%0 Book %B Lecture Notes in Computer Science %D 2020 %T Parallel Processing and Applied Mathematics: 13th International Conference, PPAM 2019, Bialystok, Poland, September 8–11, 2019, Revised Selected Papers, Part II %A Roman Wyrzykowski %A Ewa Deelman %A Jack Dongarra %A Konrad Karczewski %B Lecture Notes in Computer Science %I Springer International Publishing %P 503 %8 2020-03 %@ 978-3-030-43222-5 %G eng %R https://doi.org/10.1007/978-3-030-43222-5 %0 Book %B Lecture Notes in Computer Science %D 2020 %T Parallel Processing and Applied Mathematics: 13th International Conference, PPAM 2019, Bialystok, Poland, September 8–11, 2019, Revised Selected Papers, Part I %A Roman Wyrzykowski %A Ewa Deelman %A Jack Dongarra %A Konrad Karczewski %B Lecture Notes in Computer Science %7 1 %I Springer International Publishing %P 581 %8 2020-03 %@ 978-3-030-43229-4 %G eng %R https://doi.org/10.1007/978-3-030-43229-4 %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 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 Book Section %B Lecture Notes in Computer Science %D 2016 %T Dense Symmetric Indefinite Factorization on GPU Accelerated Architectures %A Marc Baboulin %A Jack Dongarra %A Adrien Remy %A Stanimire Tomov %A Ichitaro Yamazaki %E Roman Wyrzykowski %E Ewa Deelman %E Konrad Karczewski %E Jacek Kitowski %E Kazimierz Wiatr %K Communication-avoiding %K Dense symmetric indefinite factorization %K gpu computation %K randomization %X We study the performance of dense symmetric indefinite factorizations (Bunch-Kaufman and Aasen’s algorithms) on multicore CPUs with a Graphics Processing Unit (GPU). Though such algorithms are needed in many scientific and engineering simulations, obtaining high performance of the factorization on the GPU is difficult because the pivoting that is required to ensure the numerical stability of the factorization leads to frequent synchronizations and irregular data accesses. As a result, until recently, there has not been any implementation of these algorithms on hybrid CPU/GPU architectures. To improve their performance on the hybrid architecture, we explore different techniques to reduce the expensive communication and synchronization between the CPU and GPU, or on the GPU. We also study the performance of an LDL^T factorization with no pivoting combined with the preprocessing technique based on Random Butterfly Transformations. Though such transformations only have probabilistic results on the numerical stability, they avoid the pivoting and obtain a great performance on the GPU. %B Lecture Notes in Computer Science %S 11th International Conference, PPAM 2015, Krakow, Poland, September 6-9, 2015. Revised Selected Papers, Part I %I Springer International Publishing %V 9573 %P 86-95 %8 2015-09 %@ 978-3-319-32149-3 %G eng %& Parallel Processing and Applied Mathematics %R 10.1007/978-3-319-32149-3_9