46 Peta-FLOPS computation of defects in solid crystals is a finalist in the highest prize for scientific computing

By | HPC, News, Research

From left: Sambit Das, Phani Motamarri and Vikram Gavini

A team led by Prof. Vikram Gavini (Professor of Mechanical Engineering and MICDE affiliate) and including Dr. Sambit Das (MICDE Fellow) and Dr. Phani Motamarri (Assistant Research Scientist and MICDE affiliate), is one of two finalists nominated for this year’s Gordon Bell Prize. The award, generally considered to be the highest honor of its kind, worldwide, recognizes outstanding achievement in high-performance computing. Gavini’s team has developed a methodology that combines advanced finite-element discretization methods for Density Functional Theory (DFT)1 with efficient computational methodologies and mixed precision strategies to achieve a 46 Peta-FLOPS2 sustained performance on 3,800 GPU nodes of the Summit supercomputer. Their work titled “Fast, scalable and accurate finite-element based ab initio calculations using mixed precision computing: 46 PFLOPS simulation of a metallic dislocation3 system” also involved Dr. Bruno Turcksin and Dr. Ying Wai Li from Oak Ridge National Laboratory, and Los Alamos National Laboratory, and Mr. Brent Leback from NVIDIA Corporation.

Electron density contour of pyramidal II screw dislocation system in Mg with 61,640 electrons (6,164 Mg atoms).

First principle calculation methods4 have been immensely successful in predicting a variety of material properties.  These calculations are prohibitively expensive as the computational complexity scales with the number of electrons in the system. Prof. Gavini’s research work is focussed on developing fast and accurate algorithms for Kohn-Sham5 density functional theory, a workhorse of first principle approaches that occupies a significant fraction of the world’s supercomputing resources. In the current work, Dr. Das, Dr. Motamarri and Prof. Gavini used recent developments in the computational framework for real-space DFT calculations using higher-order adaptive finite elements, and pioneered algorithmic advances in the solution of the governing equations, along with a clever parallel implementation that reduced the data access costs and communication bottlenecks. This resulted in fast, accurate and scalable large-scale DFT calculations that are an order of magnitude faster than existing widely used DFT codes. They demonstrated an unprecedented sustained performance of 46 Peta-FLOPS on a dislocation system containing ~100,000 electrons, which is the subject of the Gordon Bell nomination.

Past winners of the Gordon Bell Prize have typically been large teams working on grand challenge problems in astrophysics, climate science, natural hazard modeling, quantum physics, materials science and public health. The purpose of the award is to track the progress over time of parallel computing, with particular emphasis on rewarding innovation in applying high-performance computing to applications in science, engineering, and large-scale data analytics. If you are attending the SuperComputing’19 conference this year in Denver, you can learn more about Dr. Das, Dr. Motamarri and Dr. Gavini’s achievement at the Gordon Bell Prize finalists’ presentations on Wednesday, November 20, 2019, at 4:15 pm in rooms 205-207

Related Publication: S. Das, P. Motamarri, V. Gavini, B. Turcksin, Y. W. Li, and B. Leback. “Fast, Scalable and Accurate Finite-Element Based Ab initio Calculations Using Mixed Precision Computing: 46 PFLOPS Simulation of a Metallic Dislocation System.” To appear in SC’19 Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, Denver, CO, November 17–22, 2019.

[1] Density functional theory (DFT) is a computational quantum mechanical modeling method used in physics, chemistry and materials science to investigate the electronic structure (or nuclear structure) (principally the ground state) of many-body systems, in particular atoms, molecules, and the condensed phases. https://en.wikipedia.org/wiki/Density_functional_theory.
[2] A PETAFLOP is a unit of computing speed equal to one thousand million million (1015) floating-point operations per second.
[3] In materials science, dislocations are line defects that exist in crystalline solids.
[4] First principle calculation methods use the principle of quantum mechanics to compute properties directly from basic physical quantities such as, e.g., mass and charge.
[5] W. Kohn, L. J. Sham, Self-consistent equations including exchange and correlation effects, Phys. Rev. 140(4A) (1965) A1133.

