IPDPS 2025 Call for Papers

DOWNLOADABLE PDF VERSION HERE

39th IEEE International Parallel &
Distributed Processing Symposium
June 3-7, 2025

Authors are invited to submit manuscripts that present novel and impactful research in high performance computing (HPC) in 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:

• Algorithms:
  This track focuses on algorithms for computational and data science in parallel and distributed computing environments (including cloud, edge, fog, distributed memory, and accelerator-based computing). Examples include structured and unstructured mesh and meshless methods, dense and sparse linear algebra computations, spectral methods, n-body computations, clustering, data mining, compression, and combinatorial algorithms such as graph and string algorithms. Also included in this track are algorithms that apply to tightly or loosely coupled systems, such as those supporting communication, synchronization, power management, distributed resource management, distributed data and transactions, and mobility. Novel algorithm designs and implementations tailored to emerging architectures (such as ML/AI accelerators or quantum computing systems) are also included.
• Applications:
  This track focuses on real-world applications (combinatorial, scientific, engineering, data analysis, and visualization) that use parallel and distributed computing concepts. Papers submitted to this track are expected to incorporate innovations that originate in specific target application areas, and contribute novel methods and approaches that address core challenges in their scalable implementation. Contributions include the design, implementation, and evaluation of parallel and distributed applications, including implementations targeting emerging architectures (such as ML/AI accelerators) and application domain advances enabled by ML/AI.
• Architecture:
  This track focuses on existing and emerging architectures for high performance computing, including architectures for instruction-level and thread-level parallelism; manycore, multicore, accelerator, domain-specific and special-purpose architectures (including ML/AI accelerators); reconfigurable architectures; memory technologies and hierarchies; volatile and non-volatile emerging memory technologies; co-design paradigms for processing-in-memory architectures; 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; and emerging architectural principles for machine learning, approximate computing, quantum computing, neuromorphic, analog, and bio-inspired computing.
• Machine Learning and Artificial Intelligence (ML/AI):
  This track focuses on all areas of ML/AI that are relevant to parallel and distributed computing, including ML/AI training on resource-limited platforms; computational optimization methods for AI such as pruning, quantization and knowledge distillation; parallel and distributed learning algorithms; energy-efficient methods for ML/AI; federated learning; design and implementation of ML/AI algorithms on parallel architectures (including distributed memory, GPUs, tensor cores and emerging ML/AI accelerators); new ML/AI methods benefitting HPC applications or HPC system management; and design and development of ML/AI software pipelines (e.g., frameworks for distributed training, integration of compression into ML/AI pipelines, compiler techniques and DSLs). Papers submitted to the ML/AI track should emphasize new ML/AI technology that is best reviewed by ML/AI experts. Papers that emphasize core parallel computing topics applied to ML/AI workloads or applications benefitting from use of existing ML/AI tools should be submitted to the topic domain tracks rather than this ML/AI track.
• 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 accelerators and architectures, including quantum, neuromorphic, and other non-Von Neumann systems; innovations made in support of large-scale infrastructures and facilities; and experiences and methods for allocating and managing system and facility resources.
• 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. Novel compiler techniques and frameworks leveraging machine learning methods are included in this track.
• System Software:
  This track focuses on software that is close to the bare high-performance computing (HPC) 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 operating system, hardware, and other software layers; system software solutions for ML/AI workloads (e.g., energy-efficient software methods for ML/AI); system software support for fault tolerance and resilience; containers and virtual machines; specialized operating systems and related support for high-performance computing; system software for future novel computing platforms including quantum, neuromorphic, and bio-inspired computing; and system software advances enabled by ML/AI.               

Best Paper Award

The program committee will select a small set of papers as Best Paper finalists. One paper will be named the Best Paper.

Best Open-Source Contribution Award

IPDPS welcomes submissions with technical contributions of open-source tool and dataset artifacts relevant to the parallel and distributed computing community. The authors of accepted papers may identify 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 Best Open-Source Contribution finalists. One paper will be recognized with the Best Open-Source Contribution Award.

