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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/.

CASC image competition open for submissions

By | General Interest, Happenings, News

The image competition for the Coalition for Academic Scientific Computation (CASC) 2019 annual brochure is now open. Winning images will be featured in the brochure, which is distributed to industry, government and academia. An image from U-M Aerospace Engineering Professor Joaquim Martins was on the cover of the 2016 edition, and several U-M investigators have had their work featured in the brochure in other years.

Images will be judged on the following criteria:

  • Illustrative of research underway at the center submitting the proposed images
  • Focus on research that offers a broad representation of what CASC members have undertaken
  • Timeliness of visualization relative to events currently in the news
  • Exhibits intellectual merit
  • Provides scientific, cultural, economic impact
  • Compelling, visually interesting, lively, colorful images in a  high-resolution format

Please send potential submissions to Dan Meisler, ARC Communications Manager, at dmeisler@umich.edu. The deadline is June 11, 2018.

ARC-TS begins work on new “Great Lakes” cluster to replace Flux

By | Flux, Happenings, HPC, News

Advanced Research Computing – Technology Services (ARC-TS) is starting the process of creating a new, campus-wide computing cluster, “Great Lakes,” that will serve the broad needs of researchers across the University. Over time, Great Lakes will replace Flux, the shared research computing cluster that currently serves over 300 research projects and 2,500 active users.

“Researchers will see improved performance, flexibility and reliability associated with newly purchased hardware, as well as changes in policies that will result in greater efficiencies and ease of use,” said Brock Palen, director of ARC-TS.

The Great Lakes cluster will be available to all researchers on campus for simulation, modeling, machine learning, data science, genomics, and more. The platform will provide a balanced combination of computing power, I/O performance, storage capability, and accelerators.

ARC-TS is in the process of procuring the cluster. Only minimal interruption to ongoing research is expected. A “Beta” cluster will be available to help researchers learn the new system before Great Lakes is deployed in the first half of 2019.

The Flux cluster is approximately 8 years old, although many of the individual nodes are newer. One of the benefits of replacing the cluster is to create a more homogeneous platform.

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. The cluster will consist of approximately 20,000 cores.

For more information, contact hpc-support@umich.edu, and see arc-ts.umich.edu/systems-services/greatlakes, where updates to the project will be posted.

Eric Parish, Aero Ph.D student, wins Von Neumann Fellowship from Sandia National Labs

By | Happenings, News, Research

Eric Parish

Eric Parish, who will graduate this spring with a Ph.D in Aerospace Engineering, is the 2018 recipient of the prestigious John von Neumann Postdoctoral Research Fellowship from Sandia National Laboratories (SNL). The highly competitive fellowship offers the opportunity to establish his own program at SNL to conduct innovative research in computational mathematics and scientific computing on advanced computing architectures.

Parish came to U-M from the University of Wyoming, and has developed innovative methodologies of computational math and physics with Prof. Karthik Duraisamy.

Parish said two of his accomplishments in his doctoral work have been developing data-driven solutions to computational physics problems using the NSF-funded ConFlux computing cluster, and bringing together ideas from statistical mechanics to develop efficient numerical solutions of complex partial differential equations.

“It was bridging a gap between communities,” he said of the latter effort.

“Eric came up with a particularly clever way of generalizing concepts from physics to develop a foundation to solve complex equations at a low cost in a mathematically rigorous fashion,” Duraisamy said. “He is one of the rare students who commands an exceptional grasp of applied mathematics, computing and physics, while being well-rounded in his organizational and communication skills. It has been a pleasure and a privilege to work with him.”

Parish said this research could eventually help usher the next generation of flight, for example, “hypersonic” aircraft that can travel at speeds of Mach 8-10. To help get there, his work moves the field toward a better understanding of the underlying physical phenomena via accurate numerical simulations.

At Sandia’s labs in Livermore, Calif., Parish said he plans to continue the work he started at U-M to develop “reduced order models”, which can process past simulation data to greatly reduce the computational cost of future simulations.

Parish said that conducting research at U-M, with the availability of high performance computing resources and a community of computational scientists to bounce ideas off of, helped push his research to a higher level.

“Within Aero, there are five or six very strong computational groups, which really helps me understand the fundamental aspects of what we’re doing, and what the addition of my small little delta means,” he said. “It’s very exciting to do computational research in that environment; it motivates me to come up with better code.”

In 2016, Parish received a $4,000 fellowship from the Michigan Institute for Computational Discovery and Engineering (MICDE). He used some of the funds to attend the International Workshop on Variational Multiscale Methods in Spain last year, where he met a few dozen people from around the world working on similar problems.

