IPDPS 2024 Call for Papers

38th IEEE International Parallel &
Distributed Processing Symposium
May 27-31, 2024

Authors are invited to submit manuscripts that present novel and impactful research in all areas of parallel and distributed processing. Works focusing on emerging technologies, interdisciplinary work spanning multiple IPDPS focus areas, and novel open-source artifacts are welcome. Topics of interest include but are not limited to the following areas:

Parallel and Distributed Algorithms for Computational Science:
This track focuses on parallel and distributed (to include cloud, edge, and fog computing) algorithms arising in the context of execution of computational science methods. Examples of computations forming these workloads include structured and unstructured grids, dense and sparse linear algebra computations, spectral methods, and n-body computations. Also included in this track are algorithmic and theory contributions that are workload agnostic but specific to tightly coupled systems, such as those supporting communication, synchronization, and power management.  

Parallel and Distributed Algorithms for Data Science:
This track focuses on parallel and distributed (to include cloud, edge, and fog computing) algorithms arising in the context of execution of data science methods, including machine learning, data mining, graph computations, clustering, visualization, and other forms of data analytic methods. Also included in this track are algorithmic and theory contributions that are workload agnostic but specific to loosely coupled systems, such as those for management of distributed resources, and those related to distributed data and transactions as well as mobility. 

Applications:
The focus of this track is on papers that develop applications to solve problems using parallel and distributed computing concepts. Papers submitted to this track are expected to incorporate considerations specific to the target application area.Topics may include the design, implementation, and evaluation of parallel and distributed applications. If the primary focus is on novel or generalizable methodologies for modeling, analyzing, or evaluating performance, the paper might be better suited to the  to the "Measurements, Modeling, and Experiments" track.

Programming Models, Compilers, and Runtime Systems:
This track covers topics ranging from the design of parallel programming models and paradigms to languages and compilers supporting these models and paradigms, to runtime and middleware solutions. Software that is close to the application (as opposed to the bare hardware) but not specific to an application is included – examples include frameworks targeting cloud and distributed systems; application frameworks for fault tolerance and resilience; software supporting data management, scalable data analytics and similar workloads, and runtime systems for future novel computing platforms including quantum, neuromorphic, and bio-inspired computing.

System Software:
This track focuses on software that is close to the bare hardware. Topics include storage and I/O systems; system software for resource management, job scheduling, and energy-efficiency; system software support for accelerators and heterogeneous HPC computing systems; interactions between the OS and the hardware with other software layers; system software support for fault tolerance and resilience; containers and virtual machines; specialized operating systems and related support for high performance computing; and system software for future novel computing platforms including quantum, neuromorphic, and bio-inspired computing.

Architecture:
This track focuses on studies related to both existing and emerging architectures, including architectures for instruction-level and thread-level parallelism; manycore, multicores, accelerators, domain-specific and special-purpose architectures, reconfigurable architectures;  memory technologies and hierarchies; volatile and non-volatile emerging memory technologies, solid-state devices; exascale system designs; data center and warehouse-scale architectures; novel big data architectures; network and interconnect architectures; emerging technologies for interconnects; parallel I/O and storage systems; power-efficient and green computing systems; resilience, security, and dependable architectures; emerging architectural trends for machine learning, approximate computing, quantum computing, neuromorphic, analog, and bio-inspired computing.

Measurements, Modeling, and Experiments:
This track focuses on experiments and performance-oriented studies in the practice of parallel and distributed computing. “Performance” may be construed broadly to include metrics related to time, energy, power, accuracy, and resilience, for instance. Topics include: methods, experiments, and tools for measuring, evaluating, and/or analyzing performance for large-scale applications and systems; design and experimental evaluation of applications of parallel and distributed computing in simulation and analysis; experiments on the use of novel commercial or research architectures, accelerators, quantum and neuromorphic architectures, and other non-traditional systems;  innovations made in support of large-scale infrastructures and facilities; and methods for and experiences allocating and managing system and facility resources.

Best Paper Award

The program committee will select a small set of top-quality papers as best paper finalists, and one paper as the winner, for recognition with the Best Paper Award. 

