MICDE catalyst grant leads to new NSF funding to study cascade “ecohydromics” in the Amazonian headwater system

By | Feature, News, Research

The Amazon Basin cycles more water through streamflow and evaporation than any other contiguous forest in the world, and transpiration by trees is a critical part of this cycle. Understanding how plant roots, stems, and leaves interact with soil water to regulate forest transpiration across landscapes is a critical knowledge gap, especially as climate changes. Professor Valeriy Ivanov, from the Department of Civil and Environmental Engineering at U-M, is the lead investigator in a newly NSF funded project that links diverse disciplines – plant ecophysiology, ecology, and hydrology – and will build a unique modeling framework to characterize landscape variation in physiological and hydrological processes in the Amazon Basin. The framework will integrate a wide array of field observations with detailed watershed modeling for hypothesis testing. The team includes Tyeen Taylor, research fellow also from the Civil and Environmental Engineering Department at U-M, and many collaborators in the U.S. at the University of Arizona, University of West Virginia, University of Nebraska, as well as Brazilian researchers at the Federal University of Eastern Para, and Federal University of Amazonas, National Institute for Amazonian Research, and Eastern Amazon Agricultural Agency. Detailed, physical models of ecophysiology and above- and below-ground hydrology will be informed by observations of leaf physiology, tree morphological traits, soil moisture, groundwater, and streamflow. Data and models will be integrated employing novel tools in probabilistic learning and uncertainty quantification. The computational framework tools to be used in this project were developed in part with the support from MICDE Catalyst grant program for the 2018 project “Urban Flood Modeling at “Human Action” Scale: Harnessing the Power of Reduced-Order Approaches and Uncertainty Quantification” led by Prof. Ivanov. 

Given (a) a mechanistic model M (e.g., a stomatal conductance model), (b) one can treat its inputs 𝛏 (e.g., parameters) as random variables. These inputs are sampled and model simulations are carried out. Using (c) PCEs, we construct a surrogate model that best approximates the model output – left-hand-side of (c). The surrogate is then evaluated with Monte Carlo simulations and used for (d) parameter inference. (d.1) is the flow of outputs from the surrogate model into a likelihood function L (D | 𝛏) to compare the surrogate model output and observed data D. This inference produces the posterior distribution for 𝛏. This pdf can then be sent back to the surrogate in (d.2) to reduce the uncertainty in the inputs and to obtain pdf for a quantity of interest (e).

“The reduced-ordered modeling approach developed during the MICDE Catalyst grant project is a key element of the new project,” said Prof. Ivanov, “the MICDE seed funding has allowed us to build a general framework that is applicable to a wide range of computational applications in earth-system science, and thus made our project proposal more competitive”.

The MICDE Catalyst Grants program funds projects that have the potential to catalyze and reorient the directions of their research fields by developing and harnessing powerful paradigms of computational science. This new NSF project is an example of the reach of the program.

Read more.

We welcome 20 students to the 2021-22 class of MICDE graduate fellows

By | Feature, News

MICDE is proud to announce the recipients of the 2021 MICDE graduate fellowships. The fellows’ research projects involve the use and advancement of scientific computing techniques and practices. “This year, MICDE awarded fellowships in a wide array of disciplines ranging from chemistry to biostatistics and interdisciplinary mathematics to applied physics,” said Krishna Garikipati, MICDE director and professor of mechanical engineering and mathematics. “Engineering is also well represented with fellows focused on disciplines such as aerospace, biomedical, civil and environmental, climate and space, industrial and operations, materials science, mechanical, and naval architecture and marine engineering.”

