IEEE Cluster 2025 is the 27th edition of the IEEE Cluster conference series. It is being held in cooperation with SIGHPC.

Computing clusters remain the primary system architecture for building many of today’s rapidly evolving computing infrastructures including high-performance computing, cloud computing, machine learning training and inference systems, and big data, and are used to solve some of the most complex problems. The challenges posed making them scalable, efficient, productive, and increasingly effective require community efforts in the areas of cluster system design, advancing the capabilities of the software stack, system management and monitoring, and the design of algorithms, methods, and applications to leverage the overall infrastructure.

For IEEE Cluster 2025, which will be held September 2-5, 2025 in Edinburgh, United Kingdom, we again solicit high-quality original work that advances the state-of-the-art in clusters and closely related fields.

All papers will be rigorously peer-reviewed for their originality, technical depth and correctness, potential impact, relevance to the conference, and quality of presentation. Generally research papers must clearly demonstrate novel research contributions, however papers reporting experiences are also welcome, but they must clearly describe the lessons learned and the resulting impact, along with the utility of the approach in comparison to previous work.

Authors must indicate the primary topic area of their submissions from the four topic areas provided below. In addition, they may optionally rank their paper relative to the overall set of topics. Transversal and emerging topics such as AI for HPC, HPC for AI, quantum computing, accelerators, and many others, are welcome within the respective areas even if they are not mentioned explicitly. Papers are limited to 10 pages, although references do not need to fit within this page limit.

IEEE Cluster 2025 follows a dual-anonymous review process. For an explanation and description of this review process, please refer to the following link: https://clustercomp.org/2025/dual_anonymous.html

Guidelines for Artificial Intelligence (AI)-Generated Text

The use of content generated by artificial intelligence (AI) in a paper (including but not limited to text, figures, images, and code) shall be disclosed in the acknowledgments section of any paper submitted to an IEEE publication. The AI system used shall be identified, and specific sections of the paper that use AI-generated content shall be identified and accompanied by a brief explanation regarding the level at which the AI system was used to generate the content.

The use of AI systems for editing and grammar enhancement is common practice and, as such, is generally outside the intent of the above policy. In this case, disclosure as noted above is recommended.

Please also refer to the IEEE Submission Policies

Area 1: Application, Algorithms, and Libraries

    HPC and Big Data application studies on large-scale clusters
    Applications at the boundary of HPC and Big Data
    New applications for converged HPC/Big Data clusters
    Application-level performance and energy modeling and measurement
    Novel algorithms on clusters
    Hybrid programming techniques in applications and libraries (e.g., MPI+X)
    Cluster benchmarks
    Application-level libraries on clusters
    Effective use of clusters in novel applications
    Performance evaluation tools

Area 2: Architecture, Network/Communications, and Management

    Node and system architecture for HPC and Big Data clusters
    Architecture for converged HPC/Big Data clusters
    Energy-efficient cluster architectures
    Packaging, power and cooling
    Accelerators, reconfigurable and domain-specific hardware
    Heterogeneous clusters
    Interconnect/memory architectures
    Single system/distributed image clusters
    Administration, monitoring and maintenance tools

Area 3: Programming and System Software

    Cluster system software/operating systems
    Programming models for converged HPC/Big Data/Machine Learning systems
    System software supporting the convergence of HPC, Big Data, and Machine Learning processing
    Cloud-enabling cluster technologies and virtualization
    Energy-efficient middleware
    Cluster system-level protocols and APIs
    Cluster security
    Management of local, center-wide and disaggregate resources and job
    Programming and software development environments on clusters
    Fault tolerance and high-availability
    Administration, monitoring and maintenance tools

Area 4: Data, Storage, and Visualization

    Cluster architectures for Big Data storage and processing
    Middleware for Big Data management
    Cluster-based cloud architectures for Big Data
    Storage systems supporting the convergence of HPC and Big Data processing
    File systems and I/O libraries
    Support and integration of non-volatile memory
    Visualization clusters and tiled displays
    Big Data/Large scale visualization tools
    Big Data application studies on cluster architectures