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 October 3, 2024. Manuscripts must be submitted by October 10, 2024. To ensure fairness, no extensions will be given. 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 for each reference cited. No supplementary sections or appendices are allowed beyond the stated page limit. The program committee will use a double-blind review process. Submitted manuscripts should not include author names and affiliations, or otherwise disclose the identity of the authors due to the double-blind review process.

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 must be submitted by following the instructions at the IPDPS 2025 Submission Site (powered by Linklings). Authors must select a “primary” track for the submission; the primary track is the area most related to the paper’s contributions. An optional “secondary” track may also be selected.

• Submit the abstract and register your paper by October 3rd

REVIEW OF MANUSCRIPTS

All submitted manuscripts will be reviewed by the Program Committee under a double-blind, two-round review process. Submissions will be judged on correctness, originality, technical strength, significance, demonstrated or potential impact, quality of presentation, and interest and relevance to the conference. Submitted manuscripts must 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 paper’s objective and provide strong support to motivate the specific problem the submission addresses.
• Limitations of state-of-art approaches. Unambiguously discuss and distinguish the paper’s contributions from the most relevant and most recent prior works.
• Key insights and contributions. Clearly articulate the major insights that enable the described approach and 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.
• Methodology. Clearly specify key theoretical or experimental methodological details. Support the chosen methodological choices (e.g., cite the prior works that have evaluated their ideas using similar methodology). If a new methodology is adopted or theoretical assumptions differ from prior art, provide a detailed justification.
• Limitations of the proposed approach. Articulate all significant limitations of the proposed approach and identify conclusions that are sensitive to assumptions made in the paper.

The Program Committee will assess submissions in the above aspects. Therefore, the authors should make these aspects clear when articulating their contributions.
Authors will have the opportunity to respond to the reviewers’ questions and provide clarifications before the first-round decisions are made. Some submissions may not be invited to submit a response/rebuttal; these submissions will be notified with an early-reject decision by December 3, 2024.

First round decisions – “accept,” “revise,” or “reject” – will be sent by December 19, 2024. Authors of papers in the “revise” category will have the opportunity to submit a new version of their papers addressing reviewers’ comments. The revised submission and a cover letter explaining changes are due on January 23, 2025. An ensuing review will then provide decisions of “accept” or “reject”; papers will be rejected if the reviewers assess that the issues they raised were not satisfactorily addressed. Notification of final decisions will be sent by February 4, 2025. Camera-ready papers are due on February 27, 2025.

Questions may be sent to pc2025@ipdps.org.

ArXiv Submission Policy

Having an arXiv paper does not prohibit authors from submitting a paper to IPDPS 2025. arXiv papers are not peer-reviewed and not considered as formal publications; hence, they do not count as prior work. Authors are not expected to compare against arXiv papers that have not formally appeared in conference or journal proceedings. Authors must follow the double-blind submission guidelines even if a submitted paper is already on arXiv. 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, or not upload/revise the arXiv version during the review period). Authors should not direct reviewers to arXiv versions of the paper; in their evaluations, reviewers will consider only the material in the submitted paper.

Guidance on Artificial Intelligence (AI)-Generated Text

Tools like ChatGPT, Grammarly, or other AI assistants may be used to improve the submission presentation. However, authors will be held accountable for the accuracy of all information presented as well as for the contributions. IEEE requires that the use of any AI-generated text be disclosed in the paper’s Acknowledgements section. The sections of the paper that contain AI-generated text must 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 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 2025 IMPORTANT DATES

• Abstract submissions: October 3, 2024
• Full manuscript submissions (double-blind):October 10, 2024 - FIRM DEADLINE
• Author response/rebuttal to reviews: December 3 – 6, 2024
• First round decisions: December 19, 2024
• Revised submissions due: January 23, 2025
• Final decisions: February 4, 2025
• Camera-ready versions due: February 27, 2025

IPDPS 2025 PROGRAM CHAIRS

• Michela Becchi, North Carolina State University, USA
• Karen Devine, Sandia National Laboratories, ret., USA