For the past seven years, MICDE has awarded fellowships to over 135 graduate students from our large community of computational scientists. “I am so excited and honored to be a part of the MICDE Fellowship program. My research interest is in an interdisciplinary field between healthcare and data science. This fellowship symbolizes my core value for career development as a data scientist in healthcare,” said 2021 MICDE Fellowship recipient Hyeon Joo, Ph.D. pre-candidate in health infrastructure and learning systems and scientific computing. The MICDE graduate student top-off fellowship provides students with a stipend to use for supplies, technology, and other materials that will further their graduate education and research. Among other things, awards have helped many to travel to conferences and meetings around the world to share the rich and diverse research in computational science being carried out at U-M.

Yifu An, Climate and Space Sciences Engineering
Andre Antoine, Applied Physics
Shreyas Bhat, Industrial and Operations Engineering
Erin Burrell, Mechanical Engineering and Scientific Computing
Alanah Cardenas-O’Toole, Climate and Space Sciences Engineering
Brian Chen, Applied and Interdisciplinary Mathematics
Xinyu Fei, Industrial and Operations Engineering and Scientific Computing
Nicholas Galioto, Aerospace Engineering
Vishwas Goel, Materials Science and Engineering and Scientific Computing
Min-Chun Han, Civil and Environmental Engineering
Dalia Hassan, Chemistry and Scientific Computing
Alexander Hrabski, Naval Architecture and Marine Engineering and Scientific Computing
Javiera Jilberto Vallejos, Biomedical Engineering and Scientific Computing
Hyeon Joo, Learning Health Sciences and Scientific Computing
Timothy Jugovic, Chemistry and Scientific Computing
Ismael Mendoza, Physics and Scientific Computing
Aagnik (Nick) Pant, Applied Physics and Scientific Computing
Hardik Patil, Civil and Environmental Engineering
Amanda Wang, Materials Science and Engineering
Wenbo Wu, Biostatistics and Scientific Computing

Learn more about the fellows and the MICDE Fellowship program

Postdoc Position in Mechanical/Biomedical Engineering at the University of Michigan

By | News, SC2 jobs

Job Description:

Example of an image-based geometric model of a human aorta, discretized using an unstructured linear tetrahedral mesh.

Prof. Jesse Capecelatro in the Department of Mechanical Engineering and Prof. Alberto Figueroa of Biomedical Engineering are currently seeking a post-doctoral scholar for a one-year position.

The project aims at developing a numerical framework to simulate a large number of particles within the human body, from deformable red blood cells within arteries to better understand stroke, to rigid calcite particles in the ear canal responsible for vertigo. This will be performed within the CRIMSON (CardiovasculaR Integrated Modeling and SimulatiON) software environment, a Fortran-based finite element solver that simulates fluid flow in patient-specific geometries on unstructured grids.

Required Qualifications:

  • PhD in Engineering or a related field (e.g., Physics, Mathematics, or Computer Science)
  • Experience in Scientific Computing (proficiency in MPI and Fortran/C)
  • Interest in biological fluid dynamics and multiphase flow.

How to Apply:

If you are interested in this position, please email your curriculum vitae and at
least two references to Jesse Capecelatro (jcaps@umich.edu).

2021-2022 Catalyst Grant awardees continue to forge new fronts in computational science

By | Feature, News, Research

The Michigan Institute for Computational Discovery and Engineering (MICDE) announced the awardees of the 2021-2022 round of Catalyst Grants. Since 2017 MICDE Catalyst Grants program has funded a wide spectrum of cutting-edge research, this year focusing on AI for physically-based biomedicine, quantum computing, convergence of natural hazards with economic dislocation, and computational integration across scales and disciplines in biology. The five projects awarded in this round represent these new frontiers of computational research spearheaded by the Institute through its initiatives.

Prof. Shravan Veerapaneni (Mathematics) is working on advancing quantum algorithm research. His team will develop a Variational Quantum Monte Carlo algorithm that can potentially be applied to a wide range of linear algebraic tasks, like QR and Singular Value Decomposition (SVD). 

Profs. Salar Fattahi (Industrial and Operations Engineering) and Arvind Rao (Computational Medicine and Bioinformatics, Biostatistics) are revisiting existing theoretically powerful maximum-likelihood estimation mathematical methods to identify areas of weakness and strengthen them for use in biomedical, largely genomic, applications.

