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About us

REX-IO focuses on extreme-scale I/O and storage challenges driven by emerging hybrid HPC workloads, from traditional simulation to AI/ML, data analytics, and complex workflows, that combine scale-up and scale-out components. As exascale systems and multi-tier storage hierarchies become more common, the gap between compute and storage performance and the growing complexity of parallel file/storage systems demand new approaches. We invite submissions on I/O characterization, data/storage management challenges, and novel optimization and management techniques (including ML/AI-enabled methods) that improve performance and usability.

Important Dates

Mon, Apr 13, 2026
Submission deadline
Mon, May 4, 2026
Notification to authors
Sat, May 16, 2026
Camera-ready paper due
Mon, Jul 13, 2026
Workshop date

Paper Submission

Research Paper Submission Tracks

I/o optimization
Emerging hpc workloads
Extreme scale i/o
Parallel file and storage systems
Understanding I/O inefficiencies in emerging workloads such as complex multi-step workflows, in-situ analysis, AI, and data analytics methods
New I/O optimization techniques, including how ML and AI algorithms might be adapted for intelligent load balancing and I/O pattern prediction of complex application workloads
Performance benchmarking and modeling, and I/O behavior studies of emerging workloads
New possibilities for the I/O optimization of emerging application workloads and their I/O subsystems
Efficient monitoring tools for metadata and storage hardware statistics at runtime, dynamic storage resource management, and I/O load balancing
Parallel file systems, metadata management, and complex data management
Understanding and efficiently utilizing complex storage hierarchies beyond the traditional two-tiered file system and archive model
User-friendly tools and techniques for managing data movement among compute and storage nodes
Use of staging areas, such as burst buffers or other private or shared acceleration tiers for managing intermediate data between computation tasks
Application of emerging big data frameworks towards scientific computing and analysis
Alternative data storage models, including object and key-value stores, and scalable software architectures for data storage and archive
Position papers on related topics

Call for Papers Description

All submitted papers should be formatted using the ACM Proceedings Style with sigconf format. Page limit: 5 to 8 pages (excluding references). All papers must be original and should not have appeared in or be simultaneously under consideration for a different workshop, conference or journal. All papers will be peer-reviewed using a single-blind peer-review process by at least three members of the program committee.

Committee

Organizing Committee

Arnab K. Paul

BITS Pilani, K K Birla Goa Campus, India

Sarah M. Neuwirth

Johannes Gutenberg University Mainz, Germany

Jay Lofstead

Sandia National Laboratories, USA

Program committee

Hadeel Albahar

Kuwait University, Kuwait

Francieli Zanon Boito

Université de Bordeaux / Inria, France

Jalil Boukhobza

ENSTA-Bretagne, France

Suren Byna

The Ohio State University, USA

Hariharan Devarajan

Lawrence Livermore National Laboratory, USA

Adrian Jackson

University of Edinburgh / EPCC, Scotland

Hideyuki Kawashima

Keio University, Japan

Awais Khan

Oak Ridge National Laboratory, USA

Youngjae Kim

Sogang University, South Korea

Radita Liem

Johannes Gutenberg University Mainz, Germany

Ricardo Macedo

INESC TEC & University of Minho, Portugal

Ramon Nou

Barcelona Supercomputing Center, Spain

Kento Sato

RIKEN R-CCS, Japan

Ahmad Tarraf

TU Darmstadt, Germany

Osamu Tatebe

University of Tsukuba, Japan

Francois Tessier

Inria Rennes, France

Lipeng Wan

Georgia State University, USA

Chen Wang

Nanyang Technological University, Singapore

Orcun Yildiz

Argonne National Laboratory, USA

Karim Youssef

Lawrence Livermore National Laboratory, USA

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