2025 PROGRAM AREA CO-CHAIRS

Algorithms

• Maxim Naumov, Meta, USA
• Albert-Jan Yzelman, Huawei Technologies, Switzerland

Applications

• Sanjukta Bhowmick, University of North Texas, USA
• Kara Peterson, Sandia National Laboratories, USA

Architecture

• Amro Awad, University of Oxford, UK
• Antonino Tumeo, Pacific Northwest National Laboratory, USA

Machine Learning and Artificial Intelligence

• Prasanna Balaprakash, Oak Ridge National Laboratory, USA
• Dong Li, University of California, Merced, USA

Measurement, Performance, and Experiments

• Alexandru Iosup, Vruje Universiteit Amsterdam, The Netherlands
• Tanzima Islam, Texas State University, USA

Programming Models, Compilers, and Runtime Systems

• Franck Cappello, Argonne National Laboratory, USA
• Xipeng Shen, North Carolina State University, USA

System Software

• Patrick Bridges, University of New Mexico, USA
• Sarah Neuwirth, Johannes Gutenberg University Mainz, Germany

2025 TECHNICAL PROGRAM COMMITTEE MEMBERS

(Posted 24 October 2024*)

Algorithms
Bilge Acun, Meta, USA
Hartwig Anzt, Technical University of Munich, Germany
Rob Bisseling, Utrecht University, Netherlands
Erik Boman, Sandia National Laboratories, USA
Aydin Buluc, Lawrence Berkeley National Laboratory, USA
Alfredo Buttari, Centre National de la Recherche Scientifique (CNRS), France
Silvina Caino-Lores, French Institute for Research in Digital Science and Technology (Inria), France
Erin Carson, Charles University, Czech Republic
Cedric CHEVALIER, French Alternative Energies and Atomic Energy Commission (CEA), France
Lung Sheng Chien, CEREBRAS, USA
Matthew L. Curry, Sandia National Laboratories, USA
Maryam Mehri Dehnavi, University of Toronto, Canada
Nikoli Dryden, Lawrence Livermore National Laboratory, USA
Benoît Dupont de Dinechin, Kalray S.A., France
S M Ferdous, Pacific Northwest National Laboratory, USA
Oded Green, NVIDIA, USA
William D. Gropp, University of Illinois, National Center for Supercomputing Applications, USA
Gaetan Hains, Universite Paris-Est Creteil, France
Kamer Kaya, Sabancı University, Turkey
Gokcen Kestor, Pacific Northwest National Laboratory, USA
Raye Kimmerer, Massachusetts Institute of Technology, USA
Sarah Knepper, Intel Corporation, USA
Penporn Koanantakool, Google, USA
Sanmukh Kuppannagari, Case Western Reserve University, USA
Johannes Langguth, Simula Research Laboratory, Norway
Ying Wai Li, Los Alamos National Laboratory, USA
Jiajia Li, North Carolina State University, USA
Weifeng Liu, China University of Petroleum, China
Vanessa Lopez-Marrero, Brookhaven National Laboratory, USA
Tze Meng Low, Carnegie Mellon University, USA
Alba Cristina Magalhaes Alves de Melo, University of Brasilia, Brazil
Fredrik Manne, University of Bergen, Norway
Aristeidis Mastoras, Huawei Technologies , Switzerland
Piyush Sao, Oak Ridge National Laboratory, USA
Olaf Schenk, Università della Svizzera italiana, Switzerland
Christian Schulz, Heidelberg University, Germany
Shubho Sengupta, Meta, USA
Francesco Silvestri, University of Padova, Italy
Edgar Solomonik, University of Illinois, USA
Aravind Sukumaran-Rajam, Meta, USA
Daisuke Takahashi, University of Tsukuba, Japan
Alexander Tiskin, Saint Petersburg State University, Russia
Sivan Toledo, Tel-Aviv University, Israel
Bora Ucar, Centre National de la Recherche Scientifique (CNRS), France
Roel Van Beeumen, Lawrence Berkeley National Laboratory, USA
Wim Vanroose, U. Antwerpen; Motulus, Belgium
Ana Lucia Varbanescu, University of Twente, University of Amsterdam, The Netherlands
Flavio Vella, University of Trento, Italy, Italy
Kees Vuik, Delft University of Technology, Netherlands, The Netherlands
Yinglong Xia, Huawei Research America, USA
Helen Xu, Georgia Institute of Technology, USA
Chao Yang, Lawrence Berkeley National Laboratory, USA
Guowei Zhang, HiSilicon, China

