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DTSTART;TZID=America/Detroit:20161202T140000
DTEND;TZID=America/Detroit:20161202T150000
DTSTAMP:20260614T164211
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SUMMARY:MICDE/RadLab/IEEE Seminar: Levent Gürel\, ABAKUS Computing Technologies
DESCRIPTION:Bio: Prof. Levent Gürel (Fellow of IEEE\, ACES\, and EMA) received the M.S. and Ph.D. degrees from the University of Illinois at Urbana-Champaign (UIUC) in 1988 and 1991\, respectively\, in electrical and computer engineering. He worked at the IBM Thomas J. Watson Research Center\, Yorktown Heights\, New York\, in 1991-94. During his 20 years with Bilkent University\, he served as the Founding Director of the Computational Electromagnetics Research Center (BiLCEM) and a professor of electrical engineering. He is also an Adjunct Professor at UIUC. Prof. Gürel is the Founder and CEO of ABAKUS Computing Technologies\, a company that is geared towards advancing the use of cutting-edge computing technologies for solving difficult scientific problems with important real-life applications and societal benefits. He is conferred the UIUC ECE Distinguished Alumni Award in 2013 and the IEEE Harrington-Mittra Award in Computational Electromagnetics in 2015. He is an IEEE Distinguished Lecturer. He was invited to address the 2011 and 2017 ACES Conferences as a Plenary Speaker and a TEDx Conference in 2014. Among other recognitions of Prof. Gürel’s accomplishments\, the two prestigious awards from the Turkish Academy of Sciences (TUBA) in 2002 and the Scientific and Technological Research Council of Turkey (TUBITAK) in 2003 are the most notable. Since 2003\, Prof. Gürel has been serving as an associate editor for Radio Science\, IEEE Transactions on Antennas and Propagation\, IEEE Antennas and Wireless Propagation Letters\, IET Microwaves\, Antennas & Propagation\, JEMWA\, PIER\, ACES Journal\, and ACES Express. \nSolution of Extremely Large Forward and Inverse Problems in Computational Electromagnetics: BIG DATA Aspects\nAs we solve some of the largest problems in the interdisciplinary domain of computational electromagnetics\, we have to deal with various aspects of big-data issues routinely. Most recently\, we have achieved the solutions of larger than 1\,500\,000\,000×1\,500\,000\,000 (1.5 billion!) dense matrix equations! This achievement is an outcome of a multidisciplinary effort involving physical understanding of electromagnetics problems\, novel parallelization strategies (computer science)\, constructing parallel clusters (computer architecture)\, advanced mathematical methods for integral equations\, fast solvers\, iterative methods\, preconditioners\, linear algebra\, and big data. Solving such large problems on a regular basis requires the generation\, representation\, storage\, processing\, analysis\, transfer and communication\, visualization and interpretation of extremely large data sets in the order of multiple terabytes. \nAccurate formulations of real-life electromagnetics problems with integral equations necessitate the solution of extremely large dense matrix equations. Solutions of such tremendously challenging problems cannot be achieved easily\, even when using the most powerful computers with state-of-the-art petascale computing capabilities. Instead\, we have been solving some of the world’s largest integral-equation problems in computational electromagnetics by employing fast algorithms implemented on parallel computers. To achieve optimal management of multiple large data sets\, we design and implement the handling of data in various levels of cache\, memory\, and disk\, leading to meticulously designed out-of-core (OoC) schemes. That way\, we enable the solution of unprecedentedly large problems with limited amounts of DRAM. In order to avoid decelerating the solution\, we optimize communications among CPU cores\, among processors\, among nodes\, from CPU to disk (and back)\, and in the case of heterogeneous architectures\, we carefully control the data traffic to/from GPUs. Furthermore\, we employ MPI and OpenMP simultaneously in a parallelization strategy designed to reduce data duplications among processes so that vast numbers of cores can be efficiently utilized without requiring extra memory. \nI will present fast and accurate solutions of large-scale electromagnetic forward and inverse problems involving three-dimensional geometries that are larger than 1000 wavelengths using the multilevel fast multipole algorithm (MLFMA) and parallel MLFMA. Solving the world’s largest computational electromagnetics problems has important implications in terms of obtaining the solutions of future grand-challenge problems in imaging\, (subsurface)\, optics\, nanotechnology\, bio-electromagnetics\, metamaterials\, remote sensing\, as well as plethora of other disciplines of science\, e.g.\, acoustics\, elastics\, quantum mechanics\, astrophysics\, molecular dynamics\, electro-statics\, fluid dynamics\, thermodynamics. For more information: http://captains.of.computing.technology/.
URL:https://micde.umich.edu/event/micderadlabieee-seminar-levent-gurel-abakus-computing-technologies/
LOCATION:3427 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Education,Featured Events,MICDE Seminar Series,Seminar
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DTSTART;TZID=UTC:20161209T110000
DTEND;TZID=UTC:20161209T120000
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SUMMARY:MICDE Seminar: Ann Almgren\, Lawrence Berkeley National Lab
DESCRIPTION:Bio:  Ann Almgren is a senior scientist in the Computational Research Division of Lawrence Berkeley National Laboratory and the Group Lead of the Center for Computational Sciences and Engineering. Her primary research interest is in computational algorithms for solving PDE’s for fluid dynamics in a variety of application areas. Her current projects include the development and implementation of new multiphysics algorithms in high-resolution adaptive mesh codes that are designed for the latest multicore architectures.  She is a SIAM Fellow and serves on the editorial boards of CAMCoS and SIREV. \nNext-Generation AMR\nBlock-structured adaptive mesh refinement (AMR) is a powerful tool for improving the computational efficiency and reducing the memory footprint of structured-grid numerical simulations. AMR techniques have been used for over 25 years to solve increasingly complex problems.  I will give an overview of recent and planned advances in AMR algorithms and implementations at BerkeleyLab to address the challenges of next-generation multicore architectures and the complexity of multiscale\, multiphysics problems.  This will include new ways of thinking about multilevel algorithms and new approaches to data layout and load balancing\, in situ and in transit visualization and analytics\, and run-time performance modeling and control. \n  \n  \n  \n 
URL:https://micde.umich.edu/event/micde-seminar-ann-almgren-lawrence-berkeley-national-lab/
LOCATION:1013 H. H. Dow\, 2300 Hayward St.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:MICDE Seminar Series
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