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Computational Research Division 2009-2012 COVER IMAGES LAWRENCE BERKELEY NATIONAL LABORATORY Computational Research Division 2009-2012 COVER IMAGES Combustion Simulation This 46082 image of a combustion simulation result was rendered by a hybrid-parallel (MPI+pthreads) raycasting volume rendering implementation running on 216,000 cores of the JaguarPF supercomputer. Combustion simulation data courtesy of J. Bell and M. Day (Center for Computional Sciences and Engineering, LBNL). For more information see “MPI- hybrid Parallelism for Volume Rendering on Large, Multi-core Systems” by M. Howison, E.W. Bethel, and H. Childs. Fluid Pinch-off The CRD Math Group has built a new mathematical model applicable to the computations of fluid pinch-off. Their method correctly computes asymptotic constants of fluid self- similarity as breakup is approached, and the resulting rapid reconfiguration due to surface capillary waves and separation into microdroplets. Global Cloud Resolving Model for Climate Accurate modeling of Earth’s climate is one of the biggest challenges facing HPC today. One of the largest sources of error in existing climate models is the simulation of clouds. By increasing the grid resolution from 200 km to 2 km, clouds can be accurately resolved in simulations. Isocontours are plotted corresponding to different vorticity levels in the atmosphere. [Data provided by Dave Randall, Celal Konor and Ross Heikes (Colorado State Univ). Joint work with Karen Schuchardt, Bruce Palmer, Annette Koontz and Jeff Daily (PNNL). Visualization by the CRD Visualization Group’s Prabhat.] Laser Wakefield Particle Acceleration Computational model of laser wakefield particle acceleration. Models of high-quality electron beams help scientists in the LOASIS program better understand beam acceleration dynamics. For more information see “Automatic Beam Path Analysis of Laser Wakefield Particle Acceleration Data” by Rübel et al. [Image by Oliver Rübel, CRD Visualization Group.] Zeolite Structures Visualization of zeolite material showing its bonding structure, and an isosurface visualization of the energy landscape for a diffusing carbon dioxide molecule (blue region indicates strong adsorption). By using Zeo++, they can identify carbon dioxide adsorbing materials much faster and by using far less computing power than through molecular simulation. This work is presented in more detail in a joint 2012 ChemPhysChem journal paper by Richard L. Martin et al. [Image generated by Richard L. Martin of the Scientific Computing Group using VisIt.] LAWRENCE BERKELEY NATIONAL LABORATORY Computational Research Division 2009-2012 CONTENTS 4 • Strategies, Competencies and Goals 15 • Division Directors 16 • Department Heads 18• Awards and Conference Honors 25 • Advanced Computing for Science Department 43 • Applied Numerical Algorithms Group 50 • Complex Systems Group 63 • Scientific Computing Group 71 • Biological Data Management and Technology Center 79 • Future Technologies Group 87 • Scientific Data Management Group 93 • Visualization Group 101 • Computational Cosmology Center 108 • Mathematics Group 119 • Center for Computational Sciences and Engineering 126 • Collaborative Projects Across CRD 144 • Computational Research Division Organization Chart 145 • Publications Computational Research Division 2009-2012 3 Strategies, Competencies and Goals LBNL COMPUTATIONAL RESEARCH DIVISION STRATEGIES, COMPETENCIES AND GOALS David L. Brown Division Director January 2013 Summary Lawrence Berkeley National Laboratory (LBNL) is a leader in mathematics and computer science with applications to research spanning many scientific disciplines, which are important to the U.S. Department of Energy (DOE) and the nation generally. LBNL’s core strengths in computing sciences lie with its world-leading team of applied mathematicians, computer, data and computational scientists in the Computational Research Division (CRD), the research arm of LBNL’s Computing Sciences Area. Spread across fourteen groups, CRD hosts approximately 150 researchers who develop mathematical models, algorithms, tools and software in support of computationally enabled scientific discovery. CRD supports the overall mission of the Computing Sciences Area, which is to accelerate scientific discovery across a broad scientific community using advanced computing and mathematics. This, in turn, supports the overall mission of LBNL, by addressing the most pressing scientific and technical challenges of our time, to transform the world energy economy and to provide a sustainable future for humankind. CRD’s research strengths can be described conveniently in terms of four core competencies, which are listed below: » Applied Mathematics, » Advanced Computer Science Technologies, » Scientific Data Management and Analytics, and » Software