Profs. Gary Luker (Microbiology and Immunology), Nikola Banovic (Electrical Engineering and Computer Science), Xun Huan (Mechanical Engineering), Jennifer Linderman (Biomedical Engineering and Chemical Engineering), and Kathryn Luker (Radiology), will develop a physics/chemistry-aware inverse reinforcement learning (IRL) computational framework that will support the understanding single-cell and cooperative decision-making that drive tumor growth, metastasis, and recurrence.

Profs. Seth Guikema (Civil and Environmental Engineering and Industrial and Operations Engineering) and Jeremy Bricker (Civil and Environmental Engineering) will develop an integrated computational modeling approach to studying equity and resilience during natural hazard events, specifically estimating what essential services are the main constraints on individuals returning to a more normal life post-hazard, and assess inequities in resilience to coastal flooding events. 

Prof. Jesse Capecelatro (Mechanical Engineering and Aerospace Engineering) and Alberto Figueroa (Biomedical Engineering and Vascular Surgery), will develop a versatile, physics-driven, computationally efficient, and massively parallel numerical framework to simulate the interaction between fluids and biological particles in patient-specific vasculature geometries. This framework will enable next-generation computer-aided diagnostics.

“This year’s cohort of MICDE Catalyst Grants range from quantum computing for engineering science, AI for the physics of cancer, and computational advances in hazards engineering, through mathematical advances in data science, and bioengineering,” said MICDE Director Krishna Garikpati, a professor of mathematics and mechanical engineering. “These projects represent new frontiers of computational research spearheaded by MICDE through its initiatives.”

Learn more about MICDE’s Catalyst Grant program and funded projects here.

“This year’s cohort of MICDE Catalyst Grants … represent new frontiers of computational research spearheaded by MICDE through its initiatives.”

Krishna Garikipati
Director, MICDE

Prof. Monica Valluri Joins MICDE Leadership Team

By | News, Uncategorized
Portrait of Monica Valluri

Monica Valluri, Astronomy

This month, MICDE welcomed Monica Valluri, Research Professor of Astronomy, as an Associate Director. Prof. Valluri’s research is on the theoretical framework of Galactic Dynamics. Dr. Valluri uses galactic dynamics to interpret and model motions of stars observed with state-of-the-art telescopes using new and powerful numerical methods. Her work has led to important insights into how these dark components influence the structure and evolution of galaxies. In 2016, she won the University of Michigan Research Faculty Achievement Award for her or her outstanding research and teaching career in theoretical galaxy dynamics. In 2019, Prof. Valluri and a team of international collaborators were awarded an MICDE Catalyst Grant, “Determining the 3D Shape of Milky Way’s Dark Matter Halo” that has lead to several federally funded grants.

Annette Ostling - Portrait

Annette Ostling, Ecology and Evolutionary Biology

Prof. Valluri’s appointment succeeds former Associate Director Annette Ostling, Associate Professor of Ecology and Evolutionary Biology. We thank Prof. Ostling for her service to MICDE. Her contributions to the growth and resiliency of MICDE have been numerous over the last five years. 

Postdoc Position at Sandia National Labs on Computational Materials and Data Science

By | News, SC2 jobs

Sandia National Labs is hiring postdoctoral researchers to support work on computational materials and data science.


What Your Job Will Be Like:

We are seeking hardworking emerging scientists interested in working at the intersection of computational materials science, data science, and materials characterization. The candidate will be part of a team of scientists developing and deploying machine-learning-based and computational materials science capabilities to (i) detect and reveal mechanisms associated with physical and chemical processes occurring during manufacturing of nanostructured materials, (ii) prognose materials behavior, and (iii) adapt manufacturing of these materials. These capabilities that are developed are expected to become enduring toolsets for both in-house and visiting researchers at the Center for Integrated Nanotechnologies (CINT) and supporting various projects across the laboratories’ complex.