Applications
Tejaswi Agarwal, University of Missouri, Columbia, USA
Michael Bader, Technical University of Munich, Germany
Dip Sankar Banerjee, Indian Institute of Technology Jodhpur, India
Thomas Dufaud, University of Versailles, France
Lin Gan, Tsinghua University, China
Priyanka Ghosh, National Institutes of Health, USA
Giulia Guidi, Cornell University, USA
Yuede Ji, University of Texas at Arlington, USA
Ziynet Kesimoglu, National Institutes of Health, USA
Hemanth Kolla, Sandia National Laboratories, USA
Jayesh Krishna, Argonne National Laboratory, USA
Harald Köstler, University of Erlangen-Nuremberg, Germany
Kim Liegeois, AMD, USA
Paul Lin, Lawrence Berkeley National Laboratory, USA
Kamesh Madduri, Pennsylvania State University, USA
Meifeng Lin, Brookhaven National Laboratory, USA
Sanchit Misra, Intel Corporation, India
Diana Moise, Hewlett Packard Enterprise, Switzerland
Mohammed Alaul Haque Monil, Oak Ridge National Laboratory, USA
Sri Hari Krishna Narayanan, Argonne National Laboratory, USA
Roger Pearce, Lawrence Livermore National Laboratory, USA
Satish Puri, Missouri University of Science and Technology, USA
Erik Saule, University of North Carolina Charlotte, USA
Ada Sedova, Oak Ridge National Laboratory, USA
Oguz Selvitopi, Lawrence Berkeley National Laboratory, USA
Yogesh Simmhan, Indian Institute of Science, India
George Slota, Rensselaer Polytechnic Institute, USA
Hongyang Sun, University of Kansas, USA
George Teodoro, Universidade de Minas Gerais, Brazil
Stephen Thomas, Advanced Micro Devices (AMD), USA
Jesmin Jahan Tithi, Intel Corporation, USA
Jerry Watkins, Sandia National Laboratories, USA
Dongfang Zhao, University of Washington, USA

Architecture
Usman Ali, Meta, USA
Ahmad Atamli, University of Southampton, UK
Mehmet E Belviranli, Colorado School of Mines, USA
Anastasiia Butko, Lawrence Berkeley National Laboratory, USA
Luca Carloni, Columbia University, USA
Vito Giovanni Castellana, Pacific Northwest National Laboratory, USA
Anup Das, Drexel University, USA
Aditya Dhakal, Hewlett Packard Labs, USA
Benjamin M Feinberg, Sandia National Laboratories, USA
Dimitris Gizopoulos, National and Kapodistrian University of Athens, Greece
Patricia Gonzalez-Guerrero, Lawrence Berkeley National Laboratory, USA
Clayton Hughes, Sandia National Laboratories, USA
Ryoo Jeeho, Fairleigh Dickinson University, USA
Yao Kang, NVIDIA, USA
Omer Khan, University of Connecticut, USA
Hyokeun Lee, Ajou University, Republic of Korea
John Leidelc, Tactical Computing Laboratories LLC, USA
Bo Mao, Xiamen University, China
Miquel Moretó, Polytechnic University of Catalonia; Barcelona Supercomputing Center, Spain
Seonjin Na, Georgia Institute of Technology, USA
Gianluca Palermo, Politecnico di Milano, Italy
Ivy Peng, KTH Royal Institute of Technology, Sweden
Miquel Pericas, Chalmers University of Technology, Sweden
Artur Podobas, KTH Royal Institute of Technology, Sweden
Roxana Rusitoru, ARM, UK
Catherine Schuman, University of Tennessee, USA
Devesh Singh, Qualcomm, Inc., USA
Gwendolyn Voskuilen, Sandia National Laboratories, USA
Dan Wilkinson, Imagination Technologies, UK
Di Wu, University of Central Florida, USA
Changgang Zheng, University of Oxford, UK
Kazi Abu Zubair, Intel Corporation, USA

