REX-IO '25

5th 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. 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 contribute 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, hybrid 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: May 14, 2025
Submission deadline: July 9, 2025 July 25, 2025
Notification to authors: August 7, 2025 August 9, 2025
Camera-ready paper due: August 14, 2025
Author Registration due: August 18, 2025
Workshop date: September 2, 2025

Paper Submissions:

All papers must be original and not simultaneously submitted to another journal or conference. 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 should be a complete manuscript. REX-IO accepts traditional research papers (page limit: 8 pages + 2 additional pages) for in-depth topics and short papers (page limit: 4 pages + 1 additional page) for work in progress on hot topics. Page limits include figures and tables, but exclude references.

Paper format: single-spaced double-column pages using 10-point size font on 8.5x11 inch pages (IEEE conference style). The submitted manuscripts should include author names and affiliations. The IEEE conference style templates for MS Word and LaTeX provided by IEEE eXpress Conference Publishing are available here: https://www.ieee.org/conferences/publishing/templates.html

Papers are to be submitted electronically in PDF format. Submitted papers should not have appeared in or be under consideration for a different workshop, conference or journal. All accepted papers need to be presented at the workshop by one of the authors.

All accepted papers (subject to post-review revisions) will be published in the IEEE Cluster 2025 companion proceedings.

Submission Link: https://easychair.org/conferences/?conf=rexio25

Guidelines for Artificial Intelligence (AI)-Generated Text

The use of content generated by artificial intelligence (AI) in a paper (including but not limited to text, figures, images, and code) shall be disclosed in the acknowledgments section of any paper submitted to an IEEE publication. The AI system used shall be identified, and specific sections of the paper that use AI-generated content shall be identified and accompanied by a brief explanation regarding the level at which the AI system was used to generate the content.

The use of AI systems for editing and grammar enhancement is common practice and, as such, is generally outside the intent of the above policy. In this case, disclosure as noted above is recommended.

Further information about the IEEE Submission Policies:
https://conferences.ieeeauthorcenter.ieee.org/author-ethics/guidelines-and-policies/submission-policies/

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