Best Open-Source Contribution Award 

IPDPS welcomes submissions with open-source tool and dataset artifacts, relevant to the parallel and distributed computing community, as one of their technical contributions. The authors of accepted papers will be encouraged to identify if they wish their submissions to be considered for the best open-source contribution award. Such papers will be evaluated by a dedicated open-source tool and dataset artifacts committee. A small set of such papers will be identified as the best open-source contribution paper finalists as appropriate and applicable based on the quality of the contribution. One paper may be selected as the winner among the finalists, for recognition with the Best Open-Source Contribution Award, depending upon the contribution level and the recommendation from the committee. The two award categories are not exclusive (a paper can be nominated for both the best paper award and best open-source contribution award).

WHAT/WHERE TO SUBMIT

Abstracts of at most 500 words must be submitted by September 28, 2023. Manuscripts must be submitted by October 5, 2023; submitted manuscripts may not exceed ten (10) single-spaced double-column pages using 10-point size font on 8.5x11-inch pages (IEEE conference style), including all figures and tables. There is no page limit for references, which must be complete and include all author names. The submitted manuscripts should not include author names and affiliations, or otherwise disclose the identity of the authors, because a double-blind review process will be followed.

The IEEE conference style templates for MS Word and LaTeX provided by IEEE eXpress Conference Publishing are available for download. See the latest versions here.

Files should be submitted by following the instructions at the IPDPS 2024 Submission Site (powered by Linklings). Click here to submit abstract and register your paper by September 28th.

A “primary” track must be marked, and an optional “secondary” track may also be specified.

REVIEW OF MANUSCRIPTS

All submitted manuscripts will be reviewed by the Program Committee under a double-blind review process. Submissions will be judged on correctness, originality, technical strength, significance, potential impact, quality of presentation, and interest and relevance to the conference scope. Submitted manuscripts should NOT have appeared in or be under consideration for another conference, workshop, or journal.

A high-quality submission should articulate its contributions in multiple aspects:

• Motivation. Clearly state the objective of the paper and provide strong support to motivate the specific problem the submission is solving.
• Limitations of state-of-art approaches. Unambiguously discuss and distinguish from the most relevant and most recent prior works. 
• Key insights and contributions. Clearly articulate the major insights that enable the described approach or make it effective. Clearly specify the novelty of these insights and how they advance state-of-the-art. Provide a list of key contributions including flagship theoretical or experimental results and improvement over the prior art, as applicable. 
• Methodology. Clearly specify the key theoretical or experimental methodological details, as applicable. Support the chosen methodological choices (e.g., cite that most relevant and most recent prior works have evaluated their ideas using similar methodology).  If new methodology is adopted or theoretical assumptions different from prior art are made, a detailed justification should be provided. 
• Limitations of the proposed approach. As applicable, articulate all the major limitations of the proposed approach and identify conclusions that are sensitive to specific assumptions made in the paper.

The Program Committee will be encouraged to assess the submissions in the above aspects. Therefore, the authors should consider making these aspects clear and easily identifiable, as possible, when articulating their contributions. We hope this will help improve both the review-quality (author experience) and reviewing-experience. 

Authors will have the opportunity to respond to the reviewers’ questions and provide clarifications before the first-round decisions are made. Note that not all submissions may be invited to submit a response/rebuttal. The submissions that are not invited to submit a response/rebuttal will be notified with an early-reject decision by December 4, 2023.

Questions may be sent to pc2024@ipdps.org. Abstracts are due September 28, 2023, and full manuscripts must be received by October 5, 2023. This is a final, hard deadline. To ensure fairness, no extensions will be given.

Preliminary decisions will be sent by December 18, 2023, with a decision of either “accept”, “revise”, or “reject”. Authors of papers in the “revise” decision will have the opportunity to submit a new version addressing reviewers’ comments.  Such a revised submission will be due on January 18, 2024, with a cover letter explaining the changes. The ensuing review process for such submissions will result in decisions of either “accept” or “reject”, the latter for the cases where the reviewers assess that the issues they raised were not satisfactorily and sufficiently addressed. Notification of final decisions will be mailed by January 30, 2024, and camera-ready papers will be due on February 22, 2024 February 29, 2024.