Machine Learning and Artificial Intelligence
Sameh Abdulah, King Abdullah University of Science and Technology, Saudi Arabia
Ashwin M. Aji, Advanced Micro Devices (AMD), USA
Filip Blagojevic, Western Digital Research, USA
Huimin Cui, Chinese Academy of Sciences, China
Wenqian Dong, Florida International University, USA
Ian Foster, Argonne National Laboratory , USA
Xinwei Fu, Amazon Web Services, USA
Rong Ge, Clemson University, USA
Giorgis Georgakoudis, Lawrence Livermore National Laboratory, USA
Luanzheng Guo, Pacific Northwest National Laboratory, USA
Shaoyi Huang, Stevens Institute of Technology, USA
Ignacio Laguna, Lawrence Livermore National Laboratory, USA
Zhiling Lan, University of Illinois Chicago, USA
Stefano Markidis, KTH Royal Institute of Technology, Sweden
Laura Morselli, CINECA, Italy
Konstantinos Parasyris, Lawrence Livermore National Laboratory, USA
Jie Ren, College of William & Mary, USA
Bin Ren, College of William & Mary, USA
Olatunji Ruwase, Microsoft Corporation, USA
Kento Sato, RIKEN, Japan
Shashank Subramanian, Lawrence Berkeley National Laboratory, USA
Jeyan Thiyagalingam, Rutherford Appleton Laboratory, UK
Brian C. Van Essen, Lawrence Livermore National Laboratory, USA
Venkatram Vishwanath, Argonne National Laboratory, USA
Mohamed Wahib, RIKEN , Japan
Zheng Wang, University of Leeds, UK
Feiyi Wang, Oak Ridge National Laboratory, USA
Kai Wu, Microsoft Corporation, USA
Bing Xie, Microsoft Corporation, USA
Zhen Xie, State University of New York at Binghamton, USA
Rio Yokota, Tokyo Institute of Technology, Japan
Minjia Zhang, University of Illinois, Urbana-Champaign, USA
Wenhui Zhang, Bytedance, USA
Amelie Chi Zhou, Hong Kong Baptist University, China

Measurement, Performance, and Experiments
Cristina Abad, ESPOL, Ecuador
Ahmed Ali-Eldin Hassan, Chalmers University of Technology, Sweden
Jean-Baptiste Besnard, ParaTools Inc., France
Walter Binder, Università della Svizzera italiana, Switzerland
Milind Chabbi, Uber Technologies, USA
Nicholas Chaimov, ParaTools Inc., USA
Marcin Copik, ETH Zürich, Switzerland
Camille Coti, École de Technologie Supérieure, Canada
Tiziano De Matteis, VU Amsterdam, Netherlands
Trilce Estrada, University of New Mexico, USA
Wanling Gao, Chinese Academy of Sciences, China
Maria Garzaran, Intel Corporation, USA
Markus Geimer, Juelich Supercomputing Centre, Germany
Ann Gentile, Sandia National Laboratories, USA
Nusrat Islam, Advanced Micro Devices (AMD), USA
Brandic Ivona, Vienna University of Technology, Austria
Ali Jannesari, Iowa State University, USA
Joachim Jenke, RWTH Aachen University, Germany
Lingda Li, Brookhaven National Laboratory, USA
Allen Malony, University of Oregon; ParaTools, Inc., USA
Dejan Milojicic, Hewlett Packard Labs, USA
Josh Milthorpe, Australian National University, Australia
Shirley Moore, University of Texas, El Paso, USA
Cosmin Eugen Oancea, University of Copenhagen, Denmark
Doru Thom Popovici, Lawrence Berkeley National Laboratory, USA
Barry Rountree, Lawrence Livermore National Laboratory, USA
Krzysztof Rzadca, Google and University of Warsaw, Poland
Naw Safrin Satter, Oak Ridge National Laboratory, USA
Toyotaro Suzumura, University of Tokyo, Japan
Hiroyuki Takizawa, Tohoku University, Japan
Jesper Larsson Träff, Technical University Wien , Austria
Petr Tuma, Charles University, Czech Republic
Blesson Varghese, University of St Andrews, UK
Bert Wesarg, Technische Universität Dresden, Germany
Avani Wildani, Cloudflare Inc, USA
Nicholas J. Wright, National Energy Research Scientific Computing Center (NERSC), USA
Bo Wu, Colorado School of Mines, USA

