%0 Book Section %B Fog Computing: Theory and Practice %D 2020 %T Harnessing the Computing Continuum for Programming Our World %A Pete Beckman %A Jack Dongarra %A Nicola Ferrier %A Geoffrey Fox %A Terry Moore %A Dan Reed %A Micah Beck %X This chapter outlines a vision for how best to harness the computing continuum of interconnected sensors, actuators, instruments, and computing systems, from small numbers of very large devices to large numbers of very small devices. The hypothesis is that only via a continuum perspective one can intentionally specify desired continuum actions and effectively manage outcomes and systemic properties—adaptability and homeostasis, temporal constraints and deadlines—and elevate the discourse from device programming to intellectual goals and outcomes. Development of a framework for harnessing the computing continuum would catalyze new consumer services, business processes, social services, and scientific discovery. Realizing and implementing a continuum programming model requires balancing conflicting constraints and translating the high‐level specification into a form suitable for execution on a unifying abstract machine model. In turn, the abstract machine must implement the mapping of specification demands to end‐to‐end resources. %B Fog Computing: Theory and Practice %I John Wiley & Sons, Inc. %@ 9781119551713 %G eng %& 7 %R https://doi.org/10.1002/9781119551713.ch7 %0 Generic %D 2019 %T A Collection of Presentations from the BDEC2 Workshop in Kobe, Japan %A Rosa M. Badia %A Micah Beck %A François Bodin %A Taisuke Boku %A Franck Cappello %A Alok Choudhary %A Carlos Costa %A Ewa Deelman %A Nicola Ferrier %A Katsuki Fujisawa %A Kohei Fujita %A Maria Girone %A Geoffrey Fox %A Shantenu Jha %A Yoshinari Kameda %A Christian Kniep %A William Kramer %A James Lin %A Kengo Nakajima %A Yiwei Qiu %A Kishore Ramachandran %A Glenn Ricart %A Kim Serradell %A Dan Stanzione %A Lin Gan %A Martin Swany %A Christine Sweeney %A Alex Szalay %A Christine Kirkpatrick %A Kenton McHenry %A Alainna White %A Steve Tuecke %A Ian Foster %A Joe Mambretti %A William. M Tang %A Michela Taufer %A Miguel Vázquez %B Innovative Computing Laboratory Technical Report %I University of Tennessee, Knoxville %8 2019-02 %G eng %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 %0 Generic %D 2018 %T A Collection of White Papers from the BDEC2 Workshop in Bloomington, IN %A James Ahrens %A Christopher M. Biwer %A Alexandru Costan %A Gabriel Antoniu %A Maria S. Pérez %A Nenad Stojanovic %A Rosa Badia %A Oliver Beckstein %A Geoffrey Fox %A Shantenu Jha %A Micah Beck %A Terry Moore %A Sunita Chandrasekaran %A Carlos Costa %A Thierry Deutsch %A Luigi Genovese %A Tarek El-Ghazawi %A Ian Foster %A Dennis Gannon %A Toshihiro Hanawa %A Tevfik Kosar %A William Kramer %A Madhav V. Marathe %A Christopher L. Barrett %A Takemasa Miyoshi %A Alex Pothen %A Ariful Azad %A Judy Qiu %A Bo Peng %A Ravi Teja %A Sahil Tyagi %A Chathura Widanage %A Jon Koskey %A Maryam Rahnemoonfar %A Umakishore Ramachandran %A Miles Deegan %A William Tang %A Osamu Tatebe %A Michela Taufer %A Michel Cuende %A Ewa Deelman %A Trilce Estrada %A Rafael Ferreira Da Silva %A Harrel Weinstein %A Rodrigo Vargas %A Miwako Tsuji %A Kevin G. Yager %A Wanling Gao %A Jianfeng Zhan %A Lei Wang %A Chunjie Luo %A Daoyi Zheng %A Xu Wen %A Rui Ren %A Chen Zheng %A Xiwen He %A Hainan Ye %A Haoning Tang %A Zheng Cao %A Shujie Zhang %A Jiahui Dai %B Innovative Computing Laboratory Technical Report %I University of Tennessee, Knoxville %8 2018-11 %G eng %0 Journal Article %J CTWatch Quarterly %D 2007 %T The Impact of Multicore on Computational Science Software %A Jack Dongarra %A Dennis Gannon %A Geoffrey Fox %A Ken Kennedy %B CTWatch Quarterly %V 3 %8 2007-02 %G eng %N 1 %0 Journal Article %J Making the Global Infrastructure a Reality %D 2003 %T NetSolve: Past, Present, and Future - A Look at a Grid Enabled Server %A Sudesh Agrawal %A Jack Dongarra %A Keith Seymour %A Sathish Vadhiyar %E Francine Berman %E Geoffrey Fox %E Anthony Hey %K netsolve %B Making the Global Infrastructure a Reality %I Wiley Publishing %8 2003-00 %G eng