IEEE Cluster 2024 is the 26th 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 2024, which will be held September 24--27, 2024 in Kobe, Japan, 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 2024 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/2024/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