Concurrency and Computation: Practice and Experience
Concurrency and Computation: Practice and Experience (CCPE) publishes high-quality, original research papers, and authoritative research review papers, in the overlapping fields of: Parallel and distributed computing; High-performance computing; Computational and data science; Artificial intelligence and machine learning; Big data applications, algorithms, and systems; Network science; Ontologies and semantics; Security and privacy; Cloud/edge/fog computing; Green computing; and Quantum computing. Emphasis on novel research related to practice and experience in these areas should be an essential aspect of contributions, rather than addressing theoretical aspects. Submissions should involve or imply significant concurrency and/or computational issues. Within these broad areas, the scope of CCPE includes the design, implementation, and optimization of compute and data-intensive applications for parallel and distributed systems. This includes the development of novel concurrent algorithms and applications, their parallel performance analysis and modelling, and new programming or modelling languages and relevant methodologies for composing them. Areas relevant to compute and data-intensive applications include, but are not limited to, large-scale computational science, artificial intelligence, and the processing of voluminous datasets from satellites, scientific experiments, sensor networks, medical instruments, and other sources. Techniques for resource management in the context of parallel and distributed systems, and energy-aware computing are also topics of interest.