并發(fā)與計算實踐與經(jīng)驗（CCPE）出版了高質(zhì)量的原始研究論文和權(quán)威研究評論論文，這些論文的重疊領(lǐng)域包括：并行和分布式計算；高性能計算；計算和數(shù)據(jù)科學(xué)；人工智能和機器學(xué)習(xí)；大數(shù)據(jù)應(yīng)用、算法和系統(tǒng)；網(wǎng)絡(luò)科學(xué)；本體論和語義學(xué)；安全和隱私；云/邊緣/霧計算；綠色計算；以及量子計算。強調(diào)與這些領(lǐng)域的實踐和經(jīng)驗相關(guān)的新研究應(yīng)該是貢獻(xiàn)的一個重要方面，而不是解決理論方面的問題。提交應(yīng)該涉及或暗示重大的并發(fā)性和/或計算問題。在這些廣泛的領(lǐng)域中，CCPE的范圍包括并行和分布式系統(tǒng)計算和數(shù)據(jù)密集型應(yīng)用程序的設(shè)計、實現(xiàn)和優(yōu)化。這包括新的并發(fā)算法和應(yīng)用程序的開發(fā)，它們的并行性能分析和建模，以及新的編程或建模語言和相關(guān)的組合方法。與計算和數(shù)據(jù)密集型應(yīng)用相關(guān)的領(lǐng)域包括但不限于大規(guī)模計算科學(xué)、人工智能以及處理衛(wèi)星、科學(xué)實驗、傳感器網(wǎng)絡(luò)、醫(yī)療儀器和其他來源的海量數(shù)據(jù)集。并行和分布式系統(tǒng)環(huán)境下的資源管理技術(shù)，以及能源感知計算也是人們感興趣的話題。

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.