U-M selects Dell EMC, Mellanox and DDN to Supply New “Great Lakes” Computing Cluster

By | Flux, General Interest, Happenings, HPC, News

The University of Michigan has selected Dell EMC as lead vendor to supply its new $4.8 million Great Lakes computing cluster, which will serve researchers across campus. Mellanox Technologies will provide networking solutions, and DDN will supply storage hardware.

Great Lakes will be available to the campus community in the first half of 2019, and over time will replace the Flux supercomputer, which serves more than 2,500 active users at U-M for research ranging from aerospace engineering simulations and molecular dynamics modeling to genomics and cell biology to machine learning and artificial intelligence.

Great Lakes will be the first cluster in the world to use the Mellanox HDR 200 gigabit per second InfiniBand networking solution, enabling faster data transfer speeds and increased application performance.

“High-performance research computing is a critical component of the rich computing ecosystem that supports the university’s core mission,” said Ravi Pendse, U-M’s vice president for information technology and chief information officer. “With Great Lakes, researchers in emerging fields like machine learning and precision health will have access to a higher level of computational power. We’re thrilled to be working with Dell EMC, Mellanox, and DDN; the end result will be improved performance, flexibility, and reliability for U-M researchers.”

“Dell EMC is thrilled to collaborate with the University of Michigan and our technology partners to bring this innovative and powerful system to such a strong community of researchers,” said Thierry Pellegrino, vice president, Dell EMC High Performance Computing. “This Great Lakes cluster will offer an exceptional boost in performance, throughput and response to reduce the time needed for U-M researches to make the next big discovery in a range of disciplines from artificial intelligence to genomics and bioscience.”

The main components of the new cluster are:

  • Dell EMC PowerEdge C6420 compute nodes, PowerEdge R640 high memory nodes, and PowerEdge R740 GPU nodes
  • Mellanox HDR 200Gb/s InfiniBand ConnectX-6 adapters, Quantum switches and LinkX cables, and InfiniBand gateway platforms
  • DDN GRIDScaler® 14KX® and 100 TB of usable IME® (Infinite Memory Engine) memory

“HDR 200G InfiniBand provides the highest data speed and smart In-Network Computing acceleration engines, delivering HPC and AI applications with the best performance, scalability and efficiency,” said Gilad Shainer, vice president of marketing at Mellanox Technologies. “We are excited to collaborate with the University of Michigan, Dell EMC and DataDirect Networks, in building a leading HDR 200G InfiniBand-based supercomputer, serving the growing demands of U-M researchers.”

“DDN has a long history of working with Dell EMC and Mellanox to deliver optimized solutions for our customers. We are happy to be a part of the new Great Lakes cluster, supporting its mission of advanced research and computing. Partnering with forward-looking thought leaders as these is always enlightening and enriching,” said Dr. James Coomer, SVP Product Marketing and Benchmarks at DDN.

Great Lakes will provide significant improvement in computing performance over Flux. For example, each compute node will have more cores, higher maximum speed capabilities, and increased memory. The cluster will also have improved internet connectivity and file system performance, as well as NVIDIA Tensor GPU cores, which are very powerful for machine learning compared to prior generations of GPUs.

“Users of Great Lakes will have access to more cores, faster cores, faster memory, faster storage, and a more balanced network,” said Brock Palen, Director of Advanced Research Computing – Technology Services (ARC-TS).

The Flux cluster was created approximately 8 years ago, although many of the individual nodes have been added since then. Great Lakes represents an architectural overhaul that will result in better performance and efficiency. Based on extensive input from faculty and other stakeholders across campus, the new Great Lakes cluster will be designed to deliver similar services and capabilities as Flux, including the ability to accommodate faculty purchases of hardware, access to GPUs and large-memory nodes, and improved support for emerging uses such as machine learning and genomics.

ARC-TS will operate and maintain the cluster once it is built. Allocations of computing resources through ARC-TS include access to hundreds of software titles, as well as support and consulting from professional staff with decades of combined experience in research computing.

Updates on the progress of Great Lakes will be available at https://arc-ts.umich.edu/greatlakes/.

MICDE announces 2018-2019 fellowship recipients

By | Educational, General Interest, Happenings, News

The Michigan Institute for Computational Discovery and Engineering (MICDE) is pleased to announce the 2018-2019 recipients of the MICDE Fellowships for students enrolled in the PhD in Scientific Computing or the Graduate Certificate in Computational Discovery and Engineering. The fellowships, which carry a $4,000 stipend, are meant to augment other sources of funding and are available to students in both programs. See our Fellowship page for more information.