 

Required Qualifications:

  • Possess, or are nearing completion of, a PhD in Applied Mathematics, Computational Sciences, Materials Science & Engineering, Physics, or related field.
  • Demonstrated research success, written communication, and presentation skills as evidenced by a record of publication in peer-reviewed journals and external presentations at professional scientific conferences
  • Experience with advanced data analysis techniques and/or computational materials science tools such as molecular dynamics, phase field, etc.

Desired Qualifications:

  • Familiarity or direct experience with machine learning/artificial intelligence
  • Proven programming skills in Python, Matlab, Mathematica, C++, or similar
  • Experience with image analysis techniques
  • Experience in direct comparison of experimental and modeling data
  • Experience with mechanical, chemical, thermal and/or irradiation properties of materials
  • Interest in developing new analysis techniques
  • Interest in developing capabilities with commercialization potential
  • Interest in collaborative team-based research

About the Team:

The Nanostructure Physics department (01881) conducts research to establish the scientific principles that govern the design, performance, and integration of nanoscale materials into microscale and macroscale systems and devices. Department members perform discovery and use-inspired basic research in support of the DOE/SC Center for Integrated Nanotechnologies (CINT) and other Sandia programs. CINT is a DOE/Office of Science National User Facility operated jointly by Sandia and Los Alamos National Laboratories with facilities at both Laboratories.

Our team is committed to nurturing environment compatible with a broad group of people and perspectives in accordance with the changing makeup of the workforce. In support of this vision, our center actively recruits applicants from diverse groups of backgrounds and fosters an inclusive community.

Join us and work towards your goals while making a difference!

 

Position Information:

This postdoctoral position is a temporary position for up to one year, which may be renewed at Sandia’s discretion up to five additional years. The PhD must have been conferred within five years prior to employment.

Individuals in postdoctoral positions may bid on regular Sandia positions as internal candidates, and in some cases may be converted to regular career positions during their term if warranted by ongoing operational needs, continuing availability of funds, and satisfactory job performance.

 

How to Apply:

1. Select the link to access our careers site.
2. Sign In to access your account or if you are not an existing user select the New User link to create one.
3. Review the job description and select the Apply button to begin your application.

 

About Sandia:

Sandia National Laboratories is the nation’s premier science and engineering lab for national security and technology innovation, with teams of specialists focused on cutting-edge work in a broad array of areas. Some of the main reasons we love our jobs:
• Challenging work with amazing impact that contributes to security, peace, and freedom worldwide
• Extraordinary co-workers
• Some of the best tools, equipment, and research facilities in the world
• Career advancement and enrichment opportunities
• Flexible work arrangements for many positions include 9/80 (work 80 hours every two weeks, with every other Friday off) and 4/10 (work 4 ten-hour days each week) compressed workweeks, part-time work, and telecommuting (a mix of onsite work and working from home)
• Generous vacations, strong medical and other benefits, competitive 401k, learning opportunities, relocation assistance and amenities aimed at creating a solid work/life balance*

World-changing technologies. Life-changing careers. Learn more about Sandia at: http://www.sandia.gov
*These benefits vary by job classification.

 

Security Clearance:

This position does not currently require a Department of Energy (DOE) security clearance.

Sandia will conduct a pre-employment drug test and background review that includes checks of personal references, credit, law enforcement records, and employment/education verifications. Furthermore, employees in New Mexico need to pass a U.S. Air Force background screen for access to Kirtland Air Force Base. Substance abuse or illegal drug use, falsification of information, criminal activity, serious misconduct or other indicators of untrustworthiness can cause access to be denied or terminated, resulting in the inability to perform the duties assigned and subsequent termination of employment.

If hired without a clearance and it subsequently becomes necessary to obtain and maintain one for the position, or you bid on positions that require a clearance, a pre-processing background review may be conducted prior to a required federal background investigation. Applicants for a DOE security clearance need to be U.S. citizens. If you hold more than one citizenship (i.e., of the U.S. and another country), your ability to obtain a security clearance may be impacted.

