[hpc-announce] CFP: REX-IO Workshop at IEEE Cluster 2022 - Submissions due July 1, 2022

Sarah Neuwirth s.neuwirth at em.uni-frankfurt.de
Sun May 22 12:07:40 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 1, 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 gov>

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 (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)

Dr. Sarah M. Neuwirth
Goethe-University Frankfurt | Campus Riedberg
Modular Supercomputing and Quantum Computing
FIAS Building | Room 2.403 | Ruth-Moufang-Str. 1
60438 Frankfurt am Main | Germany
Phone: +49 (0)69 798-47533
Email: s.neuwirth at em.uni-frankfurt.de

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