MICDE Seminar: Ann Almgren, Lawrence Berkeley National Lab

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AnnAlmgren

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.

Next-Generation AMR

Block-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.

 

 

 

 

New MICDE Catalyst Grants to fund research projects in computational science

By | Funding Opportunities

micde2016symposiumfrontpageThe Michigan Institute for Computational Discovery & Engineering (MICDE) seeks proposals for innovative research projects in computational science that combine elements of mathematics, computer science, and cyberinfrastructure. Of interest is computational science research in any emerging area, including but not limited to (a) applications such as neuroscience, ecology, environmental science, evolutionary biology, human-made complex systems, urban infrastructure and energy; and (b) frameworks for scientific software, and exascale computing. Priority will be given to high-impact projects with potential to attract external funding. MICDE expects to fund 3-4 one-year projects at up to $100,000 each.

An informational session will be held on Thursday, Nov. 10, 2016 at 2:00 p.m. in Room D of the Michigan League (911 N. University).

For more information go to http://micde.umich.edu/grants/catalyst-grants/

MICDE affiliated faculty Monica Valluri (Astronomy) recognized for her outstanding research and teaching achievements

By | News

valluriMonica Valluri, a Research Associate Professor in the Department of Astronomy, has been honored with the U-M Research Faculty Achievement Award for her outstanding research and teaching career in theoretical galaxy dynamics. She uses numerical calculations and simulations to probe galactic phenomena, including supermassive black holes and dark matter halos,two types of invisible matter whose presence is inferred primarily from their gravitational effects on stars and other visible matter.

Valluri earned a Ph.D. in astrophysics at the Indian Institute of Science in Bangalore, did postdoctoral research at Columbia University and Rutgers University, and joined the U-M faculty in 2007.

In addition to developing a more accurate method to determine the masses of SMBH, Valluri has transformed our understanding of galactic bars — elongated cigar-shaped clusters of orbiting stars that exist in many spiral galaxies, including the Milky Way. She demonstrated the traditional view of how stars move in bars is incomplete and that neglecting the effects of galactic bars can cause large errors in the measurement of black hole masses and host galaxy properties. Her work soon will be applied to data being gathered by the European Space Agency’s Gaia space observatory and is expected to verify or refute important predictions of the dominant paradigm regarding the nature of dark matter.

Valluri has published 42 journal articles. In addition to creating and teaching undergraduate astronomy and earth and space science courses, Valluri has taught at the Michigan Math and Science Scholars camp for high school students on a number of occasions. She has served on five doctoral committees and mentored 17 undergraduates. She also founded and organizes Conversations on Equity and Inclusion in Astrophysics and has served on the astronomy department’s curriculum committee and Michigan Institute for Research in Astrophysics planning committee. Valluri is chair of the American Astronomical Society Division of Dynamical Astronomy and a member of the Astronomical Society of India and International Astronomical Union.

With information from the record.umich.edu

MICDE Seminar: Andrea Lodi, Polytechnique Montréal

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AndreaLodi

Bio:  Andrea Lodi received a PhD in System Engineering from the University of Bologna in 2000 and he was a Herman Goldstine Fellow at the IBM Mathematical Sciences Department, NY from 2005–2006. He was a full professor of Operations Research at DEI, University of Bologna between 2007 and 2015. Since 2015 he has been the Canada Excellence Research Chair in “Data Science for Real-time Decision Making” at the Polytechnique Montréal. His main research interests are in Mixed-Integer Linear and Nonlinear Programming and Data Science and his work has received recognition including the IBM and Google faculty awards. He is author of more than 80 publications in the top journals of the field of Mathematical Optimization. He serves as Associate Editor for several prestigious journals in the area. He has been the network coordinator and principal investigator of two large EU projects/networks, and, since 2006, consultant of the IBM CPLEX research and development team. Finally, Andrea Lodi is the co-principal investigator (with Yoshua Bengio) of the project “Data Serving Canadians: Deep Learning and Optimization for the Knowledge Revolution”, recently funded by the Canadian Federal Government under the Apogée Programme.

