Parallel, Distributed and Grid/Cloud/Edge/Fog/P2P Computing

Dear Colleagues,

This Special Issue focuses on the design and implementation of parallel and distributed algorithms. Parallelizing an algorithm in order to achieve good performance and scalability is not trivial, requiring a good data organization and decomposition strategy to balance the workload while minimizing data dependencies. Additional issues are related to minimizing synchronization and communication overhead, as well as I/O costs. Creating breakthrough parallel algorithms requires addressing several key computing problems by providing novel and clever solutions.

When the data are spread among different geographically distributed sites, centralized processing is very inefficient and expensive; however, it is often impractical and subject to security risks. Therefore, we face the challenge of processing the data and minimizing the amount of data being exchanged, whilst simultaneously guaranteeing correctness and efficiency. Distributed algorithms, including Grid/Cloud/Edge/Fog/P2P computing, perform distributed data processing, paying careful attention to resource constraints: in particular, bandwidth limitation, privacy concerns, and computing power.

The aim of this Special Issue is to address current research and trends in the field by focusing on theory, technologies, and applications, highlighting both challenges and opportunities. The scope of the Special Issue is very broad, taking into account the interdisciplinarity of parallel and distributed computing. Among the topics of interest are the following:

• Scalable parallel algorithms using message-passing, shared-memory, or hybrid programming paradigms.
• Exploiting modern parallel architectures including FPGA, GPU, and many-core accelerators for parallel applications.
• Middleware for high-performance applications on grid and cloud environments.
• Benchmarking and performance studies of high-performance applications.
• Novel programming paradigms to support high-performance computing.
• Performance models for high-performance applications and middleware.
• Programming models, tools, and environments for high-performance computing.
• Caching, streaming, pipelining, and other optimization techniques for data management in high-performance computing.
• Novel distributed algorithms minimizing data communication.
• Distributed algorithms for Grid/Cloud/Edge/Fog/P2P environments.

Prof. Dr. Massimo Cafaro
Prof. Dr. Italo Epicoco
Dr. Marco Pulimeno
Guest Editors

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• parallel computing
• distributed computing
• grid computing
• cloud computing
• edge computing
• fog computing
• P2P networks