Recent Advances in High-Performance Parallel and Distributed Computing

Dear Colleagues,

This Special Issue focuses on emerging paradigms and technologies that advance the performance, scalability, and intelligence of parallel and distributed computing systems. It emphasizes the convergence of high-performance computing (HPC) with cloud, edge, and AI-driven infrastructures, showcasing how recent innovations enable real-time analytics, intelligent orchestration, and energy-efficient computing at scale.

The topics of interest include, but are not limited to, the following:

• Advanced architectures for the cloud–edge continuum and federated computing;
• Parallel and distributed algorithms for large-scale AI and deep learning models;
• Resource management and scheduling in heterogeneous computing environments;
• Performance modeling, optimization, and benchmarking of distributed systems;
• Data locality, in-network computing, and high-throughput data pipelines;
• Security, reliability, and fault tolerance in large-scale distributed systems;
• Green and sustainable computing strategies for data-intensive applications;
• Integration of HPC, quantum, and AI accelerators into distributed platforms;
• Emerging applications: Digital twins, autonomous systems, IoT, and 6G networking.

The purpose of this collection is to provide a comprehensive and forward-looking platform for researchers and practitioners to present breakthroughs that bridge theory, systems, and applications in next-generation distributed computing. It aims to highlight recent technological transitions—from traditional cluster computing to intelligent, adaptive, and decentralized ecosystems—and to foster cross-disciplinary discussion that drives future developments in computing infrastructure, software frameworks, and algorithmic design.

While extensive research has been published on parallel computing, cloud infrastructures, and distributed AI systems, most of the existing literature treats these domains separately. This Special Issue differentiates itself by addressing their convergence—specifically, how advances in edge intelligence, hybrid cloud architectures, and deep learning acceleration reshape traditional HPC paradigms. It will complement prior works in journals such as IEEE TPDS, IEEE TCC, IEEE Access, and FGCS by integrating new directions on data-driven optimization, AI-assisted orchestration, and energy-aware design, providing a holistic view of recent advances and emerging challenges across the computing continuum.

Prof. Dr. Ching-Hsien Hsu
Guest Editor

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