CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE

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官方网站：http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1532-0634

投稿网址：http://mc.manuscriptcentral.com/cpe

PMC链接：http://www.ncbi.nlm.nih.gov/nlmcatalog?term=1532-0626%5BISSN%5D

并发与计算实践与经验（CCPE）出版了高质量的原始研究论文和权威研究评论论文，这些论文的重叠领域包括：并行和分布式计算；高性能计算；计算和数据科学；人工智能和机器学习；大数据应用、算法和系统；网络科学；本体论和语义学；安全和隐私；云/边缘/雾计算；绿色计算；以及量子计算。强调与这些领域的实践和经验相关的新研究应该是贡献的一个重要方面，而不是解决理论方面的问题。提交应该涉及或暗示重大的并发性和/或计算问题。在这些广泛的领域中，CCPE的范围包括并行和分布式系统计算和数据密集型应用程序的设计、实现和优化。这包括新的并发算法和应用程序的开发，它们的并行性能分析和建模，以及新的编程或建模语言和相关的组合方法。与计算和数据密集型应用相关的领域包括但不限于大规模计算科学、人工智能以及处理卫星、科学实验、传感器网络、医疗仪器和其他来源的海量数据集。并行和分布式系统环境下的资源管理技术，以及能源感知计算也是人们感兴趣的话题。

Concurrency and Computation-Practice & 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; andQuantum 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.