IPDPS 2026 Call for Papers

40th IEEE International Parallel &
Distributed Processing Symposium
May 25-29, 2026
New Orleans, USA

Download 2026 Call for Papers (PDF)

Submit Your Paper

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.

 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 tools 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 2, 2025. Manuscripts must be submitted by October 9, 2025; 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-anonymous review process. Submitted manuscripts should not include author names and affiliations, or otherwise disclose the identity of the authors due to the double-anonymous 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 2026 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.

REVIEW OF MANUSCRIPTS

All submitted manuscripts will be reviewed by the Program Committee under a double-anonymous, 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:

1. Motivation. Clearly state the paper’s objective and provide strong support to motivate the specific problem the submission addresses.
2. Limitations of state-of-art approaches. Unambiguously discuss and distinguish the paper’s contributions from the most relevant and most recent prior works.
3. 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.
4. 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.
5. 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 1, 2025.

First round decisions – “accept,” “revise,” or “reject” – will be sent by December 18, 2025. 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 19, 2026. 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 2, 2026
• Camera-ready papers are due on February 20, 2026

Questions may be sent to pc2026@ipdps.org.

ArXiv Submission Policy

Having an arXiv paper does not prohibit authors from submitting a paper to IPDPS 2026. 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-anonymous 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 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.

Travel Arrangements

We welcome all geographically diverse set of attendees to the conference. If a visa is required for travel the program committee will provide an invitation letter to the registered attendees. We strongly encourage the attendees to immediately apply for VISA in their country as the process can take a significant amount of time. We highlight that authors of the accepted papers are expected to present them in person at the conference. We are very excited to meet all of you in New Orleans, USA.

IPDPS 2026 IMPORTANT DATES

• Abstract submissions: October 2, 2025
• Full manuscript submissions (double-anonymous): October 9, 2025 - FIRM DEADLINE
• Author response/rebuttal to reviews: December 1 – 4, 2025
• First round decisions: December 18, 2025
• Revised submissions due: January 19, 2026
• Final decisions: February 2, 2026
• Camera-ready versions due: February 20, 2026

REPRODUCIBILITY INITIATIVE

In an effort to produce a standardized, long lasting impact, IPDPS is introducing a computational result reproducibility appendix. This appendix aims at describing the processes used to obtain the computational results and will be appended to the accepted papers. The appendix will be due after acceptance. More information can be found here.

• Maxim Naumov, Meta, USA
• Cristina Silvano, Politecnico di Milano, Italy

PROGRAM TRACK CO-CHAIRS

Algorithms

• Sivan Toledo, University of Tel-Aviv, Israel
• Oded Green, Nvidia, USA

Applications

• Lin Gan, Tsinghua University, China
• Axel Huebl, Lawrence Berkeley National Lab (LBNL), USA

Architectures

• Catherine Schuman, University of Tennessee, USA
• Ioannis Sourdis, Chalmers University of Tech, Sweden

Machine Learning and Artificial Intelligence

• Jongsoo Park, OpenAI, USA
• Alexey Tumanov, Georgia Tech, USA

Measurement, Performance and Experiments

• Marc Casas, Barcelona Supercomputing Center (BSC), Spain
• Shirley Moore, University of Texas - El Paso, USA

Programming Models, Compilers, and Runtime Systems

• Dimitros Nikolopolous, Virginia Tech, USA
• Valeria Cardellini, University of Roma Tor Vergata, Italy

System Software

• Bogdan Nicolae, Argonne National Lab (ANL), USA
• Ivy Peng, KTH Royal Institute Tech, Sweden

