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This year, Bay Area Scientific Computing Day will be at Stanford University on Saturday, December 13, 2014.
An annual one-day meeting focused on fostering interactions and collaborations between researchers in the fields of scientific computing and computational science and engineering from the San Francisco Bay Area, BASCD provides junior researchers a venue to present their work to the local community and an opportunity for the Bay Area scientific and computational science and engineering communities at large to interchange views on today’s multidisciplinary computational challenges and state-of-the-art developments.
If you are interested in speaking at the event, please send an abstract and the title of your presentation to Michael Minion: firstname.lastname@example.org by November 20.
Register if you will attend
Before Friday, December 5th at http://bascd2014.rsvpify.com/
Schedule is available here: https://icme.stanford.edu/system/files/file-insertions/Agenda_3.pdf
Location, Maps, and Parking
BASCD will take place on the Stanford campus in the Jen-Hsun Huang Engineering Center in the Science and Engineering Quad.
3rd floor, room 300 (the Mackenzie Board Room)
Information about visiting the Stanford campus: http://visit.stanford.edu/
Information on visitor parking: http://transportation.stanford.edu/parking_info/VisitorParking.shtml (parking is generally free of charge on Saturday; check posted signs)
ICME will be celebrating our annual holiday party on Friday, December 5th at 2:00p.m. Join us in the ICME main lobby for an afternoon of food, drinks, and good conversation.
Communication-avoiding Krylov subspace methods in finite precision
Communication-avoiding (s-step) Krylov subspace methods can achieve an O(s) reduction in data movement over classical Krylov subspace methods for a fixed number of iterations, allowing the potential for significant speedups on modern computers. However, although the s-step variants are equivalent to the classical variants in exact arithmetic, empirical observations demonstrate that they can behave quite differently in finite precision. Increased roundoff errors can manifest as a loss of accuracy and/or deterioration of convergence rate relative to the classical method, reducing the potential performance benefits of the s-step approach.
Jack Poulson is a Simon’s Math+X Assistant Professor in the Department of Mathematics at Stanford University. His research tends to focus on distributed-memory algorithms and software for modern numerical algorithms, with concentrations on direct linear algebra and optimization and algebra on/with structured matrices.
Ceres Solver (http://ceres-solver.org) is an open source C++ library for modeling and solving nonlinear least squares problems. It is used at Google to estimate the pose of Street View cars, aircraft, and satellites; to build 3D models for PhotoTours; stitch panoramic images on your cellphone, and more. Outside Google, its uses include movie special effects, computer vision, computer graphics, robot navigation, and semi-conductor physics.
I will describe the architecture of Ceres Solver, what goes into engineering a high performance and portable optimization library, and some of the lessons learned from observing people use it over the past four years.
Antony Jameson, Professor of Aeronautics and Astronautics at Stanford, will be celebrating his 80th birthday this year. In honor of his 80th birthday, the Boeing Company will be hosting a Symposium from November 20-21, 2014 with a Dinner & Social on the evening on November 20th. Support and additional sponsorship will be provided by the Institute for Computational and Mathematical Engineering.
Please click here for additional information as well register for the events.
George Papanicolaou is a Professor in the Department of Mathematics at Stanford University. In the past, George Papanicolaou has been interested in waves and diffusion in inhomogeneous or random media and in the mathematical analysis of multi-scale phenomena that arise in their study. Applications come from electromagnetic wave propagation in the atmosphere, underwater sound, waves in the lithosphere, diffusion in porous media, etc. He has studied both linear and nonlinear waves and diffusion, in both direct and inverse problems. He is now working on assessing multiple scattering effects in imaging and communication systems, including time reversal arrays. Another recent interest is financial mathematics, the use of asymptotics for stochastic equations in analyzing complex models of financial markets and in data analysis.
Emmanuel Candes is a Professor of Statistics and Mathematics at Stanford University. His research interests include compressive sensing, mathematical signal processing, computational harmonic analysis, multiscale analysis, scientific computing, statistical estimation and detection, high-dimensional statistics; applications to the imaging sciences and inverse problems. Other topics of recent interest include theoretical computer science, mathematical optimization, and information theory.