AWARDEES

Zhitong Bai, Mechanical Engineering
Kyle Bushick, Materials Science and Engineering
Geunyeong Byeon, Industrial and Operations Engineering
Sehwan Chung, Civil and Environmental Engineering
Khoi Dang, Chemistry
Sicen Du, Materials Science and Engineering
Joseph Hollowed, Physics
Jia Li, Physics
Sabrina Lynch, Biomedical Engineering
Samar Minallah, Climate and Space Sciences and Engineering
Everardo Olide, Applied Physics
Shaowu Pan, Aerospace Engineering
Alicia Petersen, Climate and Space Sciences and Engineering
Vyas Ramasubramani, Chemical Engineering
Fabricio Vasselai, Political Science
Nathan Vaughn, Applied and Interdisciplinary Mathematics
Blair Winograd, Chemistry
Samuel Young, Chemical Engineering
Kexin Zhang, Chemistry
Bu Zhao, School of Environment and Sustainability

ARC-TS seeks pilot users for two new research storage services

By | General Interest, Happenings, HPC, News

Advanced Research Computing – Technology Services (ARC-TS) is seeking pilot users for two new research storage services.

The first, Locker, is group project storage focused on large data sets, and is available at a cost less than half that of current primary storage services. Locker still provides encryption, replication, snapshots, and workstation access. Example use cases for Locker are research projects in climate studies, genomics, imaging, and other data-intensive sciences.

The second service, Data Den, provides archive class storage for research data that is not actively used. As our lowest cost research storage offering, Data Den provides “cold storage” for massive amounts of data with 20 petabytes of encrypted and replicated capacity. Data Den allows researchers to preserve data between rounds of funding and management plans, and to free up space in more expensive primary storage by moving valuable, but not currently used, data.

Those interested in participating in the pilots should contact ARC-TS at hpc-support@umich.edu.

Eric Michielssen honored for paper describing new algorithm to solve Maxwell’s equations

By | General Interest, Happenings, News

Eric Michielssen, professor of Electrical Engineering and Computer Science, and Associate Vice President for Advanced Research Computing, has won the Sergei A. Schelkunoff Transactions Prize Paper Award for research impacting the ability to rapidly analyze electromagnetic phenomena.

This award is presented to the authors of the best paper published in the IEEE Transactions on Antennas and Propagation during the previous year.

The 2017 paper, “A Butterfly-Based Direct Integral-Equation Solver Using Hierarchical LU Factorization for Analyzing Scattering From Electrically Large Conducting Objects,“ co-authored by Han Guo (ECE doctoral student), Yang Liu (MSE PHD, EE, 2013 2015; Lawrence Berkeley National Lab), and Prof. Jun Hu (UESTC), describes a new algorithm for solving Maxwell’s equations that is orders of magnitude faster than prior algorithms, opening the door to its use for the design and optimization of electromagnetic devices.

For more, see the College of Engineering press release.

Postdoctoral Position at U-M School of Public Health

By | General Interest, SC2 jobs

Postdoctoral Position

University of Michigan School of Public Health
Departments of Epidemiology and Health Management and Policy

Applications are invited for two two-year postdoctoral research positions to join the NIH-funded Center for the Assessment of the Public Health Impact of Tobacco Regulations, with a multidisciplinary and multi-institutional team of collaborators. The project will conduct analyses of the public health impact of tobacco regulations across a range of tobacco-related conditions and policy outcomes. The interdisciplinary team includes epidemiologists, economists, tobacco scientists, applied mathematicians and statisticians (Rafael Meza, David Mendez, Ken Warner, Nancy Fleischer (University of Michigan), David Levy (Georgetown University), Ted Holford (Yale University)).

Postdoc description and desired qualifications

The postdoc will develop and examine simulation models of tobacco use that explicitly consider multiple tobacco-products and multiple disease outcomes.

Desired areas of expertise include: dynamic and complex systems, parameter estimation, computer programming (familiarity with, R, Python, C++, Matlab), statistical analysis, econometrics and epidemiology modeling.

Experience developing mathematical/simulation models to address problems in public health, epidemiology or health outcomes is a plus.

Applicants should have a doctoral degree in Epidemiology, Health Economics, Econometric, Engineering, Applied Mathematics, Mathematics, Statistics, Operations Research or related field.