PROGRAM COMMITTEE MEMBERS

(Posted 5 October 2025*

Algorithms
Oliver Alvarado Rodriguez, Hewlett Packard Enterprise (HPE)
Hartwig Anzt, Technical University of Munich; University of Tennessee, Knoxville
Ariful Azad, Texas A&M University, Indiana University
Grey Ballard, Wake Forest University
Olivier Beaumont, French Institute for Research in Computer Science and Automation (INRIA), University of Bordeaux (LaBRI)
Anne Benoit, ENS Lyon, Georgia Institute of Technology
Jonathan Berry, Sandia National Laboratories
Sanjukta Bhowmick, University of North Texas
Amanda J. Bienz, University of New Mexico
George Biros, University of Texas, Oden Institute
Rob Bisseling, Utrecht University
Erik Boman, Sandia National Laboratories
Christina Boucher,
Erin Carson, Charles University, Czech Republic
Jee Choi, University of Oregon
Rezaul Chowdhury, Stony Brook University
Guojing Cong, Oak Ridge National Laboratory (ORNL)
Fanny Dufosse, French Institute for Research in Computer Science and Automation (INRIA)
Patrick Flick, Google LLC
Pieter Ghysels, AMD
John Gilbert, University of California, Santa Barbara
Giulia Guidi, Cornell University, Lawrence Berkeley National Laboratory
John A. Gunnels, NVIDIA Corporation
Mahantesh Halappanavar, Pacific Northwest National Laboratory (PNNL)
Jeff R. Hammond, NVIDIA Helsinki Oy, NVIDIA Corporation
Nikhil Jain, NVIDIA Corporation
Hans Johansen, Lawrence Berkeley National Laboratory (LBNL)
Ananth Kalyanaraman, Washington State University
Ramakrishnan Kannan, Georgia Institute of Technology
Oguz Kaya, Universite Paris-Saclay, Laboratoire Interdisciplinaire des Sciences du Numerique (LISN)
Penporn Koanantakool, Google LLC
Kishore Kothapalli, International Institute of Information Technology (IIIT), Hyderabad
Sidharth Kumar, University of Illinois Chicago
Tze Meng Low, Carnegie Mellon University
Hatem Ltaief, King Abdullah University of Science and Technology (KAUST)
Piotr Luszczek, Massachusetts Institute of Technology (MIT), Lincoln Laboratory; University of Tennessee, Knoxville
Kamesh Madduri, Pennsylvania State University
Fredrik Manne, University of Bergen
Marco Minutoli, Pacific Northwest National Laboratory (PNNL)
Roger Pearce, Lawrence Livermore National Laboratory (LLNL), Texas A&M University
Cynthia Phillips, Sandia National Laboratories
Peter Sanders, Karlsruhe Institute of Technology (KIT)
Piyush Sao, Oak Ridge National Laboratory (ORNL)
Olaf Schenk, Panua Technologies, Universite della Svizzera italiana
Bertil Schmidt, Johannes Gutenberg University Mainz
Christian Schulz, Heidelberg University
George Slota, Rensselaer Polytechnic Institute, Sandia National Laboratories
Alok Tripathy, Essential AI
Bora UCAR, French National Center for Scientific Research (CNRS); LIP, ENS de Lyon
Brian C. Van Essen, Lawrence Livermore National Laboratory (LLNL)
Flavio Vella, University of Trento, Italy
Ichitaro Yamazaki, Sandia National Laboratories
Jeffrey Young, Georgia Institute of Technology
Albert-Jan Yzelman, Huawei Technologies Switzerland AG

Applications
David Abramson, University of Queensland, Australia
Cangi Attila,
Julien Bigot, French Alternative Energies and Atomic Energy Commission (CEA)
Dezun Dong, NUDT, College of Computer
Erik Draeger, Lawrence Livermore National Laboratory (LLNL), Center for Applied Scientific Computing
Xiaohui Duan, Shandong University, National Supercomputing Center in Wuxi
Kevin Gott, Lawrence Berkeley National Laboratory, National Energy Research Scientific Computing Center (NERSC)
TAKESHI IWASHITA, Kyoto University
Ali Jannesari, Iowa State University
Tomasz Kacprzak, ETH Zurich, Swiss Data Science Center
Daniel S. Katz, University of Illinois, National Center for Supercomputing Applications (NCSA)
Damien Lebrun-Grandie, Oak Ridge National Laboratory (ORNL)
Weifeng Liu, China University of Petroleum, Beijing; Super Scientific Software Laboratory
Lokamani Mani,
Veronica G. Melesse Vergara, Oak Ridge National Laboratory (ORNL)
Rowan Michael,
Diana Moise, Hewlett Packard Enterprise (HPE)
Andrew Myers, Lawrence Berkeley National Laboratory (LBNL)
Tan Nigel Phillip, Los Alamos National Laboratory (LANL)
Bo Peng, Shanghai Jiao Tong University
Sivasankaran Rajamanickam, Sandia National Laboratories
Erik Saule, University of North Carolina at Charlotte
Klaus Steiniger, Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Center for Advanced Systems Understanding (CASUS)
Daisuke Takahashi, University of Tsukuba
Valerie Taylor, Argonne National Laboratory (ANL), University of Chicago
George Teodoro, Federal University of Minas Gerais
Yinuo Wang, Tsinghua University
Jingheng Xu, Tsinghua University, China
Wei Xue, Tsinghua University, China
Zekun Yin, Shandong University, China
Jiapeng Zhang, Hunan University
Michael Zingale, Stony Brook University