Members of the workforce (MOWs) hired at Sandia who require uncleared access for greater than 179 days during their employment, are required to go through the Uncleared Personal Identity Verification (UPIV) process. Access includes physical and/or cyber (logical) access, as well as remote access to any NNSA information technology (IT) systems. UPIV requirements are not applicable to individuals who require a DOE personnel security clearance for the performance of their SNL employment or to foreign nationals. The UPIV process will include the completion of a USAccess Enrollment, SF-85 (Questionnaire for Non-Sensitive Positions) and OF-306 (Declaration of for Federal Employment). An unfavorable UPIV determination will result in immediate retrieval of the SNL issued badge, removal of cyber (logical) access and/or removal from SNL subcontract.  All MOWs may appeal the unfavorable UPIV determination to DOE/NNSA immediately. If the appeal is unsuccessful, the MOW may try to go through the UPIV process one year after the decision date.

 

EEO:

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or veteran status and any other protected class under state or federal law.

The crucial role of massively parallel simulations in future space exploration missions

By | HPC, News, Research

The NASA Mars 2020 Mission was launched with the goal of seeking signs of ancient life and collecting samples of rock and regolith (broken rock and soil) for possible return to Earth. Perseverance, the mission’s rover, is testing technologies to help pave the way for future human exploration of Mars. While Perseverance was launched in the summer of 2020, landing on the martian surface on February 18, 2021, the journey started years earlier when the mission’s objectives were outlined, including realistic surface operations, a proof-of-concept instrument suite, and suggestions for threshold science measurements that would meet the proposed objectives. The success of this, as well as past and future missions, is the collective result of thousands of NASA funded projects from teams of researchers and scientists from all over the country that span many decades. University of Michigan Professor Jesse Capecelatro (Mechanical Engineering & Aerospace Engineering) is the lead of one of these projects. In 2016, his research group started working on a project aimed to develop high fidelity models of plume-Induced soil erosion during lunar and planetary landings that will be used in future missions. 

During descent, exhaust plumes fluidize surface soil and dust, forming craters and buffeting the lander with coarse, abrasive particles. The SkyCrane technology, used by the Curiosity Rover in 2012 and by Perseverance in 2021, was designed to avoid plume-surface interactions by keeping the jets far above the surface. Regardless of this feature, a wind sensor on NASA’s Mars Curiosity rover was damaged during landing. In the Perseverance’s video footage of the landing, significant erosion and high-speed ejecta were observed.  It is also not a practical option for future crewed and sample return missions. 

NASA is aiming to improve rovers’ landing gears for future missions. Computational models and simulations are a critical component to achieve this as it is not feasible to mimic the martian or other celestial bodies entry conditions and run thousands of tests in a lab anywhere on Earth. This is where Prof. Capecelatro’s research group, including doctoral candidates Greg Shallcross, and Meet Patel, and postdoctoral fellow Medhi Khalloufi, work comes in as the accurate prediction of surface-plume interactions is necessary for the overall success of future space missions. While simulations of surface-plume interactions have been conducted in the past, these are outdated, and typically relied on simplified assumptions that prevent a detailed and dynamic analysis of the fluid-particle coupling. Capecelatro’s research code will provide NASA with a framework to better predict how different rover designs would impact the landing, specifically the effects of the force of the collision on the planet’s surface, and ability to adjust the rover’s landing trajectory independent of the NASA mission control team on earth.  

Prof. Capecelatro’s research project utilizes predictive simulation tools to capture the complex multiphase dynamics associated with rocket exhaust impingement during touchdown. Even in the most powerful supercomputers, a direct solution approach is only capable of accounting for  about a thousand particles at the same time, so accurate and predictive multi-scale models of the unresolved flow physics are essential. 

