Innovations and Advancements in Parallel and Distributed Systems

Dear Colleagues,

The rapid advancement of large-scale systems we have seen in recent years can be attributed to innovations in numerical methods, algorithms, hardware, and software. This progress has led to considerable enhancements in the efficiency and scalability of computations, achieved through the concurrent execution of tasks on individual machines and the utilization of graphics processors and specialized accelerators (e.g., TPUs), which facilitate massive parallelism in applications such as deep learning and scientific simulations.

Modern large-scale applications require a comprehensive approach to the design and implementation of what is known as co-design, taking into account the latest advancements in numerical methods, algorithms, hardware, and software. Consequently, the objective of this Special Issue is to gather the most recent achievements and novel solutions in the design, implementation, and application of parallel and distributed computing systems. This Special Issue welcomes contributions that support the transition toward exascale computing, with emphasis on scalable numerical methods, AI/ML integration, data-driven modelling, software stack development, and reproducibility.

We invite original research and comprehensive reviews focused on multi-core and heterogeneous architectures, distributed algorithms, scalable system design, parallel programming models, and cloud-to-edge infrastructures. Contributions at the intersection of applied mathematics and HPC—including topics such as reduced-order modelling, inverse problems, uncertainty quantification, and data assimilation—are also welcome. Submissions that present practical implementations of fundamental methods and algorithms through large-scale computations simulating real-world phenomena are especially encouraged. We also seek work exploring the convergence of distributed systems with artificial intelligence, and investigations into performance optimization, energy efficiency, fault tolerance, and reproducibility in large-scale simulations.

Suggested topics include (but are not limited to) the following:

• Applied mathematics, numerical methods, and algorithms for large-scale and exascale computing.
• Reduced-order modelling, surrogate models, and scientific machine learning methods for large-scale computing.
• Inverse problems and data assimilation at scale.
• Uncertainty quantification and sensitivity analysis in high-performance simulations.
• Computing in heterogeneous and accelerated-processing environments.
• Large-scale simulations with a particular emphasis on CFD and ABMS (Agent-Based Modelling Systems) areas.
• New and improved frameworks for high-performance problems.
• Efficient data distribution and scheduling for parallel/distributed applications.
• Exploration of co-design for HPC applications.
• Scalability and efficiency of simulation applications.
• Exploring emerging architectures for high-performance applications.
• HPC and HPDA platforms for efficient data processing.
• Parallel and distributed performance evaluation methodology: benchmarking, profiling, and reproducibility.
• Performance optimization, precision tuning, and energy efficiency in large-scale applications.
• Data analytics and AI solution advancements in high-fidelity problems.
• Efficient data storage and transfer for HPC and HPDA systems.
• Workflows for complex scenarios processing.
• Tools and platforms for immersive visualization of large-scale simulation data.
• Fault tolerance, replication, and recovery strategies in distributed systems.
• Integration of machine learning and AI models in distributed and hybrid systems.
• Containerization and orchestration, and CI/CD strategies for scientific workflows.
• Demonstrator, mini-app, and proxy-app development for real-world use cases and benchmarking.

Dr. Marcin Lawenda
Dr. Harald Köstler
Prof. Dr. Christophe Prud’homme
Guest Editors

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