Over the past four years, the Big Data and Exascale Computing (BDEC) project organized a series of five international workshops that aimed to explore the ways in which the new forms of data-centric discovery introduced by the ongoing revolution in high-end data analysis (HDA) might be integrated with the established, simulation-centric paradigm of the high-performance computing (HPC) community. Based on those meetings, we argue that the rapid proliferation of digital data generators, the unprecedented growth in the volume and diversity of the data they generate, and the intense evolution of the methods for analyzing and using that data are radically reshaping the landscape of scientific computing. The most critical problems involve the logistics of wide-area, multistage workflows that will move back and forth across the computing continuum, between the multitude of distributed sensors, instruments and other devices at the networks edge, and the centralized resources of commercial clouds and HPC centers. We suggest that the prospects for the future integration of technological infrastructures and research ecosystems need to be considered at three different levels. First, we discuss the convergence of research applications and workflows that establish a research paradigm that combines both HPC and HDA, where ongoing progress is already motivating efforts at the other two levels. Second, we offer an account of some of the problems involved with creating a converged infrastructure for peripheral environments, that is, a shared infrastructure that can be deployed throughout the network in a scalable manner to meet the highly diverse requirements for processing, communication, and buffering/storage of massive data workflows of many different scientific domains. Third, we focus on some opportunities for software ecosystem convergence in big, logically centralized facilities that execute large-scale simulations and models and/or perform large-scale data analytics. We close by offering some conclusions and recommendations for future investment and policy review.
|Original language||English (US)|
|Number of pages||45|
|Journal||International Journal of High Performance Computing Applications|
|State||Published - Jul 1 2018|
Bibliographical noteFunding Information:
The authors would like to acknowledge David Rogers for his work on the illustrations, Sam Crawford for editing support and the creation of the Appendix, and Piotr Luszc-zek for technical support. They would also gratefully acknowledge all the following sponsors who supported the big data and exascale computing workshop series: Government Sponsors: US Department of Energy, the National Science Foundation, Argonne National Laboratory, European Exascale Software Initiative, and European Commission; Academic Sponsors: University of Tennessee, the National Institute of Advanced Industrial Science and Technology (AIST), Barcelona Supercomputer Center, Kyoto University, Kyushu University, Riken, The University of Tokyo, the Tokyo Institute of Technology, and the University of Tsukuba Center for Computational Sciences; Industry Sponsors: Intel, Cray, Data Direct Networks, Fujitsu, Hitachi, Lenovo, and NEC. The authors would also like to thank Jean-Claude André and Gabriel Antoniu for their valuable contributions to this work.
© The Author(s) 2018.
- Big data
- extreme-scale computing
- future software
- high-end data analysis
- traditional HPC
ASJC Scopus subject areas
- Theoretical Computer Science
- Hardware and Architecture