[Ieee_vis] CFP for ISAV: In Situ AI, Analysis and Visualization at #SC25

[Matthew Larsen]

https://isav-workshop.github.io/2025/

In conjunction with: SC25, The International Conference for High
Performance Computing, Networking, Storage, and Analysis
<https://sc25.supercomputing.org/>
Workshop Theme <https://isav-workshop.github.io/2025/#workshop-theme>

As HPC platforms and applications increase significantly in size,
complexity, and heterogeneity, one major challenge is the widening gap
between computation and our ability to gain insight from extreme-scale data
and make timely, data-driven decisions. A well-known, yet challenging,
approach is in situ processing – performing as much analysis as possible
while computed data is still resident in memory.

This is the 11th year of the ISAV workshop, and we are expanding the
workshop’s scope to In Situ AI, Analysis and Visualization. Furthermore, we
are expanding the workshop’s technical program to now accept full paper
submissions. We celebrate that in situ processing has evolved from research
efforts to a central component in supercomputer, cloud and edge
applications. In situ methods are in high demand:

   - in training ML/AI surrogate models or leveraging ML/AI models for
   analysis,
   - in system-scale 3D visualization for the latest Exascale
   supercomputers,
   - in cloud products providing responsive user experiences,
   - in tightly coupled digital twins, and in computational sciences.

Each one of these examples has a different set of requirements in response
time, data throughput and complexity of data pipelines, and more
exploration in the in situ space is needed to address multifaceted goals:
(1) to preserve important elements of simulations, (2) to significantly
reduce the data needed to preserve these elements, (3) to offer as much
flexibility as possible for post-processing exploration, and (4) to
accelerate the gathering of insights to be fast enough to make timely
decisions based on it.

ISAV’25 brings together the broader HPC community and researchers,
developers and practitioners from industry, academia, and government
laboratories who are developing, applying, and deploying scalable in situ
methods at any high performance platform. The goal is to present research
findings, lessons learned, and insights related to developing and applying
in situ methods across a range of science and engineering applications in
scalable environments; to discuss topics like opportunities presented by
new workflows in AI/ML, modeling, data processing, emerging architectures,
infrastructure needs, requirements, and gaps, and experiences to foster and
enable in situ AI, analysis, and visualization. Since its inception in
2015, ISAV has fostered and catered a diverse audience and supported early
career members, becoming a “center of gravity” for researchers,
practitioners, and users/consumers of in situ methods, software and
integrations in the HPC space. Through presentations and discussions of
research findings, lessons learned, and early ideas, ISAV illuminates new
requirements and gaps driven by science and engineering applications, and
fosters the community members and knowledge base around the development and
application of in situ methods with its peer-reviewed proceedings.
Participation/Call for Papers
<https://isav-workshop.github.io/2025/#participationcall-for-papers>

In its 11th year ISAV is expanding in scope and technical focus, and now
invites full paper submissions up to 10 pages (including references) and
works on in situ AI/ML training or inference. ISAV also continues to invite
short papers (5 page + 1 page references) and lightning talk abstracts (1
page).

Full papers should present research results, identity opportunities or
challenges, or present case studies/best practices for in situ methods.
Short papers may also document late breaking ideas & early progress on
novel concepts. Lightning talks are encouraged to present preliminary works
or ideas to foster discussion with the community. Full and short papers
will appear in the workshop proceedings and authors will be invited to give
an oral presentation at the workshop; lightning talks will be invited to
give brief oral presentations at the workshop.

Submissions of all types may identify opportunities, challenges and best
practices for in situ AI/ML, in situ analysis and in situ visualization.
They may propose new methods and techniques, provide positions, or
experience reports on in situ analysis, learning and visualization. Areas
of interest for ISAV include, but are not limited to:

   - Methods, Algorithms and Synthesis between HPC & ML: In situ analysis
   (feature detection, data reduction/compression, data summarization, ML
   training) and scientific visualization using data-driven,
   surrogate-assisted, statistical, temporal, geometric, or time-varying
   methods.
   - Applications and Workflows: Applications (simulations, data
   processing, scientific user facilities) and integrations into digital
   twins. Workflows for supporting complex in situ processing pipelines (incl.
   enabling accelerated post-processing and elasticity), their resilience
   (error detection, data congestion, fault recovery) and reproducibility.
   - Scalability Requirements: Scalability, resource utilization, data
   flow, and simplified access to extreme heterogeneous resources. Real-time
   coupling of data (modeled or measured), surrogates and algorithms.
   - Case Studies, Data Sources and Best Practices & Usability:
   Examples/case studies of solving a specific science challenge with in situ
   methods/infrastructure. In situ methods/systems applied to data from
   simulations, and/or observations/experiments. Deployments & software
   engineering.
   - Software Evolution & Standardization: In situ libraries from research
   prototypes to production quality. Challenges, opportunities, gaps in
   existing capabilities. API designs and development of community standards.
   - Enabling Hardware & Emerging Architectures: Hardware & emerging system
   architectures that provide opportunities for in situ processing. Efficient
   use of hardware accelerators and heterogeneous architectures, incl. HPC,
   Data Center or Edge.
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://listserv.uni-tuebingen.de/pipermail/ieee_vis/attachments/20250519/996cc6af/attachment-0001.html>