On Wide Split Cuts for Mixed-Integer Programming

Cutting planes (or simply cuts) are a fundamental component of modern Mixed-Integer Linear Programming (MILP) solvers because they help in strengthening the linear programming relaxation, a proxy to make the branchand-bound tree small. A classical way of devising cuts is to exploit disjunctions, for example in the domain of an integer variable, where, of course, no fractional value leads to any feasible solution. Cutting planes of this type, called split cuts, classically exploit disjunctions whose ‘width’ is always equal to one, i.e., no fractional value is feasible between two consecutive integer values. We investigate cutting planes that arise when widening the associated disjunctions. This allows, e.g., to model non contiguous domains of (integer) variables (or, stated differently, ‘holes’ in the domains). The validity of the disjunctions in a MILP can come from either primal or dual information, and we present examples and computational results in both cases. We further explore an exact MILP approach based on these cutting planes, that in addition tackles non-contiguity directly via branching and as a side-effect reduces the model size. (Joint work with P. Bonami, F. Serrano, A. Tramontani, S. Wiese.)

This seminar is co-sponsored by the U-M Department of Industrial & Operations Engineering

MICDE Seminar: Anthony Wachs, University of British Columbia

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cropped-Anthony_Wachs_photo2Bio: Anthony Wachs is an assistant professor with a joint appointment in the departments of Mathematics and of Chemical and Biological Engineering at the University of British Columbia, Vancouver, Canada. He received his B. Sc. and M. Sc. from the University Louis Pasteur of Strasbourg and his PhD from the Institut National Polytechnique of Grenoble in 2000. Right after, he was hired in 2001 as a Fluid Mechanics research engineer at IFP Energies nouvelles (IFPEN, at that time Institut Français du Pétrole) in Paris.

In 2009, he spent a one-year sabbatical at the nuclear research center of Cadarache in the south of France, where he worked for IRSN (the french national safety administration for nuclear energy). In 2010, he got his HDR (French Habilitation to Supervise Research) and was later promoted Scientific Advisor at IFPEN in Multiphase Flows and Scientific Computing. He then moved to IFPEN-Lyon where he supervised a group of researchers (including PhD and post-doc students) on the numerical simulation of reactive particulate flows (www.peligriff.com).

His main research interests are non-Newtonian Flows, Multiphase Flows and High Performance Computing. He collaborates extensively with academic groups in Canada, Brazil, France and Germany.

Micro/meso numerical modeling of flows laden with particles of arbitrary shape

Particulate flows are ubiquitous in environmental, geophysical and engineering processes. The intricate dynamics of these two-phase flows is governed by momentum transfer between the continuous fluid phase and the dispersed particulate phase. When significant temperature differences exist between the fluid and particles and/or chemical reactions take place at the fluid/particle interfaces, the phases also exchange heat and/or mass, respectively. While some multi-phase processes may be successfully modelled at the continuum scale through closure approximations, an increasing number of applications require resolution across scales, e.g. dense suspensions, fluidized beds. Within a multi-scale micro/meso/macro-framework, we develop robust numerical models at the micro and meso scales, based on a Distributed Lagrange Multiplier/Fictitious Domain method and a two-way Euler/Lagrange method, respectively. Collisions between finite size particles are modeled with a Discrete Element Method. Many real-life processes and/or flows involve non-spherical particles. Although there is still a lot to learn about flows laden with spherical particles, there is also a strong incentive to develop new modeling tools to account for non-spherical, angular, convex or even non-convex particles. We discuss assorted issues related to the numerical modelling of flows laden with particles of arbitrary shape. Along the way, we also address high performance computing issues related to our massively parallel numerical tools and challenges to efficiently transfer knowledge from small scales to large scales. We illustrate the modelling capabilities of our tools on the two following problems relevant of applications from the chemical engineering and process industry: (i) a rotating drum filled with non-convex particles and (ii) fixed and fluidized beds of multilobic (and hence non-convex) particles.

