%0 Generic %D 2019 %T A Collection of White Papers from the BDEC2 Workshop in Poznan, Poland %A Gabriel Antoniu %A Alexandru Costan %A Ovidiu Marcu %A Maria S. Pérez %A Nenad Stojanovic %A Rosa M. Badia %A Miguel Vázquez %A Sergi Girona %A Micah Beck %A Terry Moore %A Piotr Luszczek %A Ezra Kissel %A Martin Swany %A Geoffrey Fox %A Vibhatha Abeykoon %A Selahattin Akkas %A Kannan Govindarajan %A Gurhan Gunduz %A Supun Kamburugamuve %A Niranda Perera %A Ahmet Uyar %A Pulasthi Wickramasinghe %A Chathura Widanage %A Maria Girone %A Toshihiro Hanawa %A Richard Moreno %A Ariel Oleksiak %A Martin Swany %A Ryousei Takano %A M.P. van Haarlem %A J. van Leeuwen %A J.B.R. Oonk %A T. Shimwell %A L.V.E. Koopmans %B Innovative Computing Laboratory Technical Report %I University of Tennessee, Knoxville %8 2019-05 %G eng %0 Journal Article %J The International Journal of High Performance Computing Applications %D 2018 %T Big Data and Extreme-Scale Computing: Pathways to Convergence - Toward a Shaping Strategy for a Future Software and Data Ecosystem for Scientific Inquiry %A Mark Asch %A Terry Moore %A Rosa M. Badia %A Micah Beck %A Pete Beckman %A Thierry Bidot %A François Bodin %A Franck Cappello %A Alok Choudhary %A Bronis R. de Supinski %A Ewa Deelman %A Jack Dongarra %A Anshu Dubey %A Geoffrey Fox %A Haohuan Fu %A Sergi Girona %A Michael Heroux %A Yutaka Ishikawa %A Kate Keahey %A David Keyes %A William T. Kramer %A Jean-François Lavignon %A Yutong Lu %A Satoshi Matsuoka %A Bernd Mohr %A Stéphane Requena %A Joel Saltz %A Thomas Schulthess %A Rick Stevens %A Martin Swany %A Alexander Szalay %A William Tang %A Gaël Varoquaux %A Jean-Pierre Vilotte %A Robert W. Wisniewski %A Zhiwei Xu %A Igor Zacharov %X 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. %B The International Journal of High Performance Computing Applications %V 32 %P 435–479 %8 2018-07 %G eng %N 4 %R https://doi.org/10.1177/1094342018778123