Guest editors note: Special issue on clusters, clouds, and data for scientific computing

The research areas of cluster, cloud, and data analytics computing, which today provide fundamental infrastructure for all areas of advanced computational science, are being radically transformed by the convergence of at least two unprecedented trends. The fi rst is the ongoing emergence of multicore and hybrid microprocessor designs, ushering in a new era of computing in which system designers must accept energy usage as a fi rst-order constraint, and application designers must be able to exploit parallelism and data locality to an unprecedented degree. As the research community is rapidly becoming aware, the components of the traditional HPC software stack are poorly matched to the characteristics of systems based on these new architectures — hundreds of thousands of nodes, millions of cores, GPU accelerators, reduced bandwidth, and memory per core. The second trend is the dramatic escalation in the amount of data that leading edge scienti fi c applications, and the communities that use them, are either generating or trying to analyze. A key problem in such data intensive science lies not only in the shear volume of bits that must be processed and managed but also in the logistical problems associated with making the data of most current interest available to participants in large national and international collaborations, sitting in different administrative domains, spread across the wide area network, and wanting to use diverse resources — clusters, clouds, and data. This special issue gathers selected papers of the Work-shop on Clusters, Clouds and Data for Scienti fi c Computing (CCDSC) that was held at La Maison des Contes , 69490 Dareize-France, on September 6 – 9, 2022. This workshop is a continuation of a series of workshops started in 1992 entitled Workshop on Environments and Tools for Parallel Scienti