This seminar is co-organized with the Applied Interdisciplinary Mathematics program

Research highlights: A new era in disaster research

By | General Interest, News, Research

By Bob Brustman, U-M Civil and Environmental Engineering Department

University of Michigan researchers have received a $2.5 million NSF grant to develop a computational model that is hoped to significantly advance natural hazards engineering and disaster science.

Natural hazards engineers study earthquakes, tornadoes, hurricanes, tsunamis, landslides, and other disasters. They work to better understand the causes and effects of these phenomena on cities, homes, and infrastructure and develop strategies to save lives and mitigate damage.

Sherif El-Tawil

Sherif El-Tawil

Sherif El-Tawil, the lead PI for the project, is a structural engineer interested in how buildings behave, particularly in natural or man-made disasters. He’s developed 3D models and simulators that show precisely what happens in a building if a particular column or wall is destroyed during an extreme event.

On the project team are Jason McCormick, an earthquake engineering expert, Seymour Spence, who has expertise in wind engineering, and Benigno Aguirre, who is a social scientist interested in how people behave during catastrophes. The rest of the team includes. Vineet Kamat, Carol Menassa, and Atul Prakash, who will develop the simulation techniques used in the project.

The researchers of this newly funded project are creating a computational framework, using the Flux high performance computing cluster, that will define a set of standards for disaster researchers to use when constructing their models, enabling simulation models to work together.

El-Tawil explains: “Disaster research is a thriving area because disasters affect so many people worldwide and there is a lot we can do to reduce loss of life and damage to our civil infrastructure.”

“Lots of researchers study disasters, including engineers like me, but also social scientists, economists, doctors, and others. But all of the studies are essentially niche studies, belonging in the field of the researchers. Our objective is to develop computational standards so that social scientists, engineers, economists, doctors, first responders, and everyone else can produce simulators that interact together in a large, all-encompassing simulation of a disaster scenario. Think of it as the civilian equivalent of a war games simulator.”

el-tawil-nsf“Developing this common computational language will allow completely new studies to occur. Someone might look at the effects of an earthquake on a particular town and its citizens and then the subsequent effects of infectious diseases. With a common language, we can really examine the cascading and potentially out-of-control effects that occur during catastrophic events.”

Beyond developing the computational standards, they hope to create something like an app store through which researchers can share their simulation models and foster new collaborations and new areas of research. 

The grant also includes funding for a programmer housed at Advanced Research Computing (ARC) that will become a shared resource for the rest of campus. The Michigan Institute for Computational Discovery and Engineering (MICDE) provided support for the grant submission, and will continue to do so post-award.

The project brings together an experienced team with expertise in engineering, social science, and computer science. Six of the seven core members are from the University of Michigan and the seventh is from the University of Delaware.

Team members:

  • Benigno Aguirre, professor, Disaster Research Center, University of Delaware
  • Sherif El-Tawil, professor, Department of Civil and Environmental Engineering, University of Michigan
  • Vineet Kamat, professor, Department of Civil and Environmental Engineering, University of Michigan
  • Jason McCormick, associate professor, Department of Civil and Environmental Engineering, University of Michigan
  • Carol Menassa, associate professor, Department of Civil and Environmental Engineering, University of Michigan
  • Atul Prakash, professor, Department of Electrical Engineering and Computer Science, University of Michigan
  • Seymour Spence, assistant professor, Department of Civil and Environmental Engineering, University of Michigan

Research highlights: Running climate models in the cloud

By | General Interest, News, Research

Xianglei Huang

Can cloud computing systems help make climate models easier to run? Assistant research scientist Xiuhong Chen and MICDE affiliated faculty Xianglei Huang, from Climate and Space Sciences and Engineering (CLASP), provide some answers to this question in an upcoming issue of Computers & Geoscience (Vol. 98, Jan. 2017, online publication link: http://dx.doi.org/10.1016/j.cageo.2016.09.014).

