REX-IO 2022 : 2nd Workshop on Re-envisioning Extreme-Scale I/O for Emerging Hybrid HPC Workloads @ IEEE Cluster 2022
Call For Papers
Call for Papers
REX-IO 2022: 2nd Workshop on Re-envisioning Extreme-Scale I/O for
Emerging Hybrid HPC Workloads
Held in conjunction with IEEE Cluster 2022, Heidelberg, Germany.
Workshop Date: September 6, 2022
Scope, Aims, and Topics
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.
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, resource management, 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 tools for the monitoring of 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
- Data movement for HPC on edge devices
- 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 2022 proceedings.
Submission Link: https://easychair.org/conferences/?conf=rexio22
- Submissions open: May 3, 2022
- Submission deadline: July 10, 2022, 11:59PM AoE (FINAL EXTENSION)
- Notification to authors: July 21, 2022
- Camera-ready paper due: July 25, 2025
- Workshop date: September 6, 2022
- Arnab K. Paul (BITS Pilani, K K Birla Goa Campus, India) (arnabp AT goa DOT bits-pilani DOT ac DOT in)
- Sarah M. Neuwirth (Goethe-University Frankfurt, Germany) (s.neuwirth AT em DOT uni-frankfurt DOT de)
- Jay Lofstead (Sandia National Laboratories, USA) (gflofst AT sandia DOT gov)
- Ali Anwar (University of Minnesota, USA)
- Scott Atchley (Oak Ridge National Laboratory, USA)
- Jean Luca Bez (Lawrence Berkeley National Laboratory, USA)
- Thomas Boenisch (High-Performance Computing Center Stuttgart (HLRS), Germany)
- Suren Byna (Lawrence Berkeley National Laboratory, USA)
- Phil Carns (Argonne National Laboratory, USA)
- Yue Cheng (Goerge Mason University, USA)
- Wei Der Chien (The University of Edinburgh, UK)
- Hariharan Devarajan (Lawrence Livermore National Laboratory, USA)
- Awais Khan (Oak Ridge National Laboratory, USA)
- Youngjae Kim (Sogang University, South Korea)
- Julian Kunkel (Georg-August-Universität Göttingen/GWDG, Germany)
- Ricardo Macedo (INESC TEC & University of Minho, Portugal)
- Houjun Tang (Lawrence Berkeley National Laboratory, USA)
- Bing Xie (Oak Ridge National Laboratory, USA)
- Nannan Zhao (Northwestern Polytechnical University, China)
- Mai Zheng (Iowa State University, USA)