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High Performance Computing for Similarity on Massive & Complex data
Many scientific fields require the analysis of large volumes of sequence-type data of varying complexity (e.g., periodicity, completeness, multivariate nature), with a particular focus on measuring and exploiting similarity. Application domains range from medicine (e.g., patient stratification, gene alignment) to social sciences (e.g., semantic trajectory analysis), and data science (e.g., generation and recommendation of exploration pipelines). However, as data grow in both volume and complexity, traditional approaches to similarity analysis face critical challenges. Without the use of parallel and/or high-performance computing (HPC), many of these approaches become intractable, either due to the scale of the data or the computational intensity of the algorithms involved.
The HPCoSiMC special session addresses the growing need for efficient similarity computations on massive and complex sequence-type data. It highlights innovative strategies that harness modern parallel platforms to overcome two key bottlenecks: handling large-scale data volumes and managing computational complexity. Topics of interest include methods for exploiting advanced parallel architectures, algorithmic adaptations enabling scalable execution, and user-oriented approaches that simplify access to high-performance computing for non-experts.
Call for papers
The objective of this special session is to gather contributions addressing the challenges of similarity studies, particularly those arising from computational complexity and the massive volume of data involved. We encourage submissions on the following topics, which are not limited to the following:
Parallelization models and algorithms for large-scale similarity analysis on massive datasets
Leveraging accelerator architectures such as GPUs and FPGAs to optimize similarity computations
Hybrid and heterogeneous computing approaches (multi-core, distributed-memory, GPU, accelerators) for similarity tasks
Implicit parallelism models for similarity analysis on sequence-type data, workflow design, and optimization for large-scale similarity pipelines
Tools and frameworks to make HPC-based similarity analysis accessible to non-specialists
Benchmarking and performance evaluation of similarity algorithms on modern parallel architectures
Dimensionality reduction and indexing methods for scalable similarity search
Novel and emerging applications that benefit from similarity computations
Embedding techniques for efficient similarity search
New methods and metrics for measuring similarity across application domains
Proposed Schedule
Paper submission : November 28th 2025
Author notification : January 5th 2026
Camera-ready copy : January 26th 2026 (estimated)
Submission guidelines
Submission guidelines
Authors should submit a full paper not exceeding 8 pages in the IEEE Conference proceedings format (IEEEtran, double-column, 10pt) and follow format guidelines found at https://www.ieee.org/conferences/publishing/templates.html.
For submission, please refer to the Easychair submission system as indicated in the Main Conference webpage, and make sure that you select the “High Performance Computing for Similarity on Massive & Complex data (HPCoSiMC)” track.
Double-bind review: the first page of the paper should contain only the title and abstract; in the reference list, references to the authors own work should appear as “omitted for blind review” entries.
Program Committee members
Mostafa Bamha (Université d’Orléans, France)
Christel Dartigues (Université Côte d’Azur, France)
Laurent D’Orazio (Université de Rennes, France)
Pratik Gajane (Université d’Orléans, France)
Mike Gowanlock (Northern Arizona University, US),
Sébastien Limet (Université d’Orléans, France)
Patrick Marcel (Université d’Orléans, France)
Martin Musicante (Universidade Federal do Rio Grande do Norte, Brasil),
Wagner M. Nunan Zola (Universidade Federal do Paraná, Brasil),
Verónika Peralta (Université de Tours, France)
Sophie Robert (Université d’Orléans, France)
Massimo Torquati (University of Pisa, Italy)
Contacts - Special session chairs
Mike Gowanlock (Michael.Gowanlock@nau.edu)
Verónika Peralta (veronika.peralta@univ-tours.fr)
Sophie Robert (sophie.robert@univ-orleans.fr)
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