Full landing site image credit: NASA/JPL-Caltech (mars.nasa.gov/resources/24762/mars-sample-return-lander-touchdown-artists-concept/); particle and intermediate scale images: Capecelatro’s Research Group

Particle Scale

The group has been developing the simulation capabilities to directly resolve the flow at the sub-particle scale to shed light on important physics under the extreme conditions relevant to particle-structure interactions. Their model uses a massively parallel compressible particle-laden flow simulation tool where the exhaust plume and its corresponding flow features are computed in an Eulerian-Lagrangian framework. At this scale, for example, the flow between individual particles are resolved, providing important insight on drag and turbulence under these extreme conditions.

Intermediate Scale

As a next step, the particle-scale results inform models used in the intermediate-scale simulations developed by the group, where particles are still tracked individually but the flow is not resolved at a sub-particle resolution, allowing them to simulate upwards of 1 billion particles. At this scale, an Eularian-Lagrangian framework is used to incorporate the ground’s particle flow with the jet’s plume. 

Full Landing Site Scale

While the intermediate-scale simulations allow to study erosion and cratering, a full landing site that contains trillions of particles is still out of reach even in the most powerful HPC clusters. After further modeling, Capecelatro’s multi-scale framework will be handed over to NASA where it will be incorporated in simulations of the full landing site. At this scale, NASA’s framework uses an Eularian-based, two fluid model that treats both fluid and particles as a continuum, informed by the particle- and middle-scales models. 

Mission Mars 2020 is expanding NASA’s robotic presence on the red planet. While it is a big step to set the stage for future human exploration, the Perseverance Rover needs further redesign to make the voyage safe for humans. Capecelatro’s physics-based models are aiding this task by helping predict and model more accurately the outcomes of a spacecraft attempting to safely land millions of miles from home. As in many other fields, computational science will continue to play a critical role in the future of humanity’s quest to conquer space. #computationalscience everywhere!

Related links:
Sticking the landing on Mars: High-powered computing aims to reduce guesswork
Capecelatro’s Research Group
NASA 2020 Mars Mission: Perseverance Rover

Summer STEM Institute (SSI) Teaching Opportunties

By | News, SC2 jobs

The Summer STEM Institute (SSI) is a virtual education program that teaches programming, data science, and research.

 

SSI is currently hiring for both part-time and full-time roles for summer 2021. Both roles offer competitive compensation.

Role 1: Part-Time Research Mentor (10-15 hours/week):

Responsibilities: Lead a virtual lab of 2-3 students; mentor students through the ideation and completion of their own computational or theoretical research projects; support students through the creation of weekly research deliverables, including a background research report, a research proposal, and a final paper and presentation

Qualifications: Passion for teaching and mentorship; graduate student, postdoctoral fellow, or (in exceptional circumstances) undergraduate with extensive programming or research experience in a computational or theoretical field; past research experiences and deliverables, including published papers and presentations

Note: Research mentors are able to work for the program alongside full- time job, internship, or research commitments.

Role 2: Full-Time Teaching Fellow (40 hours/week):

Responsibilities: Teach and work closely to support students through the data science and research bootcamp, answer student questions and discussion board posts; host office hours; leave feedback on student homework assignments

Qualifications: Passion for teaching and mentorship; experience with Python programming and data science libraries (numpy, pandas, matplotlib, sklearn); experience with data science and the research process


Interested undergraduate and graduate students are encouraged to apply. Please fill out this 2-minute interest form. If we decide to move forward with your application, we will send more information about the roles and also times to schedule an interview. If you have any questions, please reach out to hiring@summersteminstitute.org.

Seeking Master’s Student Research Engineer to Build GUI for IGA Meshing

By | News, SC2 jobs

YB Numerics Inc. is seeking to hire a research engineer to work on the development of a meshing tool and GUI to support general-purpose volumetric mesh generation for Isogeometric Analysis (IGA).

