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/
[2] https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20160007674.pdf

Research Opportunity, Mechanical Engineering, TREE Lab – Summer 2019

By | Educational, Research, SC2, SC2 jobs

Dr. Bala Chandran’s Research Group, Mechanical Engineering, TREE Lab

Dr. Bala Chandran is seeking a highly motivated graduate (doctoral or masters) student interested in
doing research in the broad area of understanding radiative heat transfer in granular and
suspension flows via computational modeling for applications of high-temperature
energy storage and catalysis applications. Applicants are expected to have a sound
knowledge of fluid/continuum mechanics and the fundamentals of heat-transfer;
experience in complex fluids or multiphase flows is desirable, though not essential.
Applicants should be interested in the computational aspects of this project to develop
and write code.


  • Strong analytical and computational skills, and intellectual independence (i.e.,
    able to read books and papers and learn by oneself; able to apply theoretical
    knowledge to practical situations)
  • Relevant course work and experience related to
    • Undergraduate level fluid mechanics, solid mechanics, heat transfer,
      radiation, numerical methods and programming, computational fluid/solid
    • Graduate level courses on any/all of the above topics will be a plus point
  • Excellent professional and work ethic
  • Team player that is ready to interface with people developing experiments on
    this project

Application Procedure

If you are interested in this opportunity, please email Prof. Bala Chandran
(rbchan@umich.edu) all the following documents AS SOON AS POSSIBLE:

  1. A 2-page CV with references listed
  2. Unofficial academic transcript
  3. 1 one-page (maximum) statement of interest that explains why you are best suited for working on the proposed research topic and indicates how you meet the required project criteria.
  4. Slides (maximum 5) that showcase your research experience and contributions

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…