Programming Models, Compilers, and Runtime Systems
Guoyang Chen, Apple Inc., USA
Jou-An Chen, Qualcomm, Inc., USA
Bronis R. de Supinski, Lawrence Livermore National Laboratory, USA
Irene Dea, Databricks, USA
Bernhard Egger, Seoul National University, Republic of Korea
Peng Jiang, University of Iowa, USA
Sriram Krishnamoorthy, Google LLC, USA
Jaejin Lee, Seoul National University, Republic of Korea
Seyong Lee, Oak Ridge National Laboratory, USA
Ang Li, Pacific Northwest National Laboratory, USA
Harshitha Menon, Lawrence Livermore National Laboratory, USA
Dhabaleswar K. (DK) Panda, The Ohio State University, USA
Lawrence Rauchwerger, University of Illinois, Urbana-Champaign, USA
Martin Schulz, Technical University Munich, Germany
Dingwen Tao, Chinese Academy of Sciences, China
Kenjiro Taura, University of Tokyo, Japan
Keita Teranishi, Oak Ridge National Laboratory, USA
Samuel Thibault, University of Bordeaux (LaBRI), French Institute for Research in Computer Science and Automation (INRIA), France
Miwako Tsuji, RIKEN, Japan
Jin Wang, NVIDIA, USA
Siyue Wang, Microsoft, USA
Yu Zhang, University of Science and Technology of China, China
Feng Zhang, Renmin University of China, China
Zhijia Zhao, University of California, Riverside, USA

System Software
Jean-Thomas Acquaviva, Data Direct Networks, France
Hadeel Albahar, Kuwait University, Kuwait
Tyler Allen, University of North Carolina Charlotte, USA
Amanda J. Bienz, University of New Mexico, USA
Ron Brightwell, Sandia National Laboratories, USA
Florina M. Ciorba, University of Basel, Switzerland
Dilma Da Silva, Texas A&M University, USA
Hariharan Devarajan, Lawrence Livermore National Laboratory, USA
Peter Dinda, Northwestern University, USA
Jens Domke, RIKEN, Japan
Matthew G. F. Dosanjh, Sandia National Laboratories, USA
Holger Froening, Heidelberg University, Germany
Balazs Gerofi, Intel Corporation, USA
Taylor Groves, Lightmatter, USA
Luanzheng Guo, Pacific Northwest National Laboratory, USA
Sascha Hunold, Technical University of Vienna, Austria
Adrian Jackson, University of Edinburgh, EPCC, UK
Hideyuki Kawashima, Keio University, Japan
John Lange, Oak Ridge National Laboratory, USA
Thomas Leibovici, CEA, France
Jianshu Liu, Boise State University, USA
Jay Lofstead, Sandia National Laboratories, USA
David Lowenthal, University of Arizona, USA
Jakob Luettgau, French Institute for Research in Digital Science and Technology (Inria), France
Arthur Maccabe, University of Arizona, USA
Preeti Malakar, Indian Institute of Technology, Kanpur, India
Bogdan Nicolae, Argonne National Laboratory, Illinois Institute of Technology, USA
Arnab K. Paul, BITS Pilani, K. K. Birla Goa Campus, India
Olga Pearce, Lawrence Livermore National Laboratory , USA
Galen Shipman, Los Alamos National Laboratory, USA
Anthony Skjellum, Tennessee Technological University, USA
Marc-André Vef, Data Direct Networks, Germany
Vanamala Venkataswamy, University of Virginia, USA
Lipeng Wan, Georgia State University, USA
Chen Wang, Lawrence Livermore National Laboratory, USA
Patrick Widener, Oak Ridge National Laboratory, USA
Robert W. Wisniewski, Samsung, USA

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