 

IGA is a novel computational modeling approach that makes use of smooth spline functions commonly employed in Computer-Aided Design and Computer Graphics to carry out Finite Element Analysis in solid and fluid mechanics, coupled fluid-structure interaction (FSI), and other areas.

 

The ideal candidate would be someone pursuing their MS degree in Computational Science and Engineering or Computer Science. The engagement can be from 20 hours/week all the way to full-time employment. The candidate may work remotely, relocation to the Providence area is not required. The candidate may also use some products of this work to contribute to his/her MS thesis if desired.

 

Duties:
The research engineer will mainly work on the implementation of a GUI for generating volume meshes for IGA using the multiblock Non-Uniform Rational B-Spline (NURBS) technology. The research engineer will also help generate NURBS meshes and carry out computational analyses of solid and fluid mechanics applications using the meshes produced by the meshing tool. The research engineer will interact with the YB Numerics and Rice University team members and their collaborators on the GUI development, NURBS meshing, and numerical simulations.

 

Required Qualifications:
– Bachelor’s degree in Computer Science or Mechanical, Civil or Aerospace (or related)
Engineering
– Experience with Qt GUI programming (Qt OpenGL and WebGL)
– Experience with team programming environments (GitHub/GitLab, CMake, Docker)
– Must be a US Citizen or Permanent Resident

 

Desired Qualifications:
– Master’s degree
– Good knowledge of geometric modeling
– Good knowledge of numerical methods
– Knowledge of fluid and solid mechanics
– Experience with Fortran 90 programming
– Experience with high-performance computing (MPI, OpenMP)


Interested candidates should send an e-mail to ybnumerics@gmail.com with the
subject line “Interested in IGA Meshing Tool and GUI Development”.

Argonne Training Program on Extreme-Scale Computing

By | News, SC2 jobs

The annual Argonne Training Program on Extreme-Scale Computing (ATPESC) is set to take place August 1–13, 2021. The call for applications is now open through March 1, 2021.

 

Apply now for an opportunity to learn the tools and techniques needed to carry out scientific computing research on the world’s most powerful supercomputers. ATPESC participants will be granted access to DOE’s leadership-class systems at the ALCF, OLCF, and NERSC.

The Argonne Training Program on Extreme-Scale Computing (ATPESC) provides intensive, two-week training on the key skills, approaches, and tools to design, implement, and execute computational science and engineering applications on current high-end computing systems and the leadership-class computing systems of the future.

The core of the program will focus on programming methodologies that are effective across a variety of supercomputers and that are expected to be applicable to exascale systems. Additional topics to be covered include computer architectures, mathematical models and numerical algorithms, approaches to building community codes for HPC systems, and methodologies and tools relevant for Big Data applications.

PROGRAM CURRICULUM

Renowned scientists and leading HPC experts will serve as lecturers and guide the hands-on sessions. The core curriculum will cover:

  • Hardware architectures
  • Programming models and languages
  • Data-intensive computing and I/O
  • Visualization and data analysis
  • Numerical algorithms and software for extreme-scale science Performance tools and debuggers
  • Software productivity
  • Machine learning and deep learning tools and methods for science

ELIGIBILITY AND APPLICATION

Doctoral students, postdocs, and computational scientists interested in attending ATPESC can review eligibility and application details on the website.

There are no fees to participate in ATPESC. Domestic airfare, meals, and lodging are also provided. Application deadline: March 1, 2021.

Doctoral students, postdocs, and computational scientists interested in attending ATPESC can review eligibility and application details on the application instructions web page.

The event will be held in the Chicago area. If an in-person meeting is not possible, it will be held as a virtual event.

Note: There are no fees to participate. Domestic airfare, meals, and lodging are provided.

IMPORTANT DATES – ATPESC 2021

  • March 1, 2021 – Deadline to submit applications
  • April 26, 2021 – Notification of acceptance
  • May 3, 2021 – Account application deadline

For more information, contact support@extremecomputingtraining.anl.gov