IEEE Cluster 2022 is the 24th edition of the IEEE Cluster conference series.
Clusters remain the primary system architecture for building many of today’s rapidly evolving computing infrastructures 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 2022, which will be held September 6 – 9, 2022 in Heidelberg, Germany, 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 may be submitted as either a full 10-page paper or as a shorter 4-page paper submission. Please note that references are not counted in the limits on the number of pages and a 10-page submission may be accepted with a caveat of transforming it into a 4-page version for presentation at the conference.
IEEE Cluster 2022 will use a double-blind review process, which is a change from previous years. For an explanation of this process, please visit this page.
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)
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
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
Cluster system-level protocols and APIs
Resource and job management
Programming and software development environments on clusters
Fault tolerance and high-availability
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
Programming models for Big Data processing
Big Data application studies on cluster architectures
Submissions must be in PDF format and must conform to the following Xplore layout, page limit, and font size.
Submissions are required to be no more than 10 pages (excluding references).
Submissions must be single-spaced, 2-column numbered pages in IEEE Xplore format (8.5×11-inch paper, margins in inches – top: 0.75, bottom: 1.0, sides:0.625, and between columns:0.25, main text: 10pt).
Papers will be reviewed double-blind. Author names and affiliations should NOT be included in the submitted paper. For additional guidelines read the double blind review policy.