[hpc-announce] [Call for Papers] REX-IO Virtual Workshop at IEEE Cluster 2021 - Submissions due July 2, 2021

[Arnab Kumar Paul]

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

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Call for Papers

REX-IO 2021: 1st Workshop on Re-envisioning Extreme-Scale I/O for
Emerging Hybrid HPC Workloads

Held in conjunction with IEEE Cluster 2021.

September 7, 2021

<https://sites.google.com/view/rexio/>

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Scope, Aims, and Topics
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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.

The Re-envisioning Extreme-Scale I/O for Emerging Hybrid HPC Workloads
(REX-IO) workshop 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. We envision that this workshop will contribute to the community
by analyzing emerging hybrid workloads, recognizing the gap in the data
management methodologies, and providing novel techniques to improve I/O
performance for the exascale supercomputing era and beyond. This workshop
will also provide a platform to facilitate 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
- Position papers on related topics

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Submission Guidelines
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All papers must be original and not simultaneously submitted to another
journal or conference. Indicate all authors and affiliations. All
manuscripts will be reviewed by at least three members of the program
committee. Submissions should be a complete manuscript. Manuscripts shall
not exceed six (6) single-spaced, double-column pages using 10-point size
font on 8.5X11 inch pages (IEEE conference format,
https://www.ieee.org/conferences/publishing/templates.html) including text
and figures 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 2021 proceedings.

Submission Link: https://easychair.org/conferences/?conf=rexio21

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Important Dates
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- Submission deadline: July 2, 2021
- Notification to authors: July 26, 2021
- Camera-ready paper due: July 30, 2021
- Workshop date: September 7, 2021

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Workshop Committees
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Workshop Co-Chairs:
- Arnab K. Paul (Oak Ridge National Laboratory, USA) <paula AT ornl DOT gov>
- 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:
- Thomas Boenisch (High-Performance Computing Center Stuttgart (HLRS),
Germany)
- Sarp Oral (Oak Ridge National Laboratory, USA)
- Feiyi Wang (Oak Ridge National Laboratory, USA)
- Ali R. Butt (Virginia Tech, USA)
- Bing Xie (Oak Ridge National Laboratory, USA)
- Yue Cheng (George Mason University, USA)
- Ali Anwar (IBM Research, USA)
- Nannan Zhao (Northwestern Polytechnical University, China)
- Elsa Gonsiorowski (Lawrence Livermore National Laboratory, USA)
- Julian Kunkel (University of Reading, UK)
- Ryan Chard (Argonne National Laboratory, USA)
- Jean Luca Bez (Federal University of Rio Grande do Sul (UFRGS), Brazil)
- Houjun Tang (Lawrence Berkeley National Laboratory, USA)
- Huan Ke (The University of Chicago, USA)
- Luna Xu (IBM Research, USA)
- Sandra Mendez (Barcelona Supercomputing Center (BSC), Spain)
- Wolfgang Frings (Jülich Supercomputing Centre (JSC), Germany)
- Esteban Rangel (Argonne National Laboratory (ANL), USA)

Regards,
Arnab K. Paul, Ph.D.
Postdoctoral Research Associate,
Oak Ridge National Laboratory, USA
Website: https://arnabkrpaul.github.io/