Optimizing memory traffic in pf3D laster-plasma simulations
Operationalizing Financial Covenants
We study the interplay between financial covenants and the operational decisions of a firm that obtains financing through a secured (asset-based) lending contract. While it is widely held that covenants serve to protect lenders, the specific ways in which a borrowing firm can adapt its operations in response have not been studied. We characterize the product market conditions, involving demand distribution, growth potential, profit margin, and product depreciation rate, under which covenants are necessary, and argue that these are routinely met in practice. Furthermore, we show that covenants are not substitutable by other contractual terms, such as interest rates and loan limits, and provide operational insights for their optimal design. We discuss when covenants ensure that system-optimal decisions are taken in equilibrium, and show that operational flexibility can impact their effectiveness in a surprising, non-monotonic way.
Michael Minion is a Consulting Professor in the Institute for Computational and Mathematical Engineering. Michael's research interests are in scientific computing with an emphasis on novel algorithms for problems in fluid dynamics.
An efficient non-intrusive uncertainty propagation method for stochastic multi-physics models.
Benevolent vs malicious high frequency trading
ICME hosts the first-ever Xtend event this autumn!
This will be a unique opportunity for alumni, students, faculty, and industry partners to network, discuss current trends in our fields, and attend several workshops and short courses.
Thursday, November 6, 2014
Friday, November 7, 2014: Huang Engineering Center, Mackenzie Room
Saturday, November 8
As always, ICME appreciates the financial generosity of our Alumni which supports our ongoing teaching and research mission.
We look forward to welcoming you back to the Farm!
Questions? Contact us at email@example.com or 650-724-3313.
Xtend Keynote by Persis S. Drell, Dean of the Stanford School of Engineering
Persis S. Drell is the Frederick Emmons Terman Dean of the Stanford School of Engineering, the James and Anna Marie Spilker Professor in the School of Engineering and a professor of Materials Science and Engineering and Physics at Stanford University. She received her B.A. in mathematics and physics from Wellesley College in 1977. She received her Ph.D. in atomic physics from the University of California, Berkeley, in 1983. She then switched to high-energy experimental physics and worked as a postdoctoral scientist with Lawrence Berkeley National Laboratory. She joined the faculty of the Physics Department at Cornell University in 1988. In 2000, she became head of the Cornell high-energy group; in 2001, she was named deputy director of Cornell's Laboratory of Nuclear Studies. In 2002, Dr. Drell accepted a position as Professor and Associate Director, Research Division at SLAC. She was the Deputy Project Manager for the Fermi Gamma Ray Space Telescope 2004-2005. In 2007 she was named Director at SLAC. She stepped down from the SLAC Directorship in 2012. Her current research activities are in Particle Astrophysics and Free Electron Laser science.
Dr. Drell has been the recipient of a Guggenheim Fellowship; a National Science Foundation Presidential Young Investigator Award; she is a fellow of the American Physical Society; a member of the American Academy of Arts and Sciences; and a member of the National Academy of Sciences. In 2012 she was the recipient of the 2012 Helmholtz International Fellow Award for outstanding scientific achievement.
Anders Petersson is a Consulting Professor in the Institute for Computational and Mathematical Engineering. His research interests lie in area of grid generation and numerical solution of partial differential equations. Anders earned his Ph.D. in Numerical Analysis from the Royal Institute of Technology in 1991. He joined the Lawrence Livermore Laboratory in 1999.
Acceleration of PDE-Constrained Optimization Problems using Progressively-Constructed Reduced-Order Models
Optimization problems constrained by nonlinear Partial Differential Equations (PDE) arise in many engineering fields and contexts including inverse modeling, control, and shape optimization. An inherent difficulty in solving PDE-constrained optimization problems is that the solution of the PDE is required at many parameter configurations. For practical problems defined by a complicated geometry and complex physics, each PDE solution will require significant computational resources, rendering the optimization problem prohibitively expensive.
In this talk, I will present a methodology for accelerating the solution of PDE-constrained optimization problems using projection-based Reduced-Order Models (ROM). The key feature of the proposed approach is construction of the ROM occurs incrementally during the optimization process. The proposed methodology will be applied to a variety of PDE-constrained optimization problems, including aerodynamic shape optimization, structural topology optimization, and nozzle design.
Introduction to applied topology and some recent work.
We will give a brief overview of the emerging field of applied topology, in particular, and ordinary persistent homology. We will focus on definitions and examples, and the basic algorithms. Time permitting we will mention some work on deriving correct parallel algorithms, there implementations, and experimental results. For those interested in Numerical Linear Algebra, the theory of persistence generalizes standard numerical linear algebra to the module setting.