Teaming up with co-authors Dr. Chaoyi Jiao and Prof. Mark Flanner, also in CLASP, as well as Brock Palen and Todd Raeker from U-M’s Advanced Research Computing – Technology Services (ARC-TS), they compared the reliability and efficiency of Amazon’s Web Service – Elastic Compute 2 (AWS EC2) with U-M’s Flux high performance computing (HPC) cluster in running the Community Earth System Model (CESM), a flagship climate model in the U.S. developed by the National Center for Atmospheric Research.

The team was able to run the CESM in parallel on an AWS EC2 virtual cluster with minimal packaging and code compiling effort, finding that the AWS EC2 can render a parallelization efficiency comparable to Flux, the U-M HPC cluster, when using up to 64 cores. When using more than 64 cores, the communication time between virtual EC2 nodes exceeded the communication time in Flux.

Until now, climate and earth systems simulations had relied on numerical model suites that run on thousands of dedicated HPC cores for hours, days or weeks, depending on the size and scale of each model. Although these HPC resources have the advantage of being supported and maintained by trained IT support staff, making them easier to use them, they are expensive and not readily available to every investigator that needs them.

Furthermore, the systems within reach are sometimes not large enough to run simulations at the desired scales. Commercial cloud systems, on the other hand, are cheaper and accessible to everyone, and have grown significantly in the last few years. One potential drawback of cloud systems is that the user needs to provide and install all the software and the IT expertise needed to run the simulations’ packages.

Chen and Huang’s work represents an important firstxiangleihuangpost2016 step in the use of cloud computing in large-scale climate simulations. Now, cloud computing systems can be considered a viable alternate option to traditional HPC clusters for computational research, potentially allowing researchers to leverage the computational power offered by a cloud environment.

This study was sponsored by the Amazon Climate Initiative through a grant awarded to Prof. Huang. The local simulation in U-M was made possible by a DoE grant awarded to Prof. Huang.

Top image: http://www.cesm.ucar.edu/

MICDE Seminar: Jonathan Freund, University of Illinois at Urbana-Champaign

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Bio: Jonathan Freund is the Donald Biggar Willett Professor of Mechanical Science & Engineering and Aerospace at the University of Illinois at Urbana-Champaign.   He is a Fellow of the American Physical Society, and a winner of the 2008 Frenkiel Prize from its Division of Fluid Dynamics where he currently serves as the division secretary/treasurer.  He is an associate editor of Physical Review Fluids and on the editorial board of Annual Review of Fluid Mechanics.  Computational science has been central to his research, which has included simulations of turbulent jet noise and its control, the dynamics of molecularly thin liquid films, nanostructure formation by ion-bombardment of semiconductor materials, and most recently the dynamics of red blood cells flowing in the narrow confines of the microcirculation.  He co-directs the DOE-funded Center for Exascale Simulation of Plasma-Coupled Combustion at the University of Illinois.

Adjoint-based optimization for understanding and reducing flow noise

Advanced simulation tools, particularly large-eddy simulation techniques, are becoming capable of making quality predictions of jet noise for realistic nozzle geometries and at engineering relevant flow conditions.  Increasing computer resources will be a key factor in improving these predictions still further.  Quality prediction, however, is only a necessary condition for the use of such simulations in design optimization.  Predictions do not of themselves lead to quieter designs.  They must be interpreted or harnessed in some way that leads to design improvements.  As yet, such simulations have not yielded any simplifying principals that offer general design guidance. The turbulence mechanisms leading to jet noise remain poorly described in their complexity.  In this light, we have implemented and demonstrated an aeroacoustic adjoint-based optimization technique that automatically calculates gradients that point the direction in which to adjust controls in order to improve designs.  This is done with only a single flow solutions and a solution of an adjoint system, which is solved at computational cost comparable to that for the flow. Optimization requires iterations, but having the gradient information provided via the adjoint accelerates convergence in a manner that is insensitive to the number of parameters to be optimized.  The talk will review the formulation of the adjoint of the compressible flow equations for optimizing noise-reducing controls and present examples of its use.  We will particularly focus on some mechanisms of flow noise that have been revealed via this approach.

