Innovations and Advancements in Parallel and Distributed Systems

Dear Colleagues,

The rapid advancement of large-scale systems noted in recent years can be attributed to innovations in both 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 necessitate a holistic approach to the design and implementation of what is termed co-design, which considers the latest advancements provided by both 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. Original research and comprehensive reviews focusing on the evolution of multi-core and heterogeneous architectures, including those utilizing accelerators, will be of particular interest. We also invite studies on distributed algorithms, scalable system design, big data processing frameworks, parallel programming paradigms, and cloud-to-edge computing infrastructures. Submissions that present practical implementations of fundamental issues through large-scale computations simulating real-world phenomena are encouraged. Furthermore, we welcome papers that examine fault tolerance, reliability, and resource orchestration within distributed environments. Moreover, we seek contributions that investigate the convergence of distributed systems with fields such as artificial intelligence and data analysis.

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

• Algorithms and methods for efficient solving of high-scalable applications;
• Computing in heterogeneous and accelerated-processing environments;
• Large-scale simulations with a particular emphasis on CFD and ABMS (agent-based modeling 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;
• Performance optimizations and energy efficiency;
• Data analytics and AI solutions advancing in high-fidelity problems;
• Efficient data storage and transfer for HPC and HPDA systems;
• Workflows for complex scenarios processing;
• Powerful and immersive tools for visualizing massive datasets of simulation results;
• Fault tolerance, replication, and recovery in distributed systems;
• Machine learning and AI in distributed environments;
• Containerization and orchestration.

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

Manuscript Submission Information

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• parallel computing
• distributed systems
• multi-core architectures
• GPU acceleration
• high-performance computing (HPC)
• scalability
• load balancing
• latency and throughput
• post-processing data analysis
• fault tolerance