REX-IO '22: Workshop on Re-envisioning Extreme-Scale I/O for Emerging Hybrid HPC Workloads Heidelberg University, respectively its “New University” building Heidelberg, Germany, September 6, 2022 |
Conference website | https://sites.google.com/view/rexio/ |
Submission link | https://easychair.org/conferences/?conf=rexio22 |
Abstract registration deadline | July 10, 2022 |
Submission deadline | July 10, 2022 |
Notifications | July 21, 2022 |
High Performance Computing (HPC) applications are evolving to include not only traditional scale-up modeling and simulation bulk-synchronous workloads but also scale-out workloads like artificial intelligence (AI), data analytics methods, deep learning, big data and complex multi-step workflows. Exascale workflows are projected to 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-intensive vs. read-intensive, having flexible I/O systems will be crucial to support these 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.
Submission Guidelines
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. Manuscript for full paper should not exceed 6 single-spaced, double-column pages using 10-point size font on 8.5 X 11 inch pages (IEEE conference style) 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 2022 proceedings.
Important Dates
Please note: All Dates are Anywhere on Earth
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Submissions open: May 3, 2022
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Submission deadline:
July 1, 2022July 10, 2022 (FINAL EXTENSION) -
Notification to authors: July 21, 2022
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Camera-ready paper due: July 25, 2022
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Workshop date: September 6, 2022
List of Topics
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Understanding I/O inefficiencies in emerging workloads such as complex multi-step workflows, in-situ analysis, AI, and data analytics methods
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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
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Performance benchmarking, resource management, and I/O behavior studies of emerging workloads
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New possibilities for the I/O optimization of emerging application workloads and their I/O subsystems
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Efficient tools for the monitoring of metadata and storage hardware statistics at runtime, dynamic storage resource management, and I/O load balancing
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Parallel file systems, metadata management, and complex data management
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Understanding and efficiently utilizing complex storage hierarchies beyond the traditional two-tiered file system and archive model
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User-friendly tools and techniques for managing data movement among compute and storage nodes
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Use of staging areas, such as burst buffers or other private or shared acceleration tiers for managing intermediate data between computation tasks
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Application of emerging big data frameworks towards scientific computing and analysis
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Alternative data storage models, including object and key-value stores, and scalable software architectures for data storage and archive
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Data movement for HPC on edge devices
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Position papers on related topics
Committees
Program Committee
- TBD
Organizing committee
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Arnab K. Paul (BITS Pilani, K K Birla Goa Campus, India)
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Sarah M. Neuwirth (Goethe-University Frankfurt, Germany)
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Jay Lofstead (Sandia National Laboratories, USA)
Contact
All questions about submissions should be emailed to <rexio22 AT easychair DOT org>