Architecture
Nikolaos Alachiotis, University of Twente
Chloe Alverti,
Ahmad Atamli, University of Southampton
Christos Bouganis, Imperial College London
Anastasiia Butko, Lawrence Berkeley National Laboratory (LBNL)
Ramon Canal, Universitat Politecnica de Catalunya
Anup Das, Drexel University
Giorgos Dimitrakopoulos,
Ben Feinberg, Sandia National Laboratories
Georgi Gaydadjiev, Delft University of Technology
Dimitris Gizopoulos, National and Kapodistrian University of Athens
Maya Gokhale, Lawrence Livermore National Laboratory (LLNL)
Jahanzeb Hashmi, NVIDIA
H. Peter Hofstee, IBM, Delft University of Technology
Pekka Jaaskelainen, Tampere University
Stefanos Kaxiras, Uppsala University
Paul H.J. Kelly, Imperial College, London
Robin Knauerhase, AMD Research
Christos Kotselidis, The University of Manchester
Angeliki Kritikakou, Univ Rennes, IUF
Hiroki Matsutani, Keio University
George Michelogiannakis, Lawrence Berkeley National Laboratory (LBNL), Stanford University
Gianluca Palermo, DEIB, Politecnico di Milano
Vassilis Papaefstathiou, FORTH-ICS, Greece; Institute of Computer Science, Institute of Computer Science, Institute of Computer Science, Foundation for Research and Technology - Hellas
Ardavan Pedram,
Artur Podobas, KTH Royal Institute of Technology, Sweden
Roxana Rusitoru, ARM Limited
Martin Schulz, Technical University of Munich, Leibniz Supercomputing Centre (LRZ)
John Shalf, Lawrence Berkeley National Laboratory (LBNL)
Leonel Sousa, INESC-ID, IST, University of Lisbon; Instituto Superior Técnico, Universidade de Lisboa
Christos Strydis, Erasmus Medical Center, Rotterdam; Delft University of Technology
Jesmin Jahan Tithi, Intel Corporation
Pedro Trancoso, Chalmers University of Technology; University of Gothenburg, Sweden
Keith Underwood, Hewlett Packard Enterprise (HPE)
Gwendolyn Voskuilen, Sandia National Laboratories

Machine Learning and Artificial Intelligence
Sameh Abdulah, King Abdullah University of Science and Technology (KAUST)
Ashwin M. Aji, Advanced Micro Devices (AMD) Inc
Rick Archibald, Oak Ridge National Laboratory (ORNL)
Protonu Basu, Lawrence Berkeley National Laboratory
Moise Blanchard,
Peng Chen, Georgia Institute of Technology
Dazhao Cheng, Wuhan University
Huimin Cui, Chinese Academy of Sciences; University of Chinese Academy of Sciences, Beijing, China
Zhaoxia Deng,
Wenqian Dong, Oregon State University
Constantine Dovrolis,
Trilce Estrada, University of New Mexico
Naila Farooqui,
Igor Fedorov,
Ian Foster, Argonne National Laboratory (ANL), University of Chicago
Vijay Ganesh,
Animesh Garg,
Eduard Gorbunov, MBZUAI
Luanzheng Guo, Pacific Northwest National Laboratory (PNNL)
Alexander Heinecke, Intel Corporation
Cheol-Ho Hong, Chung-Ang University
Dan Huang, Sun Yat-sen University
Shaoyi Huang, Stevens Institute of Technology
Soonwook Hwang, Korea Institute of Science and Technology Information (KISTI)
Dhiraj D. Kalamkar, Intel Corporation
Clayton Kerce, Georgia Tech Research Institute
Alind Khare, Microsoft
Youngsok Kim, Yonsei University
Manoj Krishnan, Google
Salem Lahlou,
Jinho Lee, Seoul National University
Pan Li,
Zichang Liu,
Steve Mussmann, Georgia Tech
Yunjie Pan,
Mostofa Patwary, NVIDIA Corporation
Sarunya Pumma, Meta Platforms, Inc.
Bin Ren, College of William & Mary
Soojung Ryu, SNU
Kento Sato, RIKEN Center for Computational Science (R-CCS)
Dingwen Tao, Institute of Computing Technology, Chinese Academy of Sciences; University of Chinese Academy of Sciences, Beijing
Jeyan Thiyagalingam, Rutherford Appleton Laboratory, Science and Technology Facilities Council (STFC)
Karthikeyan Vaidyanathan, Intel
Feiyi Wang, Oak Ridge National Laboratory (ORNL)
Zhen Xie, State University of New York at Binghamton
Yufan Xu, Uber Technologies, Inc.
Hanmei Yang, Meta Platforms, Inc.
Wenhui Zhang, Bytedance
Jiyuan Zhang, Meta
Juba Ziani,

