REX-IO 2023 : 3rd Workshop on Re-envisioning Extreme-Scale I/O for Emerging Hybrid HPC Workloads @ IEEE Cluster 2023
Call For Papers
Call for Papers
REX-IO 2023: 3rd Workshop on Re-envisioning Extreme-Scale I/O for
Emerging Hybrid HPC Workloads
Held in conjunction with IEEE Cluster 2023, Santa Fe, NM, USA.
Workshop Date: October 31, 2023
Scope, Aims, and Topics
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, but are not limited to:
- 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
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. Full paper submissions should not exceed 6 single-spaced, double-column pages using 10-point size font on 8.5 X 11 inch pages (IEEE conference style, https://www.ieee.org/conferences/publishing/templates.html) including everything excluding references.
Papers are to be submitted electronically in PDF format through EasyChair. Submitted papers should not have appeared in or be under consideration for a different workshop, conference or journal. It is also expected that all accepted papers will 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 2023 proceedings.
Submission Link: https://easychair.org/conferences/?conf=rexio23
Please note: All deadlines and dates are Anywhere on Earth
- Submissions open: May 26, 2023
- Submission deadline: August 21, 2023, 11:59PM AoE (FINAL DEADLINE)
- Notification to authors: September 04, 2023
- Camera-ready paper due: September 11, 2023
- Workshop date: October 31, 2023
- Arnab K. Paul (BITS Pilani, K K Birla Goa Campus, India)
- Sarah M. Neuwirth (Goethe University Frankfurt, Germany)
- Jay Lofstead (Sandia National Laboratories, USA)
- Phil Carns (Argonne National Laboratory, USA)
- Wei Der Chien (The University of Edinburgh, UK)
- Hariharan Devarajan (Lawrence Livermore National Laboratory, USA)
- Shadi Ibrahim (National Institute for Research in Digital Science and Technology (Inria), France)
- Radita Liem (RWTH Aachen, Germany)
- Glenn Lockwood (Microsoft, USA)
- Ricardo Macedo (INESC TEC & University of Minho, Portugal)
- Preeti Malakar (Indian Institute of Technology Kanpur, India)
- Sebastian Oeste (TU Dresden, Germany)
- Kento Sato (RIKEN, Japan)
- Houjun Tang (Lawrence Berkeley National Laboratory, USA)
- François Tessier (National Institute for Research in Digital Science and Technology (Inria), France)
- Marc-André Vef (Johannes Gutenberg University Mainz, Germany)
- Lipeng Wan (Georgia State University, USA)
- Chen Wang (Lawrence Livermore National Laboratory, USA)
- Mai Zheng (Iowa State University, USA)
All questions about submissions should be emailed to (rexio23 AT easychair DOT org)