2020/08/21 by Ian Foster, Foster, Ian, Carl Kesselman +1 · 1 voice
Biochemistry, Genetics and Molecular Biology · Computer Science · Decision Sciences · Engineering · #Biomedical and Engineering Education #Distributed #FOS: Computer and information sciences #Genetics, Bioinformatics, and Biomedical Research #Parallel #Scientific Computing and Data Management #and Cluster Computing (cs.DC) #cs.DC
paper · pdf · doi:10.48550/arxiv.2008.09591
arxiv created 2020/08/21 · openalex publication_date 2020/08/21 · arxiv published 2020/08/21 · arxiv updated 2020/08/24 · openalex created_date 2022/07/22 · openalex updated_date 2026/07/28
A growing gap between progress in biological knowledge and improved health outcomes inspired the new discipline of translational medicine, in which the application of new knowledge is an explicit part of a research plan. Abramson and Parashar argue that a similar gap between complex computational technologies and ever-more-challenging applications demands an analogous discipline of translational computer science, in which the deliberate movement of research results into large-scale practice becomes a central research focus rather than an afterthought. We revisit from this perspective the development and application of grid computing from the mid-1990s onwards, and find that a translational framing is useful for understanding the technology's development and impact. We discuss how the development of grid computing infrastructure, and the Globus Toolkit, in particular, benefited from a translational approach. We identify lessons learned that can be applied to other translational computer science initiatives.