Ian Foster, M. Ainsworth, J. Bessac, F. Cappello, J. Choi, S. Di, Z. Di, A. M. Gok, Hanqi Guo, K. Huck, Christopher Kelly, S. Klasky, K. Kleese van Dam, Xin Liang, Kshitij Mehta, M. Parashar, T. Peterka, Line C. Pouchard, Tong Shu, O. Tugluk, H. V. van Dam, Lipeng Wan, Matthew Wolf, J. Wozniak, Wei Xu, I. Yakushin, Shinjae Yoo, T. Munson
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引用次数: 14
Abstract
A growing disparity between supercomputer computation speeds and I/O rates means that it is rapidly becoming infeasible to analyze supercomputer application output only after that output has been written to a file system. Instead, data-generating applications must run concurrently with data reduction and/or analysis operations, with which they exchange information via high-speed methods such as interprocess communications. The resulting parallel computing motif, online data analysis and reduction (ODAR), has important implications for both application and HPC systems design. Here we introduce the ODAR motif and its co-design concerns, describe a co-design process for identifying and addressing those concerns, present tools that assist in the co-design process, and present case studies to illustrate the use of the process and tools in practical settings.
期刊介绍:
With ever increasing pressure for health services in all countries to meet rising demands, improve their quality and efficiency, and to be more accountable; the need for rigorous research and policy analysis has never been greater. The Journal of Health Services Research & Policy presents the latest scientific research, insightful overviews and reflections on underlying issues, and innovative, thought provoking contributions from leading academics and policy-makers. It provides ideas and hope for solving dilemmas that confront all countries.