Technology Development. The first three are research competencies, while the fourth is a supporting competency that cuts across the others. While many venues outside Berkeley Lab focus on the first three competencies as independent research focus areas, it is a hallmark of LBNL’s computational research that a synergistic application of capabilities, from all of these competencies, is brought to bear on the science and engineering challenges being addressed. A major strength of LBNL computational research has been in the development of mathematics, algorithms and software supporting large-scale modeling and simulation. In the future, as large-scale experimental and observational science becomes increasingly dependent on mathematical and computational advances, computational solutions that enable scientific discovery will increasingly require a combination of modeling and simulation, advanced computer science technologies, scientific data management and analytics, integrated effectively into advanced scientific data ecosystems. 4 Lawrence Berkeley National Laboratory Strategies, Competencies and Goals Our Science Engagement Strategy The Computing Sciences Area’s main strategy for accelerating scientific discovery across a broad scientific computing community is science engagement; the direct engagement of scientists in other domains to collaboratively advance scientific discovery by using advanced mathematical and computational technology. In many cases, our research staff members themselves have considerable credentials as scientists in science domains outside mathematics and computer science. In CRD, this science engagement strategy has driven our substantial track record in the development of mathematical models, algorithms, simulation and analysis tools, including exceptional software tools that have had a broad impact on DOE and industrial scientific and engineering applications, as well as on our own fields of applied mathematics and computer science. As we look to the future, the rapidly growing data challenges at DOE’s scientific user facilities, many of which are located at LBNL, drive a need for the development of advanced computational technology to enhance scientific discovery at those facilities. Over the past few years, we have begun to engage scientists in LBNL’s other science divisions, many of which are responsible for DOE scientific user facilities, to understand and address their emerging computational challenges. We see significant research challenges that require the development of new analysis methods, new mathematical models and algorithms instantiated in highly effective simulation and analysis codes. The need for the development of scientific data ecosystems to address the management, transmission and analysis of data has become increasingly evident. As we have done for many science domains outside LBNL in the past, we will seek to provide LBNL science enterprises with advanced software technology solutions to enable and accelerate scientific discovery. Our vision is to significantly strengthen LBNL science through advanced mathematical and computer science research and advanced software technology. CRD’s Core Competencies and Future Goals Applied Mathematics: CRD has one of the strongest, if not the strongest, applied mathematics competencies among the DOE National Laboratories. Most of the applied and computational mathematicians in CRD are found in the Center for Computational Sciences and Engineering, the Mathematics Group, the Applied Numerical Algorithms Group, the Complex Systems Group, and the Scientific Computing Group. Five of our applied mathematicians have been elected to the U.S. National Academies of Science and Engineering.1 Berkeley Lab applied mathematicians are recognized internationally for their pioneering work in methods, algorithms and software for modeling and simulating complex scientific and engineering phenomena. Areas of research include high-resolution adaptive finite difference algorithms for multi-scale, multi-physics problems, techniques for computing complex flows with moving interfaces, numerical linear algebra, vortex and particle methods, and turbulence theory, among others. Applied mathematics research is at the core of the U.S. Department of Energy’s (DOE) Advanced Scientific Computing Research activities. CRD’s goals for future developments in applied mathematics are to » Continue our international leadership in the development of state-of-the-art mathematics, algorithms and computational techniques, impacting science and engineering challenges from DOE, other government agencies and industry; » Develop simulation and analysis software, in collaboration with computer science teams, that provides exceptional performance on computing architectures from the desktop to the extreme-scale.
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