“It was fantastic to network and learn from them,” he said.

Parish grew up in Laramie, Wyo., before attending the University of Wyoming, where he played Division 1 golf. He said there was a small but active computational science community at U-W.

“For its size, there was a lot of good computational stuff there,” he said, adding that 10 years ago he would never have predicted the current direction of his career.

Golf played a significant role in his development as well, Parish said: “Being a successful student-athlete takes an extraordinary amount of work. The successes and failures I had … played an integral part in the development of my work ethic, time management skills, mental attitude, and overall growth as a person…I believe that the experience I gained as a student-athlete gave me a unique perspective and skill set that I was able to use to my advantage.”

As far as his future goes after Sandia, Parish said he plans to either continue in the national lab environment or to explore faculty positions so that he can teach and motivate students as his professors at Wyoming and Michigan did for him.

“I’m grateful for everyone’s help,” he said. “The doors that Michigan can open and the quality of people here are very apparent.”

A simulation of magnetohydrodynamic turbulence done on the ConFlux cluster with roughly 1 billion degree of freedom computation generating about 4TB of data.

Winning posters announced for MICDE 2018 Symposium

By | Events, General Interest, Happenings, News

Approximately 50 posters from post-docs and graduate students across campus entered the Poster Competition at the 2018 MICDE Symposium on March 22, 2018. We’re proud to announce the winners:

  • First Place ($500): “Modeling and Enhanced Sampling of Protein-Protein Recognition,” Yanmin Wang, Chemistry
  • Second Place ($300): “Non-Newtonian Computational Model of Thrombosis Initiation,” Sabrina Lynch, Biomedical Engineering
  • Third Place ($200): “Computational Modeling of Particle-Laden Flows,” Gregory Shallcross, Sarah Beetham, and Yuan Yao, Mechanical Engineering
  • Honorable Mention:UM/LISA: Efficient Linear and Nonlinear Guided Wave Simulation,” Hui Zhang, Aerospace Engineering
  • Honorable Mention:Temperature-Dependent Green’s Function Methods for Electronic Structure Calculations,” Alicia Welden, Chemistry
  • Honorable Mention:Non-invasive Diagnostics of Coronary Artery Disease using Machine Learning and Computational Fluid Dynamics,” Kritika Iyer, Biomedical Engineering
  • Honorable Mention:Automated Diagnosis and Prognosis System for Traumatic Brain Injury Patients with Subdural Hematoma,” Negar Farzaneh

The 2018 MICDE Symposium: Summary by Bradley Dice, Ph.D student in Physics and Computational Science

By | Uncategorized

This piece was first published in LinkedIn by Bradley Dice, U-M Ph.D student in Physics and Computational Science.

MICDE Symposium 2018: Computation, A Pillar of Science and a Lens to the Future

High-performance computing (HPC) is becoming an increasingly powerful tool in the hands of scientists, driving new discoveries in physical sciences, life sciences, and social sciences. The development of new (frequently domain-specific) approaches to machine learning and faster, smarter processing of sets of Big Data allows us to explore questions that were previously impossible to study. Yesterday, I presented a poster at the Michigan Institute for Computational Discovery & Engineering (MICDE) annual Symposium and attended a number of talks by researchers working at the intersection of high-performance computing and their domain science. The theme for the symposium was “Computation: A Pillar of Science and a Lens to the Future.”

Collaborative Computational Science with signac

My scientific work, and the work of my colleagues in the Glotzer lab, has been made vastly more efficient through the use of tools for collaborative science, particularly the signac framework. I presented a poster about how the signac framework (composed of open-source Python packages signacsignac-flow, and signac-dashboard) enables scientists to rapidly simulate, model, and analyze data. The name comes from painter Paul Signac, who, along with Georges Seurat, founded the style of pointillism. This neo-impressionist style uses tiny dots of color instead of long brushstrokes, which collectively form a beautiful image when the viewer steps back. This metaphor fits the way that a lot of science works: given only points of data, scientists aim to see the whole picture and tell its story. Since our lab studies materials, our “points” of data fit into a multidimensional parameter space, where quantities like pressure and temperature, or even particles’ shapes, may vary. Using this data, our lab computationally designs novel materials from nanoparticles and studies the physics of complex crystalline structures.

