[hpc-announce] CFP: REX-IO Workshop at IEEE Cluster 2022 - Submission deadline extended to July 8, 2022

Arnab Kumar Paul akpaul at vt.edu
Tue Jun 28 04:27:27 CDT 2022

**[Please accept our apologies if you receive multiple copies of this


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
- 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

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. 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

Important Dates
- Submissions open: May 3, 2022
- Submission deadline: July 8, 2022, 11:59PM AoE
- Notification to authors: July 20, 2022
- Camera-ready paper due: July 25, 2025
- Workshop date: September 6, 2022

Workshop Committees
Workshop Co-Chairs:
- 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

Program Committee:
- 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),
- Suren Byna (Lawrence Berkeley National Laboratory, USA)
- Phil Carns (Argonne National Laboratory, USA)
- Yue Cheng (George 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)

Arnab K. Paul,
Assistant Professor,
Department of Computer Science and Information Systems,
BITS Pilani, KK Birla Goa Campus
Personal Website <https://arnabkrpaul.github.io/>

More information about the hpc-announce mailing list