Compensation

Compensation (salary and benefits) will be offered according to University of Michigan and NIH guidelines.

The position is available immediately but starting date is negotiable. To apply please submit CV, names of references, and inquiries to Dr Rafael Meza at rmeza@umich.edu

The University of Michigan offers a vibrant mathematical modeling and complex systems community. Modeling expertise expands across departments including Epidemiology, Health Management and Policy, Complex Systems, Ecology and Evolutionary Biology, Mathematics and Statistics. The School of Public Health is renowned for its cutting-edge research on the applications of mathematical modeling in epidemiology and public health.

MICDE to provide data analysis and dissemination support for $18 million tobacco research center

By | General Interest, Happenings, News, Research

The University of Michigan School of Public Health will house a new, multi-institutional center focusing on modeling and predicting the impact of tobacco regulation, funded with an $18 million federal grant from the National Institutes of Health and the Food and Drug Administration.

The Center for the Assessment of the Public Health Impact of Tobacco Regulations will be part of the NIH and FDA’s Tobacco Centers of Regulatory Science, the centerpiece of an ongoing partnership formed in 2013 to generate critical research that informs the regulation of tobacco products.

The Michigan Institute for Computational Discovery and Engineering (MICDE) will support the center’s Data Analysis and Dissemination core by collecting national and regional survey data, conducting analysis of the use of tobacco products including vaping and e-cigarettes, and disseminate the resulting tobacco modeling parameters to other research centers and the Food and Drug Administration.

The center is led by MICDE affiliated faculty member Rafael Meza, associate professor of Epidemiology, and David Levy, professor of Oncology at Georgetown University.

For more on the center, see the press release from the U-M School of Public Health: https://sph.umich.edu/news/2018posts/tcors-091718.html

U-M part of new software institute on high-energy physics

By | General Interest, Happenings, News, Research

The University of Michigan is part of an NSF-supported 17-university coalition dedicated to creating next-generation computing power to support high-energy physics research.

Led by Princeton University, the Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP) will focus on developing software and expertise to enable a new era of discovery at the Large Hadron Collider (LHC) at CERN in Geneva, Switzerland.

Shawn McKee, Research Scientist in the U-M Department of Physics, is a co-PI of the institute. His work will focus on integrating and extending the Open Storage Grid networking activities with similar efforts at the LHC.

For more information, see Princeton’s press release, and the NSF’s announcement.

New course for fall 2018: On-Ramp to Data Science for Chemical Engineers

By | Educational, General Interest, Happenings, News

Description: Engineers are encountering and generating a ever-growing body of data and recognizing the utility of applying data science (DataSci) approaches to extract knowledge from that data. A common barrier to learning DataSci is the stack of prerequisite courses that cannot fit into the typical engineering student schedule. This class will remove this barrier by, in one semester, covering essential foundational concepts that are not part of many engineering disciplines’ core curricula. These include: good programming practices, data structures, linear algebra, numerical methods, algorithms, probability, and statistics. The class’s focus will be on how these topics relate to data science and to provide context for further self-study.

Eligibility: College of Engineering students, pending instructor approval.

More information: http://myumi.ch/LzqPq

Instructor: Heather Mayes, Assistant Professor, Chemical Engineering, hbmayes@umich.edu.

University of Michigan awarded Women in High Performance Computing chapter

By | General Interest, News

The University of Michigan has been recognized as one of the first Chapters in the new Women in High Performance Computing (WHPC) Pilot Program.

“The WHPC Chapter Pilot will enable us to reach an ever-increasing community of women, provide these women with the networks that we recognize are essential for them excelling in their career, and retaining them in the workforce.” says Dr. Sharon Broude Geva, WHPC’s Director of Chapters and Director of Advanced Research Computing (ARC) at the University of Michigan (U-M). “At the same time, we envisage that the new Chapters will be able to tailor their activities to the needs of their local community, as we know that there is no ‘one size fits all’ solution to diversity.”

“At WHPC we are delighted to be accepting the University of Michigan as a Chapter under the pilot program, and working with them to build a sustainable solution to diversifying the international HPC landscape” said Dr. Toni Collis, Chair and co-founder of WHPC, and Chief Business Development Officer at Appentra Solutions.

The process of selecting organizations to participate in the program accounted for potential conflicts of interest; Geva did not vote on U-M’s application.

About Women in High Performance Computing (WHPC) and the Chapters and Affiliates Pilot Program

Women in High Performance Computing (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.