The core signac package, which acts as a database on top of the file system, helps organize and manage scientific data and metadata. Its companion tool signac-flow enables users to quickly define “workflows” that run on supercomputing clusters, determining what operations to perform and submitting the jobs to the cluster for processing. Finally, signac-dashboard (which I develop) provides a web-based data visualization interface that allows users to quickly scan for interesting results and answer scientific questions. These tools include tutorials and documentation, to help users acquaint themselves and get on to doing science as quickly as possible. Importantly, the tools are not specific to materials science. Many scientific fields have similar questions, and the toolkit can easily be applied in fields where exploration or optimization within parameter spaces are common, ranging from fluid mechanics to machine learning.

During the symposium, I learned a lot about how others are using scientific computing in their own work. The symposium speakers came from a wide range of fields, including biology, mathematics, and fluid dynamics. Some of my favorite talks are described below.

The Past: Phylogeny and Uncovering Life’s Origins

High-performance computing is enabling scientists to look in all sorts of directions, including into the past. Stephen Smith, Assistant Professor of Ecology and Evolutionary Biology at the University of Michigan, talked about his lab’s research in detecting evolutionary patterns using genomic data. From the wealth of genetic data that scientists have collected, the Smith lab aims to improve our understanding of the “tree of life”: the overarching phylogenetic tree that can explain the progress of speciation over time. Projects like Open Tree of Life and PHLAWD, an open-source C++ project to process data from the National Center for Biotechnology Information’s GenBank data source, are just two of the ways that open science and big data are informing our understanding of life itself.

The Present: From Algebra to Autonomy

Cleve Moler, the original author of the MATLAB language and chief mathematician, chairman, and cofounder of MathWorks, spoke about his career and how the tools MATLAB has provided for numerical linear algebra (and many other computational tasks) have been important for the development of science and engineering over the last 34 years. MATLAB is taught to STEM students in many undergraduate curricula, and is used widely across industry to simulate and model the behavior of real systems. Features like the Automated System Driving Toolbox are poised to play a role in autonomous vehicles and the difficult computational tasks inherent in their operation.

The Future: Parallel-in-Time Predictions and Meteorology

A significant challenge in weather and climate modeling is that supercomputer architectures are highly parallel, while many simulations of fluids are inherently serial: each timestep must be computed before the next timestep can begin. Beth Wingate, Professor of Mathematics at the University of Exeter and published poet, is developing a powerful approach that may change the way that such models work. Called “parallel-in-time,” it separates the effects of slow dynamics and fast dynamics, enabling parallel architectures to take advantage of longer timesteps and separate the work across many processors.

Conclusions

Computational science is growing rapidly, improving our ability to address the most pressing questions and the mysteries of our world. As new supercomputing resources come online, such as Oak Ridge National Laboratories’ Summit, the promise of exascale computing is coming ever closer to reality. I look forward to what the next year of HPC will bring to our world.

ConFlux cluster expands

By | General Interest, Happenings, HPC, News

ARC-TS has installed 15 new compute nodes into the ConFlux cluster. These nodes have the same 20 cores CPU as the original set, but with 256 GB of RAM instead of 128 GB. Neither the original nodes nor the newly added ones contain any GPUs

As a result, jobs should spend less time in queue, and users can be more liberal in their memory requirements.

HPC training workshops begin Tuesday, Feb. 13

By | Educational, Events, General Interest, Happenings, HPC, News

series of training workshops in high performance computing will be held Feb. 12 through March 6, 2018, presented by CSCAR in conjunction with Advanced Research Computing – Technology Services (ARC-TS).

Introduction to the Linux command Line
This course will familiarize the student with the basics of accessing and interacting with Linux computers using the GNU/Linux operating system’s Bash shell, also known as the “command line.”
Location: East Hall, Room B254, 530 Church St.
Dates: (Please sign up for only one)
• Tuesday, Feb. 13, 1 – 4 p.m. (full descriptionregistration)
• Friday, Feb. 16, 9 a.m. – noon (full description | registration)

Introduction to the Flux cluster and batch computing
This workshop will provide a brief overview of the components of the Flux cluster, including the resource manager and scheduler, and will offer students hands-on experience.
Location: East Hall, Room B254, 530 Church St.
Dates: (Please sign up for only one)
• Monday, Feb. 19, 1 – 4 p.m. (full description | registration)
• Tuesday, March 6, 1 – 4 p.m. (full description | registration)

Advanced batch computing on the Flux cluster
This course will cover advanced areas of cluster computing on the Flux cluster, including common parallel programming models, dependent and array scheduling, and a brief introduction to scientific computing with Python, among other topics.
Location: East Hall, Room B250, 530 Church St.
Dates: (Please sign up for only one)
• Wednesday, Feb. 21, 1 – 5 p.m. (full description | registration)
• Friday, Feb. 23, 1 – 5 p.m. (full description | registration)