ArXiv Submission Policy

Having an arXiv paper does not prohibit authors from submitting a paper to IPDPS 2024. arXiv papers are not peer-reviewed and not considered as formal publications, hence do not count as prior work. Authors are not expected to compare against arXiv papers that have not formally appeared in previous conference or journal proceedings. If a submitted paper is already on arXiv, please continue to follow the double-blind submission guidelines. Authors are encouraged to use preventive measures to reduce the chances of accidental breach of anonymity (e.g., use a different title in the submission, not upload/revise the arXiv version during the review period after the submission deadline).

Guidance on Artificial Intelligence (AI)-Generated Text (e.g., ChatGPT)

IPDPS will allow the use of tools like ChatGPT, Grammarly, or other AI assistants to help improve the submission text. We recommend that you review your submission for language issues through these services. It is not mandatory to use these external services and you should judge if the results are satisfactory. As required by IEEE, the use of any AI-generated text shall be disclosed in the acknowledgements section. The sections of the paper that use AI-generated text shall have a citation to the AI system used to generate the text.

Inclusive Description of Research Contributions

Please consider making your research contribution description inclusive in nature. For example, consider using gender-neutral pronouns, consider using examples that are ethnicity/culture-rich, consider engaging users from diverse backgrounds if your research involves a survey, etc. Best efforts should be made to make the paper accessible to visually impaired or color-blind readers.

IPDPS 2024 IMPORTANT DATES

• Abstract submissions:  Thursday, September 28, 2023
• Full manuscript submissions (double-blind): Thursday, October 5, 2023 - FIRM DEADLINE 
• Author response/rebuttal to reviews:  Monday, December 4 – Thursday, December 7, 2023
• 1st round decisions: Tuesday, December 19, 2023
• Revised submissions due: Thursday, January 18, 2024
• Final decisions:  Tuesday, January 30, 2024
• Camera-ready versions due: Thursday, February 22, 2024 February 29, 2024

IPDPS 2024 PROGRAM CHAIRS

• P. (Saday) Sadayapan, University of Utah
• Richard Vuduc, Georgia Institute of Technology

2024 PROGRAM AREA CO-CHAIRS

Parallel and Distributed Algorithms for Computational Science:
Amanda Randles, Duke University, USA
Edgar Solomonik, University of Illinois at Urbana-Champaign, USA

Parallel and Distributed Algorithms for Data Science:
Ariful Azad, Indiana University Bloomington , USA
Rio Yokota, Tokyo Institute of Technology, Japan

Applications:
Sivasankaran Rajamanickam, Sandia National Laboratory, USA
Hari Sundar, University of Utah , USA

Programming Models, Compilers, and Runtime Systems:
Frank Mueller, North Carolina State University, USA
Fabrice Rastello, INRIA Grenoble, France

System Software:
Ron Brightwell, Sandia National Laboratories, USA
Trilce Estrada, University of New Mexico, USA

Architecture:
Maya Gokhale, Lawrence Livermore National Laboratory, USA
Xiaoyi Lu, University of California, Merced, USA

Measurements, Modeling, and Experiments:
Georg Hager, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
Amelie Chi Zhou, Hong Kong Baptist University, Hong Kong

TECHNICAL PROGRAM COMMITTEE MEMBERS

(Posted 6 January 2024*)