Measurement, Performance, and Experiments
Cristina Abad, ESPOL
Mehmet E Belviranli, Colorado School of Mines
Maria Carpen-Amarie, Huawei Research
Tiziano De Matteis, Vrije Universiteit Amsterdam
Songshi Dou, The University of Hong Kong
Maria Garzaran, Intel Corporation
Ann Gentile, Sandia National Laboratories
Georg Hager, Friedrich-Alexander-Universitet Erlangen-Nurnberg, Erlangen National High Performance Computing Center
Kevin Huck, Advanced Micro Devices, Inc. (AMD)
Andra Hugo, Apple
Roman Iakymchuk, Umea University
Tanzima Z. Islam, Texas State University
Julien Jaeger, French Alternative Energies and Atomic Energy Commission (CEA)
Yuyang Jin, Tsinghua University
Zhiling Lan, University of Illinois Chicago, Argonne National Laboratory (ANL)
Jiajia Li, North Carolina State University
Ming Li, The University of Tulsa
Jianshu Liu, San Diego State University
Yang Liu, Lawrence Berkeley National Laboratory (LBNL)
Kewen Meng, Advanced Micro Devices, Inc. (AMD)
Dejan Milojicic, Hewlett Packard Labs
Tapasya Patki, Lawrence Livermore National Laboratory (LLNL)
Doru Thom Popovici, Lawrence Berkeley National Laboratory
Krzysztof Rzadca, Google; University of Warsaw, Poland
Timo Schneider, ETH Zurich
Evgenia Smirni, College of William and Mary
Joshua Suetterlein, Pacific Northwest National Laboratory (PNNL)
Hiroyuki Takizawa, Tohoku University
Nathan Tallent, Pacific Northwest National Laboratory (PNNL)
Guangming Tan, Institute of Computing Technology, Chinese Academy of Sciences; Western Institute of Computing Technology
Ahmad Tarraf, Technical University of Darmstadt
Christian Terboven, RWTH Aachen University
Deepak Tosh, University of Texas at El Paso
Jesper Larsson Traff, Technical University of Vienna
Carsten Trinitis, Technical University of Munich
Petr Tuma, Charles University
Sahil Tyagi, Oak Ridge National Laboratory, Indiana University Bloomington
Avani Wildani, Emory University, Cloudflare Inc
Nicholas J. Wright, National Energy Research Scientific Computing Center (NERSC)
Hailong Yang, Beihang University
Zhao Zhang, Rutgers University, Department of Electrical and Computer Engineering
Zhengji Zhao, Lawrence Berkeley National Laboratory (LBNL), National Energy Research Scientific Computing Center (NERSC)
Christopher Zimmer, Oak Ridge National Laboratory (ORNL)

