Abstract: In large 3-D finite element optical tomography problems, computation times for forward and adjoint solutions and for calculation of sensitivities can become prohibitive. Parallelization of computer codes can be used to obtain speedups approaching the number of processors employed, but parallel codes and computer systems can be difficult and expensive to develop and maintain. We show that by employing highly vectorized code that takes advantage of pipelining capabilities in the microprocessor we achieve dramatic speedups for forward and adjoint sensitivity calculations on a single processor microcomputer, and that these speedups actually increase as the problem size increases. Our vectorized implementations involve replication of large amounts of data and are thus memory intensive, however we effectively remove memory constraints by using domain decomposition to control the use of virtual memory. We show that global matrix assembly for a large (98,304 element) mesh is speeded up by a factor of 6.5 and adjoint sensitivity calculations of emission fluence with respect to fluorescence absorption are speeded up by a factor of 502 on a single-processor 2.2 GHz Pentium IV.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).