Parallel and Distributed Algorithms for Computational Science
Bilge Acun, Facebook AI Research
Metin Aktulga, Michigan State University
Erik Boman, Sandia National Laboratories
Joshua D. Booth, University of Alabama, Huntsville
Jon Calhoun, Clemson University
Qinglei Cao, Cerebras System
Kazem Cheshmi, University of Toronto
Steven W. D. Chien, University of Edinburgh
Jee Choi, University of Oregon
Anthony Danalis, University of Tennessee
Daniele De Sensi, Sapienza University of Rome
Nikoli Dryden, ETH Zürich and Lawrence Livermore National Laboratory
Thomas Dufaud, University of Versailles
S M Ferdous, Pacific Northwest National Laboratory
Takeshi Fukaya, Hokkaido University, Japan
Sayan Ghosh, Pacific Northwest National Laboratory (PNNL)
Pieter Ghysels, Lawrence Berkeley National Laboratory (LBNL)
Laura Grigori, EPFL
Hanqi Guo, The Ohio State University
Jeff R. Hammond, NVIDIA Helsinki Oy
Thomas Herault, University of Tennessee, Knoxville
Jian Huang, University of Tennessee, Knoxville
Nikhil Jain, NVIDIA Corporation
Peng Jiang, University of Iowa
Minming Li, City University of Hong Kong
Yang Liu, Lawrence Berkeley National Laboratory (LBNL)
Weifeng Liu, China University of Petroleum, Beijing
Vanessa Lopez-Marrero, Brookhaven National Laboratory; Stony Brook University, Institute for Advanced Computational Science (IACS)
Hatem Ltaief, King Abdullah University of Science and Technology (KAUST)
Josh Milthorpe, Oak Ridge National Laboratory (ORNL) and Australian National University
Kengo Nakajima, University of Tokyo and RIKEN
Saumil Patel, Argonne National Laboratory
Cynthia Phillips, Sandia National Laboratories
Jason Riedy, Lucata Corp.
Jon Rood, National Renewable Energy Laboratory (NREL)
Erik Saule, University of North Carolina, Charlotte
Olaf Schenk, University of Lugano
Yihan Sun, University of California, Riverside
Bora UCAR, French National Center for Scientific Research (CNRS)
Zeke Wang, Zhejiang University
Helen Xu, Lawrence Berkeley National Laboratory
Ichitaro Yamazaki, Sandia National Laboratories 
Albert-Jan Yzelman, Huawei Technologies Switzerland AG

Parallel and Distributed Algorithms for Data Science
Mustafa Abduljabbar, The Ohio State University
Gabriel Antoniu, French Institute for Research in Computer Science and Automation (INRIA)
Shaikh Arifuzzaman, University of Nevada, Las Vegas
Grey Ballard, Wake Forest University
Dip Sankar Banerjee, Indian Institute of Technology Jodhpur
Giuseppe M. J. Barca, Australian National University
George Biros, University of Texas, Oden Institute
Dazhao Cheng, Wuhan University
Rezaul Chowdhury, Stony Brook University
Alexandru Costan, French Institute for Research in Computer Science and Automation (INRIA)
Laxman Dhulipala, University of Maryland
Funda Ergun, Indiana University
Pierre Fraigniaud, CNRS and Université Paris Cité
Assefaw Gebremedhin, Washington State University
Wojciech Golab, University of Waterloo
Oded Green, NVIDIA Corporation and Georgia Institute of Technology
Giulia Guidi, Cornell University and Lawrence Berkeley National Laboratory (LBNL
Leszek Gąsieniec, University of Liverpool
Mahantesh Halappanavar, Pacific Northwest National Laboratory (PNNL)
Ali Jannesari, Iowa State University
Klaus Jansen, University of Kiel
Humayun Kabir, Microsoft
Christos Kaklamanis, University of Patras and CTI "Diophantus"
Ananth Kalyanaraman, Washington State University
Ramakrishnan Kannan, Georgia Institute of Technology
Oguz Kaya, Laboratoire de Recherche en Informatique (LRI) at Université Paris-Sud/Paris-Saclay
Mariam Kiran, Lawrence Berkeley National Laboratory
Penporn Koanantakool, Google (Thailand) Company Limited
Pavel Kromer, VSB - Technical University of Ostrava
Sanmukh Kuppannagari, Case Western Reserve University
Johannes Langguth, Simula Research Laboratory and University of Bergen, Norway
Shigang Li, Beijing University of Posts and Telecommunications
Jiajia Li, North Carolina State University
Zhengchun Liu, Argonne National Laboratory (ANL) and University of Chicago
Hang Liu, Rutgers, The State University of New Jersey
Kamesh Madduri, Pennsylvania State University
Tanu Malik, DePaul University
Henning Meyerhenke, Humboldt University of Berlin
Anisur Rahaman Molla, Indian Statistical Institute
Kaushik Mondal, Indian Institute of Technology Ropar
Adel N. Toosi, Monash University
Israt Nisa, AWS AI Research and Education
Wei Niu, University of Georgia
Prashant Pandey, University of Utah
Gopal Pandurangan, University of Houston
Miquel Pericas, Chalmers University of Technology, Sweden
Sushil K. Prasad, University of Texas, San Antonio
Radu Prodan, University of Klagenfurt, Austria
Yogish Sabharwal, IBM India Research Laboratory
Piyush Sao, Oak Ridge National Laboratory (ORNL) and Georgia Institute of Technology
Gokarna Sharma, Kent State University
Prateek Sharma, Indiana University
Dingwen Tao, Indiana University
Ramachandran Vaidyanathan, Louisiana State University
Flavio Vella, University of Trento, Italy
Abhinav Vishnu, Advanced Micro Devices (AMD) Inc
Chen Wang, IBM Research
Wei Xu, Brookhaven National Lab
Da Yan, Indiana University Bloomington and University of Alabama at Birmingham
Junqi Yin, Oak Ridge National Laboratory
Yujia Zhai, NVIDIA Corporation