Programming Models, Compilers, and Runtime Systems
Christos D. Antonopoulos, University of Thessaly
Filip Blagojevic, Western Digital Research
Jesus Carretero, University Carlos III of Madrid, Spain
Milind Chabbi, Uber Technologies
Matthew Curtis-Maury,
Daniele De Sensi, Sapienza University of Rome
Rong Ge, Clemson University
Giorgis Georgakoudis, Lawrence Livermore National Laboratory (LLNL)
Georgios Goumas, National Technical University of Athens
Panagiotis Hadjidoukas, University of Patras
Satoshi Imamura,
Vana Kalogeraki, Athens University of Economics and Business
Gokcen Kestor, Barcelona Supercomputing Center (BSC); University of California, Merced
Jaejin Lee, Seoul National University
Dong Li, University of California, Merced
Stefano Markidis, KTH Royal Institute of Technology
Gabriele Mencagli, University of Pisa, Italy; University of Pisa, Department of Computer Science
Nikela Papadopoulou, University of Glasgow
Konstantinos Parasyris, Lawrence Livermore National Laboratory (LLNL)
Polyvios Pratikakis, Foundation for Research and Technology Hellas
Radu Prodan, University of Innsbruck, Austria
Satish Puri, Missouri University of Science and Technology
Carlos Reano, Universitat de Valencia
Gabriele Russo Russo, University of Rome Tor Vergata
Stefan Schulte, Technische Universitet Hamburg
Yogesh Simmhan, Indian Institute of Science, Bangalore
Douglas Thain, University of Notre Dame
Pedro Valero-Lara, Oak Ridge National Laboratory (ORNL)
Zheng Wang, University of Leeds, School of Computer Science
Lishan Yang, George Mason University (GMU)

System Software
Jean-Thomas Acquaviva, Data Direct Networks
Gagan Agrawal, University of Georgia
Moiz Arif, Micron Technology
Michela Becchi, North Carolina State University
Francieli Boito, University of Bordeaux, France; French Institute for Research in Computer Science and Automation (INRIA)
Ron Brightwell, Sandia National Laboratories
Nick Brown, Edinburgh Parallel Computing Centre (EPCC); University of Edinburgh, Scotland
Suren Byna, The Ohio State University, Lawrence Berkeley National Laboratory (LBNL)
Philip Carns, Argonne National Laboratory (ANL)
Antony Chazapis, Foundation for Research and Technology - Hellas (FORTH)
Daniel Cordeiro, University of Sao Paulo
Alexandru Costan, French Institute for Research in Computer Science and Automation (INRIA)
Camille Coti, Ecole de Technologie Superieure
Vincenzo De Maio, TU Wien, University of Leicester
Hariharan Devarajan, Lawrence Livermore National Laboratory (LLNL), Illinois Institute of Technology
Balazs Gerofi, Intel Corporation, RIKEN Center for Computational Science (R-CCS)
Ryan Grant, Queen's University, Canada; Power API
Andreas Herten, Forschungszentrum Julich, Julich Supercomputing Centre (JSC)
Seyong Lee, Oak Ridge National Laboratory (ORNL)
Pei-Hung Lin, Lawrence Livermore National Laboratory (LLNL)
Jay Lofstead, Sandia National Laboratories, University of New Mexico
Jakob Luettgau, French Institute for Research in Computer Science and Automation (INRIA)
Alba Cristina Magalhaes Alves de Melo, University of Brasilia, Brazil
Preeti Malakar, Indian Institute of Technology (IIT), Kanpur
Aniruddha Marathe, Lawrence Livermore National Laboratory
Manolis Marazakis, Foundation for Research and Technology - Hellas (FORTH)
Avinash Kumar Maurya, Argonne National Laboratory (ANL), Rochester Institute of Technology
Antonio J. Pena, Barcelona Supercomputing Center (BSC); Universitat Politecnica de Catalunya (UPC), Spain
Dionisios Pnevmatikatos, National Technical University of Athens
M. Mustafa Rafique, Rochester Institute of Technology
Jie Ren, College of William & Mary
Galen Shipman, Los Alamos National Laboratory
Anthony Skjellum, Tennessee Technological University
Hari Subramoni, The Ohio State University
DOMENICO TALIA, University of Calabria, Italy
Massimo Torquati, University of Pisa
Miwako Tsuji, University of Tsukuba, RIKEN Center for Computational Science (R-CCS)
Antonino Tumeo, Pacific Northwest National Laboratory (PNNL)
Hans Vandierendonck, Queen's University Belfast
Ana Lucia Varbanescu, University of Twente, Netherlands; University of Amsterdam, Netherlands
Patrick Widener, Oak Ridge National Laboratory (ORNL)
Robert W. Wisniewski, Samsung
Bing Xie, Microsoft Corporation
Cong Xu, Hewlett Packard Enterprise (HPE)
Amelie Chi Zhou, Hong Kong Baptist University

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