Adrian Lew is an Associate Professor in Mechanical Engineering at Stanford University. Prof. Lew's interests lie in the broad area of computational solid mechanics. He is concerned with the fundamental design and mathematical analysis of material models and numerical algorithms.
Generalized Low Rank Models
Inventing the Future of Money
Throughout history technology has played a transformational role in how people relate to and use money. We are living in another time of great transformation as money moves from atoms to bits. But many other factors beyond technology will separate the winners from the losers. James Patterson, head of Capital One Labs, the experimental product and technology arm of Capital One, will share his experiences on reimagining the future of money.
Mykel Kochenderfer is an Assistant Professor in the Department of Aeronautics and Astronautics at Stanford University. Prior to joining the faculty, he was a member of the technical staff at Lincoln Laboratory for seven years where he worked on aircraft collision avoidance for manned and unmanned aircraft. He holds a Ph.D. from the University of Edinburgh and B.S. and M.S. degrees from Stanford. He has worked for Microsoft Research, the Honda Research Institute, and Rockwell Scientific. He is a third generation pilot.
A Framework for Practical Parallel Fast Matrix Multiplication
In this work, we show that novel fast matrix multiplication algorithms can significantly outperform vendor implementations of the classical algorithm and Strassen's fast algorithm on modest problem sizes and shapes. Furthermore, we show that the best choice of fast algorithm depends not only on the size of the matrices but also the shape. We develop a code generation tool to automatically implement multiple sequential and shared-memory parallel variants of each fast algorithm, and this allows us to rapidly benchmark over 20 fast algorithms. We will discuss practical implementation issues for these algorithms on shared-memory machines that can direct further research on making fast algorithms practical.
Compressed representation of Kohn-Sham orbitals via selected columns of the density matrix
Given a set of Kohn-Sham orbitals from an insulating system, we present a direct method to construct a localized basis for the associated subspace. We construct the basis via the use of a set of selected columns of the density matrix (SCDM) coupled with an optional orthogonalization procedure. Our method is simple, robust, does not depend on any adjustable parameters, and may be used in any code for electronic structure calculations. We demonstrate the benefits of such a localized basis by using the SCDM to efficiently perform Hartree-Fock exchange energy calculations with near linear scaling.
Design Optimization and the Consider-Then-Choose Behavioral Model
Recent research in engineering design and operations adopts discrete choice models to maximize profits (or revenues). Conventional discrete choice models are mainly predictive, instead of descriptive, in that they only intend to predict choices rather than describe the processes underlying choice. The consider-then-choose model describes a two-stage decision-making process in which consumers first eliminate a large number of product alternatives with heuristic screening rules, then perform careful tradeoff evaluation over the remaining alternatives.
Consideration, also called choice set formation, is an empirically validated choice behavior that has been shown to greatly improve model quality. From the perspective of firm strategy, modeling consideration introduces discontinuous choice probabilities to optimal design problems, as changes in product features or prices can change individuals' choice sets. We introduce consider-then-choose models, review research suggesting their importance for use in design, and compare several treatments of the discontinuous optimal design problem. We use a stylized new vehicle portfolio design example throughout.
We will see how mathematical tools from the optimal stochastic control theory allow to compute the indifference utility or the super-hedging price of a claim in an incomplete market. In particular, we will focus on the impact of portfolio contraints, such as short sell prohibition, in multidimensional local volatility models. In the one dimensional case, we will observe that super-hedging a claim simply boils down to the replication of a proper ‘facelift’ transform of the claim. We will also provide alternatives to very costly super-hedging prices, by computing quantile hedging prices in a dynamically consistent manner.
Brought to you by NVIDIA and ICME (a NVIDA CUDA Center of Excellence), each year we get together in the Huang Engineering Center for a morning full of
tech talks and an afternoon with GPU computing labs.
This year we have an amazing list of Stanford & SLAC faculty and researchers talking about how GPUs computing is an enabler to new frontiers in Machine Learning, Computer Vision, Astronomy, Medicine.
What is the event: Tech Talks & Hands on GPU Labs in GPU Computing, Machine Learning and Computer Vision
Who is it for: Undergraduate, graduate students, postdocs, researchers, and professors.
