This international journal is directed to researchers, engineers, educators, managers, programmers, and users of computers who have particular interests in parallel processing and/or distributed computing.

The Journal of Parallel and Distributed Computing publishes original research papers and timely review articles on the theory, design, evaluation, and use of parallel and/or distributed computing systems. The journal also features special issues on these topics; again covering the full range from the design to the use of our targeted systems.

Research Areas Include:

• Theory of parallel and distributed computing
• Parallel algorithms and their implementation
• Innovative computer architectures
• Parallel programming
• Applications, algorithms and platforms for accelerators
• Cloud, edge and fog computing
• Data-intensive platforms and applications
• Parallel processing of graph and irregular applications
• Parallel and distributed programming models
• Software tools and environments for distributed systems
• Algorithms and systems for Internet of Things
• Performance analysis of parallel applications
• Architecture for emerging technologies e.g., novel memory technologies, quantum computing
• Application-specific architectures e.g., accelerator-based and reconfigurable architecture
• Interconnection network, router and network interface architecture

Special Issue on Dynamic Resource Management in HPC
Submission Date: 2026-03-31

As High-Performance Computing (HPC) systems approach the exascale era and beyond, the complexity of applications, architectures, and workloads is rapidly increasing. Traditional static resource allocation models—where resources are fixed at job launch—are proving inadequate to fully exploit the potential of modern HPC infrastructures.

Emerging applications such as multi-physics simulations, large-scale AI training, digital twins, and adaptive mesh refinement demand dynamic and irregular resource usage. At the same time, HPC systems are becoming increasingly heterogeneous and distributed, integrating CPUs, GPUs, FPGAs, SmartNICs, and even quantum accelerators, all within energy-constrained environments.In this context, Dynamic Resource Management (DRM) is a key enabler for performance, scalability, energy efficiency, and system throughput. DRM allows applications and runtimes to adapt resource usage at runtime, meeting the needs of future scientific and industrial workloads.

This special issue aims to provide a timely forum for consolidating advances in DRM and fostering cross-fertilization between the HPC, cloud, and distributed computing communities. We invite original contributions that advance the theory, algorithms, software, and systems for dynamic resource management in HPC.

Guest editors:

Dr. Sergio Iserte
Barcelona Supercomputing Center, Spain
His research interests lie mainly in the following areas:

    Parallel distributed programming models
    Dynamic processes and resources management
    HPC workload modeling
    In-network acceleration
    Applied Artificial Intelligence

Verónica G. Melesse Vergara
Oak Ridge National Laboratory, USAHer research interests lie mainly in the following areas:

    HPC systems architecture
    Parallel programming models
    Large-scale HPC system testing and benchmarking
    Performance evaluation and optimization of scientific applications
    Machine learning/artificial intelligence applied to systems biology

Prof. Miwako TsujiRiken, Japan
Her research interests are:

    Programming Model
    Performance Model
    Quantum and HPC integration, middleware, and programming environment

Special issue information:

Topics include, but are not limited to:

    Dynamic (malleable and elastic) parallel applications: programming models, runtime support, and case studies
    Dynamic Load Balancing (DLB) and integration with DRM strategies
    Middleware and runtime frameworks enabling resource reallocation at scale
    Scheduling and resource managers supporting dynamic jobs in HPC and exascale systems
    Adaptive workflows and pipelines spanning edge-to-cloud-HPC environments
    Energy- and power-aware DRM
    Integration with heterogeneous resources (GPUs, FPGAs, SmartNICs, quantum accelerators)
    AI-driven decision making for adaptive resource allocation
    Resilience and fault tolerance under dynamic resource reconfiguration
    Case studies and real-world applications of DRM in science and industry