Applications
Sivaram Ambikasaran, Indian Institute of Technology Madras
Michael Bader, Technical University Munich
Peter Balogh, New Jersey Institute of Technology
Jay Bardhan, Pacific Northwest National Laboratory (PNNL)
Sanjukta Bhowmick, University of North Texas
Chao Chen, The University of Texas at Austin, Oden Institute for Computational Sciences
Milinda Fernando, The University of Texas at Austin
Lin Gan, Tsinghua University, China; National Supercomputing Center in Wuxi
Sandra Gesing, University of Notre Dame; Discovery Partner Institute, University of Illinois Chicago
Ammar Hakim, Princeton Plasma Physics Laboratory
Toshiyuki Imamura, RIKEN Center for Computational Science (R-CCS)
Balint Joo, Oak Ridge National Laboratory (ORNL)
Andreas Kloeckner, University of Illinois
Hemanth Kolla, Sandia National Laboratories
Adarsh Krishnamurthy, Iowa State University
Harald Köstler, University of Erlangen-Nuremberg
Ying Wai Li, Los Alamos National Laboratory (LANL)
Kim Liegeois, Sandia National Laboratories
Meifeng Lin, Brookhaven National Laboratory
Paul Lin, Lawrence Berkeley National Laboratory (LBNL)
Dhairya Malhotra, New York University
Andreas Mang, University of Houston
Takemasa Miyoshi, RIKEN
Johann Rudi, Virginia Tech
Karl Schulz, University of Texas
Ada Sedova, Oak Ridge National Lab
Oguz Selvitopi, Lawrence Berkeley National Laboratory
Sarat Sreepathi, Oak Ridge National Laboratory
Daisuke Takahashi, University of Tsukuba
Valerie Taylor, Argonne National Laboratory (ANL)
George Teodoro, Universidade de Minas Gerais
Stephen Thomas, Advanced Micro Devices (AMD) Inc
Edward F. Valeev, Virginia Tech
Chao Yang, Peking University, Chinese Academy of Sciences
Chao Yang, Lawrence Berkeley National Laboratory

Programming Models, Compilers, and Runtime Systems
Swarnendu Biswas, Indian Institute of Technology Kanpur
Milind Chabbi, Uber Technologies Inc, Scalable Systems Research Labs
Yue Cheng, University of Virginia
Camille Coti, École de Technologie Supérieure (ÉTS Montréal)
Guillaume Iooss, INRIA
Ignacio Laguna, Lawrence Livermore National Laboratory
Seyong Lee, Oak Ridge National Laboratory (ORNL)
Jaejin Lee, Seoul National University
Ang Li, Pacific Northwest National Laboratory (PNNL)
Peiming Liu, Google, Research
Xu Liu, North Carolina State University and Oak Ridge National Laboratory (ORNL)
John Owens, University of California, Davis
Onkar Patil, IBM Corporation
Keshav Pingali, University of Texas
Bin Ren, William & Mary
Martin Schulz, Technical University Munich, Computer Architecture and Parallel Systems; Leibniz Supercomputing Centre
Aamir Shafi, Ohio State University
Jesper Larsson Träff, Technical University Wien (Vienna University of Technology)
Frédéric Vivien, French Institute for Research in Computer Science and Automation (INRIA)
Tao Wang, Stanford University
Bo Wu, Colorado School of Mines
Bing Xie, Microsoft
Jingling Xue, UNSW Sydney
Jidong Zhai, Tsinghua University, China
Xuechen Zhang, Washington State University, Vancouver