Tech Talks – 9AM to Noon
Research Symposium where you will hear from fellow researchers on tools, research discoveries, best practices and trends using GPU computing for computational research.
GPU Computing Hands-on Labs – 1PM to 4PM
GPU Computing workshop with hands-on exercises in GPU Computing where you will learn how to program GPUs via the use of libraries, OpenACC compiler directives, and CUDA programming.
Bill Dally is chief scientist at NVIDIA and senior vice president of NVIDIA Research, the company’s world-class research organization, which is chartered with developing the strategic technologies that will help drive the company’s future growth and success. Dally first joined NVIDIA in 2009 after spending 12 years at Stanford University, where he was chairman of the computer science department and the Willard R. and Inez Kerr Bell Professor of Engineering. Dally and his Stanford team developed the system architecture, network architecture, signaling, routing and synchronization technology that is found in most large parallel computers today. He is a member of the National Academy of Engineering, a Fellow of the American Academy of Arts & Sciences, a Fellow of the IEEE and the ACM. He received the 2010 Eckert-Mauchly Award, considered the highest prize in computer architecture, as well as the 2004 IEEE Computer Society Seymour Cray Computer Engineering Award and the 2000 ACM Maurice Wilkes Award.
Coffee & Lunch will be provided.
Please bring a laptop for remote access to GPU Cluster – no gpu required in your laptop.
Nicola Castelleto is a Postdoctoral Research Fellow in the Department ol Energy Resources Engineering. His research interests concern the physics of fluid flow and deformation in porous media.
Hamdi Tchelepi is a Professor in the Department of Energy Resources Engineering. He is interested in modeling flow and transport in natural porous media. Application areas include reservoir simulation and subsurface CO2 sequestration.
Margot Gerritsen, Professor in the Department of Energy Resources Engineering and the Director of the Institute for Computational and Mathematical Engineering. She is interested in computer simulation and mathematical analysis of engineering processes.
Marco Thiele is a Consulting Associate Professor in the Department of Energy Resources Engineering. In the summer of 2014, Marco Thiele embarked on a new project in thermal EOR for heavy oil along with Margot Gerritsen and Tony Kovscek.
Direct volume rendering for deformable models
We present a system for interactive direct volume rendering of voxel grid data under deformations defined on an underlying tetrahedral mesh. The need for such a system often arises in medical simulation, where the voxel grid may contain radiodensities from a CT scan, and a finite element model deforms an underlying tetrahedral mesh. The fundamental idea of our algorithm is to first map rays in the deformed space of the object to the undeformed space before casting them through the voxel grid. This preliminary step allows us to avoid having to either resample the voxel data each time step or update any kind of underlying acceleration structure. We also introduce a spatial acceleration structure tailored for tetrahedral meshes that uses a combination of octrees and variance-based binary search partitions (BSPs), as well as a texture encoding scheme to upload this structure to a shader.
Scaling Convex Optimization with GPUs
Convex optimization is prevalent in fields such as machine learning, finance, automatic control, and signal processing. To cope with large data sets and real-time processing requirements, it is necessary to use computing architectures and algorithms that scale. By harnessing the power of GPUs, in conjunction with operator splitting methods, we have been able to solve convex optimization problems orders of magnitude faster than traditional solvers. In this presentation we discuss why GPU architectures are ideally suited to convex optimization. Our main contributions are a first of its kind open source CUDA based solver for general convex optimization problems, as well as improved heuristics for choosing parameters in operator splitting methods. Link: foges.github.io/pogs
It is well known that in K-user constant single-antenna interference channels K/2 degrees of freedom (DoF) can be achieved for almost all channel matrices. It is also known that almost all channel matrices admit K/2 DoF, but explicit conditions available guaranteeing K/2 DoF are satisfied only on a set of Lebesgue measure zero. We close this gap by identifying explicit conditions for K/2 DoF, which are satisfied for Lebesgue almost all channel matrices. We also provide a construction of corresponding asymptotically DoF-optimal input distributions. The main technical tool used is a recent breakthrough result by Hochman in fractal geometry. We conclude by discussing connections between interference alignment and additive combinatorics.
NOTE: This seminar will be held with the Information Systems Laboratory (ISL) Colloquium
Lexing Ying is a Professor in the Department of Mathematics and Institute for Computational and Mathematical Engineering at Stanford University. Professor Ying's research focuses on developing fast and accurate numerical algorithms for problems in acoustics and electromagnetics, computational seismology, computational material sciences, and transport theory.