Research Highlight: Improving aircraft aeropropulsive performance with multidisciplinary design optimization

By | News, Research

Anil Yildirim, Ph.D. Candidate, Aerospace Engineering

MICDE fellow Anil Yildirim, a Ph.D. candidate in the department of Aerospace Engineering, is working towards improving the overall efficiency of commercial tube-and-wing aircraft. The current commercial aircraft design with underwing engines have been the norm since the introduction of the Boeing 707 in the late 50’s [1]. With technological progress in composite materials and electric propulsion, as well as advancement of computational methods and computer power, researchers are developing more energy efficient systems to replace this legacy design. Working with the MDO Lab, lead by Prof. Joaquim R.R.A. Martins, and a team from NASA, Anil is studying the boundary layer ingestion (BLI) system on the STARC–ABL concept, introduced by NASA in 2016 [2] . BLI is an aeropropulsive concept, where a propulsion system is used to ingest the boundary layer generated by the aircraft. This increases propulsive efficiency and reduces the energy dissipated in the wake, effectively improving the overall aeropropulsive performance of the aircraft. Anil and his colleagues in the MDO Lab are using multidisciplinary analysis and optimization tools to study similar technologies, where design intuition is limited and interdisciplinary trades are important. Watch this video to learn more about his work (Authors: Anil Yildirim, Justin S. Gray, Charles A. Mader, Joaquim R. R. A. Martins, DOI: https://doi.org/10.2514/6.2019-3455)

 

[1] “707/720 Commercial Transport: Historical Snapshot,” 2015, http://www.boeing.com/history/
products/707.page
[2] https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20160007674.pdf

MICDE funds 7 new catalyst projects

By | General Interest, Happenings, News

Every year, The Michigan Institute for Computational Discovery & Engineering (MICDE) Catalyst Grants fund innovative research projects in computational science that combine elements of mathematics, computer science, and cyberinfrastructure.

Topics of interest include, but are not limited to:

  • Computational science approaches, algorithms, frameworks, etc.
  • Emerging paradigms in computing (exascale computing, quantum computing, FPGA computing, etc.)
  • Applications in emerging areas (neuroscience, ecology, evolutionary biology, human-made complex systems, mobility etc.)
  • Extensions of traditional computational sciences to complex decision making (reinforcement learning, transfer learning, neuromorphic computing, etc.)
  • Artificial Intelligence informing and informed by science

This year, MICDE awarded its third round of catalyst grants to faculty leading seven innovative projects in computational science.

The projects, supported by up to $90,000 in grant funding, span several research areas ranging from cosmology to artificial intelligence systems in computational systems.

Learn more about the 2019-2020 catalyst grants.

The background image is a multi-color image of the Milky Way disk, its halo and nearby satellite galaxies obtained with the European Space Agency’s Gaia Satellite (http://sci.esa.int/gaia/) . The blue curve shows an example of (half) of a regular trajectory that a star in the halo of the Milky Way might follow. [M. Valluri, Astronomy]

MICDE announces 2019-2020 fellowship recipients

By | Educational, General Interest, Happenings, News

The Michigan Institute for Computational Discovery and Engineering (MICDE) is pleased to announce the 2019-2020 MICDE Fellowship recipients. They were chosen to receive this honor because of their exceptional academic record and the outstanding promise of their research in computational sciences. Fellows are working on a wide range of groundbreaking problems, including the strategic interaction of parties and electors in democratic elections (S. Baltz, Political Science), the effects of disruption of synaptic signaling on neuronal structures (M. Budak, Biophysics),  and on the development of robust, efficient, and scalable algorithms for multidisciplinary design optimization applications applied to the design of the next generation of fuel-efficient aircrafts (A. Yildirim, Aerospace). The fellowships, which carry a $4,000 stipend, are meant to augment other sources of funding and are available to students in our three educational programs. Visit our fellowship page to learn more about the program and the fellows.

