andperformance monitoring tool for applications of current andnext generation multi petascale/exascale systems to harnessthe maximum performance and scalability.

Published
2018
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28. GooFit 2.0

Author

Schreiner, Henry, primary, Hasse, Christoph, additional, Hittle, Bradley, additional, Pandey, Himadri, additional, Sokoloff, Michael, additional, and Tomko, Karen, additional

Published
2018
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29. Machine Learning in High Energy Physics Community White Paper

Author

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

Published
2018
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34. Scalable Graph500 design with MPI-3 RMA

Author

Li, Mingzhe, primary, Lu, Xiaoyi, additional, Potluri, Sreeram, additional, Hamidouche, Khaled, additional, Jose, Jithin, additional, Tomko, Karen, additional, and Panda, Dhabaleswar K., additional

Published
2014
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40. Codesign for InfiniBand Clusters

Author

Sur, Sayantan, primary, Potluri, Sreeram, additional, Kandalla, Krishna Chaitanya, additional, Subramoni, Hari, additional, Panda, Dhabaleswar K., additional, and Tomko, Karen, additional

Published
2011
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49. Dynamic reconfiguration to support concurrent applications

Author

Jean, Jack S.N., Tomko, Karen, Yavagal, Vikram, Shah, Jignesh, and Cook, Robert

Subjects
Programmable array logic -- Research, Programmable logic devices -- Research, Gate arrays -- Research, Parallel processing -- Research
Published
1999

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