IEEE Cluster 2023 is the 25th edition of the IEEE Cluster conference series. It is being held in cooperation with SIGHPC.

Clusters remain the primary system architecture for building many of today’s rapidly evolving computing infrastructures including high-performance computing, cloud computing and big data, and are used to solve some of the most complex problems. The challenges to make them scalable, efficient, productive, and increasingly effective requires a community effort 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

Following the successes of previous IEEE Cluster conferences, for IEEE Cluster 2023, which will be held Oct 31 - Nov 3, 2023 in Santa Fe, New Mexico, USA, 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. Research papers must clearly demonstrate novel research contributions while papers reporting experiences 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. The papers should be submitted as a full 10-page paper submission. Please note that references are not counted in the limits on the number of pages.

IEEE Cluster 2023 will use a double-blind review process, which is a change from previous years. For an explanation of this process, please refer to the following link: https://clustercomp.org/2023/double_blind.html

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 visualization tools
    Big Data application studies on cluster architectures