2019-2020 MICDE Fellows (from left to right) Guodong Chen (Aero), Suyash Tandon (ME), Jiale Tan (Epidemiology), Fuming Chang (ClaSp), Kelly Broen (Epidemiology), Bradley Dice (Physics), Liz Livingston (ME), Will Weaver (EEB), Yuan Yao (ME), Samuel Baltz (Pol Sci), Joe Hollowed (Physics), Minki Kim (ME), Allison Roessler (Chem), Chongxing Fan(ClaSp), Maral Budak (Biophysics), Saibal De (Math), Xian Yu (IOE), Jiaming Zhang (Physics). [Not pictured: Thomas Waltmann (Physics), Anil Yildirim (Aero), and Jessica Conrad (IAM)]

AWARDEES

Samuel Baltz, Political Science
Kelly Broen, Epidemiology
Maral Budak, Biophysics
Fuming Chang, Climate and Space Sciences and Engineering
Guodong Chen, Aerospace Engineering
Jessica Conrad, Applied & Interdisciplinary Mathematics
Saibal De, Applied & Interdisciplinary Mathematics
Bradley Dice, Physics
Chongxing Fan, Climate and Space Sciences and Engineering
Joseph Hollowed, Physics
Minki Kim, Mechanical Engineering
Elizabeth Livingston, Mechanical Engineering
Allison Roessler, Chemistry
Jiale Tan, Epidemiology
Suyash Tandon, Mechanical Engineering
Thomas Waltmann, Physics
William Weaver, Ecology and Evolutionary Biology
Yuan Yao, Mechanical Engineering
Anil Yildirim, Aerospace Engineering
Xian Yu, Industrial & Operations Engineering
Jiaming Zhang, Physics

MICDE Director, Krishna Garikipati, wins USACM Fellow award

By | News, Uncategorized

Krishna Garikipati, professor of Mechanical Engineering and of Mathematics, and director of MICDE, has been granted a 2019 United States Association for Computational Mechanics (USACM) Fellows award for his work in developing numerical methods applied to strongly nonlinear problems in living and nonliving material systems.

The Fellows Award recognizes individuals with a distinguished record of research, accomplishment and publication in areas of computational mechanics and demonstrated support of the USACM through membership and participation in the Association, its meetings and activities. All recipients shall be members in good standing of USACM. Multiple awards may be given at two-year intervals.

MICDE to host NSF Computational Mechanics Vision workshop

By | News

In Fall 2019, MICDE will host the NSF workshop entitled Computational Mechanics Vision Workshop. Organized by Boston University, Duke University and the University of Michigan. The workshop’s aim is to solicit and synthesize directions for computational mechanics research and education in the United States over the next decade and beyond from a diverse cross section of scientists and engineers. Read more…

 

Introducing the new Clare Boothe Luce Graduate Fellows at the University of Michigan

By | Feature, News

The Michigan Institute for Computational Discovery and Engineering is pleased to announce the recipients of the Clare Boothe Luce graduate fellowships at the University of Michigan. Jessica Conrad, MS, currently an internee at LLNL, and Elizabeth Livingston, MS, a graduate of the University of Illinois, Urbana-Champaign, will be joining the University of Michigan in the Fall of 2019 to work towards their PhD. They were chosen because of their exceptional academic records and excellent preparation for graduate studies in computational sciences. Elizabeth will join the Mechanical Engineering department in the College of Engineering, and Jessica will join the Applied and Interdisciplinary Mathematics program in the College of Literature, Sciences and the Arts. As required by the fellowship, both students will enroll in the joint PhD in Scientific Computing program.

Elizabeth Livingston, Clare Boothe Luce Fellow at the University of Michigan

Elizabeth Livingston completed a BSc in Engineering Mechanics (with a minor in Computational Science and Engineering) and a MS in Mechanical Engineering at the University of Illinois, Urbana-Champaign. Elizabeth will join Prof. Garikipati’s research group in Mechanical Engineering. Elizabeth will carry out research in computational modeling of biomedical engineering problems. Of particular interest to her is the growth and remodeling of the cardio-vascular system. She will apply cutting-edge techniques of data-driven computational modeling to this topic using principles of scientific computing, including machine learning, uncertainty quantification, and finite element methods.

Elizabeth has a strong academic background, thriving while performing research in fields where women are underrepresented. Her ambition is to become a university faculty member, doing research in computational science. She looks forward to collaborating with colleagues and working with students to help them to succeed as others have helped her.

