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Purpose: This study aims to provide an overview of what characterizes the current state of research in the field of cloud computing use in human resource management (HRM) with the identification, analysis and classification of the existing literature and lines of research addressed and to provide guidance for future research. Design/methodology/approach: The systematic literature review (SLR) technique has been used to identify, select, analyze and evaluate the existing publications on cloud computing and HRM. A total of 35 papers published up to December 2020 have been obtained from the Web of Science (WoS) scientific database. The research design has allowed us to determine what characterizes the current state of research on the use of cloud computing in HRM and obtain a novel classification of the literature that identifies four lines of research and the contributions in each line and has allowed us to define the future research agenda. Findings: The four groups into which the papers on the cloud computing-HRM relationship have been classified are: (1) studies focused on the development of cloud platforms for HRM that highlight technical aspects, (2) papers that focus on the concept of human resource elasticity, (3) papers on the adoption and/or implantation of cloud platforms for HRM and (4) studies that highlight the effects or implications of cloud platforms for HRM. This paper proposes some new opportunities for future research and presents some helpful implications from the theoretical and management perspectives. Research limitations/implications: This study uses only scientific articles in the WoS database with a Journal Citation Report (JCR) or SCImago Journal Rank (SJR) impact. Originality/value: This paper provides an overview of the knowledge on cloud computing and HRM research and offers recommendations for future research. [ABSTRACT FROM AUTHOR]

The article focuses on the limitations of massively parallel computing in the analysis of data from next-generation scientific instruments. It states that powerful large-scale scientific instruments, such as the Large Synoptic Survey Telescope which is scheduled to go live in 2020, produce more data than the most powerful massively parallel supercomputers can handle. It mentions that scientists are examining new methods of reducing datasets to a manageable size, including cloud-based computing and emerging frameworks like quantum computing, and talks about algorithmic and economic constraints facing large scale data analysis. Massachusetts Institute of Technology professor Scott Aaronson suggests that Moore's Law has effectively broken down.