The advent of experimental science facilities-instruments and observatories, such as the Large Hadron Collider, the Laser Interferometer Gravitational Wave Observatory, and the upcoming Large Synoptic Survey Telescope-has brought about challenging, large-scale computational and data processing requirements. Traditionally, the computing infrastructure to support these facility's requirements were organized into separate infrastructure that supported their high-throughput needs and those that supported their high-performance computing needs. We argue that to enable and accelerate scientific discovery at the scale and sophistication that is now needed, this separation between high-performance computing and high-throughput computing must be bridged and an integrated, unified infrastructure provided. In this paper, we discuss several case studies where such infrastructure has been implemented. These case studies span different science domains, software systems, and application requirements as well as levels of sustainability. A further aim of this paper is to provide a basis to determine the common characteristics and requirements of such infrastructure, as well as to begin a discussion of how best to support the computing requirements of existing and future experimental science facilities.
mdx: A Cloud Platform for Supporting Data Science and Cross-Disciplinary Research Collaborations
The University of Tokyo（东京大学）；Hokkaido University（北海道大学）；Tohoku University（东北大学）；University of Tsukuba（筑波大学）；National Institute of Informatics（国立信息学研究所）；National Institute of Advanced Industrial Science and Technology（产业技术综合研究所）；Tokyo Institute of Technology（东京工业大学）
Supporting the development of Machine Learning for fundamental science in a federated Cloud with the AI_INFN platform
Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Firenze, Italy（意大利佛罗伦萨分所国家核物理研究所）；Istituto Nazionale di Fisica Nucleare (INFN), CNAF, Italy（意大利CNAF国家核物理研究所）；Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Perugia, Italy（意大利佩鲁吉亚分所国家核物理研究所）；Department of Physics, University of Perugia, Italy（意大利佩鲁吉亚大学物理系）
GRAPLEr: A Distributed Collaborative Environment for Lake Ecosystem Modeling that Integrates Overlay Networks, High-throughput Computing, and Web Services