Jessica Conrad has a BS in mathematics and public health, a master’s in biostatistics, and an excellent track record of computational research both in her training and current work at Los Alamos National Laboratories. This background forms an ideal foundation for blending computing and mathematics in her PhD work, which will enable her to build a successful career in STEM. Jessica’s proposed area of study is in inverse problems in mathematical epidemiology, particularly focused on using computational and mathematical methods to gain useful insights into public health problems. A critical part of this work will include developing computational approaches to parameter identifiability. Conrad plans to work with Prof. Marisa Eisenberg, an expert in identifiability and infectious disease modeling, as one of her two primary co-mentors in the AIM program.

Jessica Conrad, Clare Boothe Luce Fellow at the University of Michigan

The Clare Boothe Luce program is funded by the Henry Luce Foundation. The program was created by Clare Boothe Luce, with the goal of increasing the participation of women in the sciences, mathematics and engineering at every level of higher education. It also serves as a catalyst for colleges and universities to be proactive in their own efforts toward this goal. At the University of Michigan, the program aims to increase women’s participation in the scientific computing community by recruiting top-of-the class women into the PhD in Scientific Computing program. The program is designed to allow the fellows to focus on their academic success by funding their first 3 years in the PhD, freeing them to try high-risk, innovative research projects in a unique interdisciplinary program, with ample networking opportunities and career support.

Ruiwei Jiang wins NSF CAREER award for work in operations research

By | General Interest, Happenings, News

Ruiwei Jiang, assistant professor in Industrial and Operations Engineering and an MICDE-affiliated faculty member, has won an NSF CAREER award for work evaluating the potential benefits of incorporating decision-dependent uncertainty into decision-making problems in service industries and investigate new optimization approaches to maneuvering such uncertainty to improve decision-making.

Read more…

Women in HPC launches mentoring program

By | Educational, General Interest, HPC, News

Women in High Performance Computing (WHPC) has launched a year-round mentoring program, providing a framework for women to provide or receive mentorship in high performance computing. Read more about the program at https://womeninhpc.org/2019/03/mentoring-programme-2019/

WHPC was created with the vision to encourage women to participate in the HPC community by providing fellowship, education, and support to women and the organizations that employ them. Through collaboration and networking, WHPC strives to bring together women in HPC and technical computing while encouraging women to engage in outreach activities and improve the visibility of inspirational role models.

The University of Michigan has been recognized as one of the first Chapters in the new Women in High Performance Computing (WHPC) Pilot Program. Read more about U-M’s chapter at https://arc.umich.edu/whpc/

Balzano wins NSF CAREER award for research on machine learning and big data involving physical, biological and social phenomena

By | General Interest, Happenings, News, Research

Prof. Laura Balzano received an NSF CAREER award to support research that aims to improve the use of machine learning in big data problems involving elaborate physical, biological, and social phenomena. The project, called “Robust, Interpretable, and Efficient Unsupervised Learning with K-set Clustering,” is expected to have broad applicability in data science.

Modern machine learning techniques aim to design models and algorithms that allow computers to learn efficiently from vast amounts of previously unexplored data, says Balzano. Typically the data is broken down in one of two ways. Dimensionality-reduction uses an algorithm to break down high-dimensional data into low-dimensional structure that is most relevant to the problem being solved. Clustering, on the other hand, attempts to group pieces of data into meaningful clusters of information.

However, explains Balzano, “as increasingly higher-dimensional data are collected about progressively more elaborate physical, biological, and social phenomena, algorithms that aim at both dimensionality reduction and clustering are often highly applicable, yet hard to find.”

Balzano plans to develop techniques that combine the two key approaches used in machine learning to decipher data, while being applicable to data that is considered “messy.” Messy data is data that has missing elements, may be somewhat corrupted, or is filled heterogeneous information – in other words, it describes most data sets in today’s world.

Balzano is an affiliated faculty member of both the Michigan Institute for Data Science (MIDAS) and the Michigan Institute for Computational Discovery and Engineering (MICDE). She is part of a MIDAS-supported research team working on single-cell genomic data analysis.

Read more about the NSF CAREER award…