System Software
Abhinav Bhatele, University of Maryland
Amanda J. Bienz, University of New Mexico
George Bosilca, University of Tennessee
Patrick Bridges, University of New Mexico
Suren Byna, The Ohio State University and Lawrence Berkeley National Laboratory
Patrick Carribault, French Alternative Energies and Atomic Energy Commission, Directorate of Military Applications (CEA-DAM)
Yong Chen, Texas Tech University
Jan Ciesko, Sandia National Laboratories
Terry Cojean, Karlsruhe Institute of Technology
Salvatore Di Girolamo, NVIDIA
Jens Domke, RIKEN Center for Computational Science (R-CCS)
Christian Engelmann, Oak Ridge National Laboratory (ORNL)
Christian Fensch, ARM Norway
Kurt B. Ferreira, Sandia National Laboratories and University of New Mexico
Holger Froening, Heidelberg University
Todd Gamblin, Lawrence Livermore National Laboratory
Madhusudhan Govindaraju, SUNY Binghamton
Sumanth Gudaparthi, AMD Research
Abdou Guermouche, University of Bordeaux (IMB) and French Institute for Research in Computer Science and Automation (INRIA)
Kyle Hale, Illinois Institute of Technology
Yu Hua, Huazhong University of Science and Technology
Kamil Iskra, Argonne National Laboratory (ANL)
Youngjae Kim, Sogang University, South Korea
Volodymyr Kindratenko, University of Illinois, National Center for Supercomputing Applications (NCSA)
Zhiling Lan, University of Illinois Chicago
Michael Lang, Los Alamos National Laboratory
John Lange, Oak Ridge National Laboratory (ORNL) and University of Pittsburgh
Bogdan Nicolae, Argonne National Laboratory (ANL)
Lena Oden, University of Hagen, Germany and Forschungszentrum Jülich
Stephen L. Olivier, Sandia National Laboratories
Guillaume Pallez, National Institute for Research in Computer Science and Automation (INRIA)and University of Bordeaux, France
EunJung (EJ) Park, Qualcomm Inc.
Marc Perache, French Alternative Energies and Atomic Energy Commission
Thomas Ropars, Grenoble Alpes University, France
William Schonbein, Sandia National Laboratories
Joseph Schuchart, University of Tennessee, Innovative Computing Laboratory
Seetharami Seelam, IBM Research
Dario Suarez-Gracia, University of Zaragoza, Spain
Hari Subramoni, Ohio State University
Alan Sussman, University of Maryland
Osamu Tatebe, University of Tsukuba
Didem Unat, Koç University, Turkey
Alexandru Uta, Leiden University and Amazon AWS
Vanamala Venkataswamy, University of Virginia
Justin Wozniak, Argonne National Laboratory (ANL) and University of Chicago
Zhehui Zhang, University of California, Los Angeles
Jaroslaw Zola, University at Buffalo

