REX-IO '26

6th Workshop on Re-envisioning Extreme-Scale I/O for Emerging Hybrid HPC Workloads

Call for Papers:

High Performance Computing (HPC) applications are evolving to include not only traditional modeling and simulation bulk-synchronous scale-up workloads but also scale-out workloads, including artificial intelligence (AI), big data analytics methods, deep learning, and complex multi-step workflows. With the advent of Exascale systems such as Frontier, workflows include multiple different components from both scale-up and scale-out communities operating together to drive scientific discovery and innovation. With the often conflicting design choices between optimizing for write- vs. read-intensive, having flexible I/O systems is crucial to support hybrid workloads. Another performance aspect is the intensifying complexity of parallel file and storage systems in large-scale cluster environments. Storage system designs are advancing beyond the traditional two-tiered file system and archive model by introducing new tiers of temporary, fast storage close to the computing resources with distinctly different performance characteristics.

The changing landscape of emerging hybrid HPC workloads along with the ever increasing gap between the compute and storage performance capabilities reinforces the need for an in-depth understanding of extreme-scale I/O and for rethinking existing data storage and management techniques. Traditional approaches of managing data might fail to address the challenges of extreme-scale hybrid workloads. Novel I/O optimization and management techniques integrating machine learning and AI algorithms, such as intelligent load balancing and I/O pattern prediction, are needed to ease the handling of the exponential growth of data as well as the complex hierarchies in the storage and file systems. Furthermore, user-friendly, transparent and innovative approaches are essential to adapt to the needs of different HPC I/O workloads while easing the scientific and commercial code development and efficiently utilizing extreme-scale parallel I/O and storage resources.

Established at IEEE Cluster 2021, the Re-envisioning Extreme-Scale I/O for Emerging Hybrid HPC Workloads (REX-IO) workshop has created a forum for experts, researchers, and engineers in the parallel I/O and storage, compute facility operation, and HPC application domains. From next year, we will continue this tradition at the ACM HPDC 2026 Conference! REX-IO solicits novel work that characterizes I/O behavior and identifies the challenges in scientific data and storage management for emerging HPC workloads, introduces potential solutions to alleviate some of these challenges, and demonstrates the effectiveness of the proposed solutions to improve I/O performance for the exascale supercomputing era and beyond. We envision that this workshop will continue contributing to the community and further drive discussions between storage and I/O researchers, HPC application users and the data analytics community to give a better in-depth understanding of the impact on the storage and file systems induced by emerging HPC applications.

Topics of Interest Include:

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

Important Dates: (All Dates are Anywhere on Earth)

Submissions open: February 2, 2026
Submission deadline: March 31, 2026
Notification to authors: April 30, 2026
Camera-ready paper due: TBA
Author Registration due: TBA
Workshop date: July 13, 2026

Paper Submission Guidelines:

All submitted papers should be formatted using the ACM Master Template with sigconf format (please be sure to use the current version). The necessary document can be found here: https://www.acm.org/publications/proceedings-template
Page limits: TBD
All papers must be original and should not have appeared in or be simultaneously under consideration for a different workshop, conference or journal.
Indicate all authors and affiliations.
All papers will be peer-reviewed using a single-blind peer-review process by at least three members of the program committee.
Submissions must be in English and PDF format.
Papers must be submitted via the REX-IO 2026 submission site: TBA

Note that all accepted papers must be presented at the workshop by one of the authors. All accepted papers (subject to post-review revisions) will be published in the HPDC 2026 workshop proceedings.

Policy on the use of AI:

HPDC follows the ACM Policy on Authorship for the use of AI in authoring papers. A comprehensive FAQ on the use of AI in publications is available from ACM: https://www.acm.org/publications/policies/frequently-asked-questions.

Authors should note that the use of Generative AI tools is acceptable. However, authors should also note that they are fully responsible for the correctness and accuracy of AI-generated content in their papers. The use of Generative AI tools in the paper must be disclosed in accordance with ACM policy. We recommend that authors who choose to use Generative AI tools (such as ChatGPT, Claude, CoPilot, Grammarly, etc.) do so only for the purpose of improving the reading quality of their text and not for generating technical content.

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