This seminar is co-sponsored by U-M Aerospace Engineering

MICDE Seminar: Jeremy Lichstein, University of Florida

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JeremyLichsteinBio: Jeremy Lichstein is an assistant professor of Biology at the University of Florida. Professor Lichstein got his Ph. D. from Princeton University and was a postdoctoral research fellow at Princeton’s department of Ecology and Evolutionary Biology. He was the recipient of the University of Florida Excellence Award for Assistant Professor, and was named a Florida Climate Institute Fellow for 2016-2017. His research interests are forest dynamics, biodiversity, carbon cycle and climate change.

Biodiversity and the changing Earth System: computational challenges and new answers to old questions

Terrestrial ecosystems currently offset roughly 25% of global annual anthropogenic fossil fuel emissions. However, the fate of this carbon sink is highly uncertain, in large part because global models diverge in their predictions of ecosystem responses to climate change, drought, and other perturbations. Although there is little agreement on how terrestrial ecosystems will respond to global change on decadal and longer time-scales, there is wide consensus that current global models are overly simplistic in their representation of important ecological processes. I will discuss our current understanding of how tree functional diversity is maintained in forests, the consequences of including more realistic levels of functional diversity in global models, and the computational challenges that need to be overcome in order to introduce ecological realism into the Earth System Models that the scientific and policy communities rely on for climate projections. A key result that is emerging from empirical and theoretical studies is that shifts in species composition across time or space (beta diversity) have different (and sometimes opposite) effects on ecosystem stability as local (alpha) diversity.

This seminar is co-sponsored by the U-M department of Ecology and Evolutionary Biology

2015-2016 MICDE Research Snapshot

By | Research

karthik1reduced

Professor Karthik Duraisamy (U-M Aerospace Engineering) demonstrates data-driven turbulence modeling.

The Center for Data-Driven Computational Physics was established as a place to concentrate data-driven modeling research across campus. Its activities are focused on ConFlux, a $3.5M groundbreaking cluster funded by NSF with a unique architecture that connects big-data with traditional HPC clusters. ConFlux went online in April, and several teams are already using it, with five projects participating, totaling more than $3M to advance data-driven modeling. Soon, we expect to announce even more successes that are directly attributable to our pioneering role in this research area. 

The Center for Network and Storage-Enabled Collaborative Computational Science was established to tackle the challenges of extracting scientific results collaboratively from large, distributed or diverse data. This research center is a product of the Open Storage Research Infrastructure (OSiRIS), a $5M multi-institutional NSF investment, and is led by MICDE affiliated  research faculty Shawn McKee.

We hosted 16 internationally known speakers in our seminar series, and had a very successful symposium. With speakers including NSF’s ACI Director Irene Qualters, Tom Hughes from ICES, James Sethian from UC Berkeley, Charbel Farhat from Stanford, and Peter Haas from IBM, these events outlined top priorities in our fields, latest research and computing infrastructure, and increased  awareness of the quality and trend-imposing nature of research activities going on at U-M.

2016-02-05jimbelak

Jim Belak (Lawrence Livermore National Laboratory) delivers a talk titled “Preparing for the Future of Computing: Bridging Scales within the Exascale Materials Co-design Center” as part of MICDE Winter 2016 Seminar Series.

MICDE is coordinating or supporting several large proposal submissions to federal agencies. We offer institutional support and our established educational programs to the faculty teams writing these grants. With the backing of our parent unit, Advanced Research Computing, and their technical and consulting services (ARC-Technology Services, and Consulting for Statistics, Computing and Analytics Research), our proposals have proven stronger by virtue of this support in place behind them.

MICDE also is working with the academic units at U-M to identify compelling new directions for hiring faculty who will drive computational science in the future, and supporting these hiring processes. Many of these blue-sky ideas have come from thematic, faculty-led workshops, which we will continue to organize.