A numerical method for solving Maxwell's equations in free-space using an approzimate IVP Green's function
Two popular classes of methods for solving wave equations such as the free-space Maxwell's equations are finite-difference time-domain (FDTD) schemes and pseudo-spectral schemes. The former typically suffer from restrictive CFL conditions on the size of the time-step for stability but are easily parallelizable, whereas the latter do not face stability restrictions but require a change of basis (i.e., fft) at each step that limits parallelizability. We introduce a scheme for Maxwell's equations in free-space that uses a regularized approximation to the initial-value problem Green's function to allow for little restriction on the time step for stability while maintaining parallelization potential.
A one-shot approach to distributed sparse regression
We devise a one-shot approach to distributed sparse regression in the high-dimensional setting. The main idea is to estimate the regression coefficients by averaging corrected lasso estimates. We show the approach recovers the convergence rate of the lasso as long as the number of machines does not grow too quickly.
Mathematical Programming Methods for Large-scale Structural Topology Optimization
Structural topology optimization is a relatively new but rapidly expanding field because of its interesting theoretical implications in mathematics, mechanics, and computer science, and its important practical applications in the manufacturing and aerospace industries.
Topology optimization determines the optimal distribution of material in a prescribed design domain. The domain is often discretized by finite elements, with the variables representing the density of each element. A common example is maximizing the stiffness of the structure while satisfying a volume constraint and equilibrium equations .
While a variety of large-scale nonlinear solvers could be applied, structural topology optimization problems are usually solved by sequential convex approximation methods such as the Method of Moving Asymptotes (MMA) . This method was specially designed for use within optimal design and is now extensively used in commercial optimal design software as well as academic research codes. However, it is a first-order method with slow convergence rates.
A large set of test problems has now been gathered, along with extensive results for different solvers. Performance profiles compare the special-purpose first-order methods with some general-purpose solvers such as FMINCON, IPOPT, and SNOPT, confirming that the use of second-order information leads to better designs more efficiently than the classical structural optimization solvers.
Given the performance profiles, a sequential quadratic programming method SQP+ has been developed based on the algorithm explained in .Two phases, an inequality and an equality phase, are combined to produce faster convergence. Both phases use second-order information and problem-specific characteristics to improve the efficiency of the solver.
 K. Svanberg. The method of moving asymptotes: A new method for structural optimization. International J. for Numerical Methods in Engineering, 24:2, 359-373, 1987.
 M.P. Bendsoe and O. Sigmund. Topology Optimization: Theory, Methods and Applications, Springer, 2003.
 J.L. Morales, J. Nocedal, and Y. Wu. A sequential quadratic programming algorithm with an additional equality constrained phase, J. of Numerical Analysis, 32:2, 553-579, 2010.
Monte Carlo and Convex Optimization: Importance sampling and Stochastic Optimization
Importance sampling is one of the most widely used variance reduction technique used to speed up Monte Carlo simulations. Roughly speaking, the idea is to sample from an alternative importance distribution that over-weights the important region. However, the basic idea of importance sampling does not specify how to choose this importance distribution, and a poor choice will even worsen the estimate. In this talk, we will introduce an adaptive importance sampling based on stochastic optimization. The method will adaptively improve the importance distribution while simultaneously accumulating the Monte Carlo estimate. We will provide theoretical bounds on the method’s performance and discuss in what sense the method is optimal.
Exact Statistical Inference after Model Selection
We develop a framework for statistical inference after model selection, via lasso or marginal screening, in linear regression. At the core of this framework is a result that characterizes the exact distribution of linear functions of the response y, conditional on the model being selected (``condition on selection" framework). This allows us to construct valid confidence intervals and hypothesis tests for regression coefficients that account for the selection procedure. In contrast to recent work in high-dimensional statistics, our results are exact (non-asymptotic) and require no eigenvalue-like assumptions on the design matrix X. Furthermore, the computational cost of the algorithm is negligible compared to the cost of lasso. Although we focus on marginal screening to illustrate the applicability of the condition on selection framework, this framework is much more broadly applicable. We show how to apply the proposed framework to several other selection procedures including orthogonal matching pursuit, non-negative least squares, and marginal screening+Lasso. This is joint work with Dennis Sun, Yuekai Sun, and Jonathan Taylor.