WHPC has launched a pilot program for groups to become Affiliates or Chapters. The program will share the knowledge and expertise of WHPC as well as help to tailor activities and develop diversity and inclusion goals suitable to the needs of local HPC communities. During the pilot, WHPC will work with the Chapters and Affiliates to support and promote the work of women in their organizations, develop crucial role models, and assist employers in the recruitment and retention of a diverse and inclusive HPC workforce.

WHPC is stewarded by EPCC at the University of Edinburgh. For more information visit http://www.womeninhpc.org.  

For more information on the U-M chapter, contact Dr. Geva at sgeva@umich.edu.

MICDE awards seven Catalyst Grants

By | General Interest, Happenings, News, Research

The Michigan Institute for Computational Discovery and Engineering has awarded its second round of Catalyst Grants, providing between $80,000 and $90,000 each to seven innovative projects in computational science. The proposals were judged on novelty, likelihood of success at catalyzing larger programs and potential to leverage ARC’s computing resources.

The funded projects are:

Title: Exploring Quantum Embedding Methods for Quantum Computing
Researchers: Emanuel Gull, Physics; Dominika Zgid, Chemistry.
Description: The research team will design quantum embedding algorithms that can be early adopters of quantum computers on development of advanced materials for possible applications in modern batteries, next-generation oxide electronics, or high-temperature superconducting power cables.

Title: Teaching autonomous soft machines to swim
Researchers: Silas Alben, Mathematics; Robert Deegan, Physics; Alex Gorodetsky, Aerospace Engineering
Description: Self-oscillating gels are polymeric materials that change shape, driven by chemical reactions occurring entirely within the gel. The research team will develop a computational and machine learning program to discover how to configure self-oscillating gels so that they undergo deformations that result in swimming. The long term goal is to develop a general framework for controlling autonomous soft machines.

Title: Urban Flood Modeling at “Human Action” Scale: Harnessing the Power of Reduced-Order Approaches and Uncertainty Quantification
Researchers: Valeriy Ivanov, Civil and Environmental Engineering; Nikolaos Katopodes, Civil and Environmental Engineering; Darren McKague Climate and Space Sciences and Engineering; Khachik Sargsyan, Sandia National Labs.
Description: The research team will demonstrate urban flood monitoring and prediction capabilities using NASA Cyclone Global Navigation Satellite System (CYGNSS) data and relying on state-of-the-science uncertainty quantification tools in a proof-of-concept urban flooding problem of high complexity.

Title: Advancing the Computational Frontiers of Solution-Adaptive, Scale-Aware Climate Models
Researchers: Christiane Jablonowski, Climate and Space Sciences and Engineering; Hans Johansen, Lawrence Berkeley National Lab.
Description: Researchers will further develop a 3-D mesh adaptation model for climate modeling, allowing computational resources to be focused on phenomena of interest such as tropical cyclones or other extreme weather events. The project will also introduce data-driven machine learning paradigms into modeling of clouds and precipitation.

Title: Deciphering the meaning of human brain rhythms using novel algorithms and massive, rare datasets
Researchers: Omar Ahmed, Psychology, Neuroscience and Biomedical Engineering
Description: The team will develop a set of algorithms for use on high performance computers to analyze de-identified brain data from patients in order to better understand what electrical oscillations tell us about rapidly changing behavioral and pathological brain states.

Title: Embedded Machine Learning Systems To Sense and Understand Pollinator Behavior
Researchers: Robert Dick, Electrical Engineering and Computer Science; Fernanda Valdovinos Ecology and Evolutionary Biology, Center for Complex Systems; Paul Glaum, Ecology and Evolutionary Biology.
Description: To understand the mechanisms driving the population dynamics of pollinators, the research team will develop technologies for deeply embedded hardware/software learning systems capable of remote, long term, autonomous operation; and will analyze the resulting new data to better understand pollinator activity.

Title: Deep Learning for Phylogenetic Inference
Researchers: Jianzhi Zhang, Ecology and Evolutionary Biology; Yuanfang Guan, Computational Medicine and Bioinformatics.
Description: The research team will use deep neural networks to infer molecular phylogenies and extract phylogenetically useful patterns from amino acid or nucleotide sequences, which will help understand evolutionary mechanisms and build evolutionary models for a variety of analyses.

For more on the Catalyst Grants, see http://micde.umich.edu/catalyst/.