Architecture:
Kevin J. Barker, Pacific Northwest National Laboratory (PNNL)
Mehmet E Belviranli, Colorado School of Mines
Subhadeep Bhattacharya, NVIDIA
Anastasiia Butko, Lawrence Berkeley National Laboratory (LBNL)
Ramon Canal, Universitat Politècnica de Catalunya
Zizhong Chen, University of California, Riverside
Bo Fang, Pacific Northwest National Laboratory (PNNL)
Benjamin M. Feinberg, Sandia National Laboratories
Michael Gowanlock, Northern Arizona University School of Informatics, Computing, and Cyber Systems
Yanfei Guo, Argonne National Laboratory (ANL)
Martin C. Herbordt, Boston University
Tsung-Wei Huang, The University of Wisconsin at Madison
Khaled Ibrahim, Lawrence Berkeley National Laboratory
Hyeran Jeon, University of California, Merced
Yao Kang, Nvidia
Hyesoon Kim, Georgia Institute of Technology
Masaaki Kondo, Keio University, Tokyo and RIKEN
Hyoukjun Kwon, University of California, Irvine
Lingda Li, Brookhaven National Laboratory
Dong Li, University of California, Merced
Guanpeng Li, University of Iowa
Ang Li, Pacific Northwest National Laboratory (PNNL)
Ji Liu, Argonne National Laboratory
George Michelogiannakis, Lawrence Berkeley National Laboratory (LBNL) and Stanford University
Dhabaleswar K. (DK) Panda, Ohio State University
Won Woo Ro, Yonsei University
John Shalf, Lawrence Berkeley National Laboratory (LBNL)
Xian-He Sun, Illinois Institute of Technology
Dingwen Tao, Indiana University
Antonino Tumeo, Pacific Northwest National Laboratory (PNNL)
Keith Underwood, Hewlett Packard Enterprise
Gwendolyn Voskuilen, Sandia National Laboratories
Daniel Wong, University of California, Riverside
Zhiwei Xu, Institute of Computing Technology, Chinese Academy of Sciences
Kazutomo Yoshii, Argonne National Laboratory (ANL)
Jianfeng Zhan, University of Chinese Academy of Sciences & ICT, Chinese Academy of Sciences
Zhao Zhang, Texas Advanced Computing Center (TACC)
Jishen Zhao, University of California, San Diego
Hao Zheng, University of Central Florida

Measurements, Modeling, and Experiments
Hartwig Anzt, Karlsruhe Institute of Technology and University of Tennessee
Olivier Beaumont, French Institute for Research in Computer Science and Automation (INRIA) and University of Bordeaux (IMB)
David Boehme, Lawrence Livermore National Laboratory
Marc Casas Guix, Barcelona Supercomputing Center (BSC) and Polytechnic University of Catalonia
Xiaowen Chu, HKUST
Florina M. Ciorba, University of Basel, Switzerland
Dong Dai, University of North Carolina, Charlotte
Sheng Di, Argonne National Laboratory (ANL), University of Chicago
Ryusuke Egawa, Tokyo Denki University and Tohoku University
Thomas Gruber, FAU Erlangen-Nürnberg and Erlangen National High Performance Computing Center
Dan Huang, Sun Yat-sen University, Guangzhou, China
Hua Huang, Georgia Institute of Technology
Shadi Ibrahim, French Institute for Research in Computer Science and Automation (INRIA)
Tanzima Islam, Texas State University
Weile Jia, Institute of Computing Technology, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Beijing, China
Michael Klemm, Advanced Micro Devices (AMD) Inc. and OpenMP Architecture Review Board
Sidharth Kumar, University of Illinois at Chicago
Zhuozhao Li, Southern University of Science and Technology
Yusen Li, Nankai University
Jay Lofstead, Sandia National Laboratories
Stefano Markidis, KTH Royal Institute of Technology, Sweden
Diana Moise, Hewlett Packard Enterprise (HPE)
Shirley Moore, University of Texas, El Paso
Sarah M. Neuwirth, Johannes Gutenberg University Mainz and Juelich Supercomputing Centre (JSC)
Dimitrios Nikolopoulos, Virginia Tech
Anne-Cecile Orgerie, National Center for Scientific Research (CNRS), France
Gourav Rattihalli, Hewlett Packard Labs
Stefanie Reuter, University of Cambridge
Juan Rodriguez Herrera, University of Edinburgh
Kento Sato, RIKEN
Bertil Schmidt, University of Mainz
Harald Servat, Intel
Zhirong Shen, Xiamen University
Estela Suarez, Forschungszentrum Jülich and Juelich Supercomputing Centre (JSC), Institute for Advanced Simulation
Shixuan Sun, Shanghai Jiao Tong University
Nathan Tallent, Pacific Northwest National Laboratory (PNNL)
Shubbhi Taneja, Worcester Polytechnic Institute (WPI)
Xueyan Tang, Nanyang Technological University
Luo Tao, The Institute of High Performance Computing
Keita Teranishi, Oak Ridge National Laboratory (ORNL)
James Trotter, Simula
Roman Wyrzykowski, Czestochowa University of Technology
Orcun Yildiz, Argonne National Laboratory (ANL)
Yijia Zhang, Peng Cheng Laboratory

(*Requests for corrections or changes should be sent to contact@ipdps.org)