High Performance Computing for Similarity on Massive & Complex data (HPCoSiMC)
HPCoSiMC website:
Aims and Scope
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 the 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 limitations.
Without the use of parallel and/or high-performance computing (HPC), many of these studies 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.
Topics of interest include (not limited to) the following:
- Parallelization models 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 nonspecialists
- 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
Important dates
Paper submission: November 28th 2025
Author notification: January 5th 2026
Camera-ready copy: January 25th 2026
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.
Chairs
Mike Gowanlock, Northern Arizona University, USA
Sophie Robert, Université d’Orléans, France (contact: sophie.robert@univ-orleans.fr)
Verónika Peralta, Université de Tours, France
Program Committee
Mike Gowanlock, Northern Arizona University, USA
Wagner M. Nunam Zola, Universidade Federal do Paraná, Brésil
Martin Musicante, Universidade Federal do Rio Grande do Norte, Brésil
Christel Dartigues, Université Côte d’Azur, France
Laurent D’Orazio, Université de Rennes, France
Pratik Gajane, Université d’Orléans, France
Sébastien Limet, Université d’Orléans, France
Patrick Marcel, Université d’Orléans, France
Sophie Robert, Université d’Orléans, France
Verónika Peralta, Université de Tours, France