Hadoop and Spark workshop
Learn how to process large amounts (up to terabytes) of data using SQL and/or simple programming models available in Python, R, Scala, and Java.
Location: East Hall, Room B250, 530 Church St.
Dates: (Please sign up for only one)
• Thursday, Feb. 22, 1 – 5 p.m. (full description | registration)

U-M fosters thriving artificial intelligence and machine learning research

By | General Interest, HPC, News, Research

Research using machine learning and artificial intelligence — tools that allow computers to learn about and predict outcomes from massive datasets — has been booming at the University of Michigan. The potential societal benefits being explored on campus are numerous, from on-demand transportation systems to self-driving vehicles to individualized medical treatments to improved battery capabilities.

The ability of computers and machines generally to learn from their environments is having transformative effects on a host of industries — including finance, healthcare, manufacturing, and transportation — and could have an economic impact of $15 trillion globally according to one estimate.

But as these methods become more accurate and refined, and as the datasets needed become bigger and bigger, keeping up with the latest developments and identifying and securing the necessary resources — whether that means computing power, data storage services, or software development — can be complicated and time-consuming. And that’s not to mention complying with privacy regulations when medical data is involved.

“Machine learning tools have gotten a lot better in the last 10 years,” said Matthew Johnson-Roberson, Assistant Professor of Engineering in the Department of Naval Architecture & Marine Engineering and the Department of Electrical Engineering and Computer Science. “The field is changing now at such a rapid pace compared to what it used to be. It takes a lot of time and energy to stay current.”

Diagram representing the knowledge graph of an artificial intelligence system, courtesy of Jason Mars, assistant professor, Electrical Engineering and Computer Science, U-M

Johnson-Roberson’s research is focused on getting computers and robots to better recognize and adapt to the world, whether in driverless cars or deep-sea mapping robots.

“The goal in general is to enable robots to operate in more challenging environments with high levels of reliability,” he said.

Johnson-Roberson said that U-M has many of the crucial ingredients for success in this area — a deep pool of talented researchers across many disciplines ready to collaborate, flexible and personalized support, and the availability of computing resources that can handle storing the large datasets and heavy computing load necessary for machine learning.

“The people is one of the reasons I came here,” he said. “There’s a broad and diverse set of talented researchers across the university, and I can interface with lots of other domains, whether it’s the environment, health care, transportation or energy.”

“Access to high powered computing is critical for the computing-intensive tasks, and being able to leverage that is important,” he continued. “One of the challenges is the data. A major driver in machine learning is data, and as the datasets get more and more voluminous, so does the storage needs.”

Yuekai Sun, an assistant professor in the Statistics Department, develops algorithms and other computational tools to help researchers analyze large datasets, for example, in natural language processing. He agreed that being able to work with scientists from many different disciplines is crucial to his research.

“I certainly find the size of Michigan and the inherent diversity that comes with it very stimulating,” he said. “Having people around who are actually working in these application areas helps guide the direction and the questions that you ask.”

Sun is also working on analyzing the potential discriminatory effects of algorithms used in decisions like whether to give someone a loan or to grant prisoners parole.

“If you use machine learning, how do you hold an algorithm or the people who apply it accountable? What does it mean for an algorithm to be fair?” he said. “Can you check whether this notion of non-discrimination is satisfied?”

Jason Mars, an assistant professor in the Electrical Engineering and Computer Science department and co-founder of a successful spinoff called Clinc, is applying artificial intelligence to driverless car technology and a mobile banking app that has been adopted by several large financial institutions. The app, called Finie, provides a much more conversational interface between users and their financial information than other apps in the field.

“There is going to be an expansion of the number of problems solved and number of contributions that are AI-based,” Mars said. He predicted that more researchers at U-M will begin exploring AI and ML as they understand the potential.

“It’s going to require having the right partner, the right experts, the right infrastructure, and the best practices of how to use them,” he said.

He added that U-M does a “phenomenal job” in supporting researchers conducting AI and ML research.

“The level of support and service is awesome here,” he said. “Not to mention that the infrastructure is state of the art. We stay relevant to the best techniques and practices out there.”

Advanced Research Computing at U-M, in part through resources from the university-wide Data Science Initiative, provides computing infrastructure, consulting expertise, and support for interdisciplinary research projects to help scientists conducting artificial intelligence and machine learning research.

For example, Consulting for Statistics, Computing and Analytics Research, an ARC unit, has several consultants on staff with expertise in machine learning and predictive analysis with large, complex, and heterogeneous data. CSCAR recently expanded capacity to support very large-scale machine learning using tools such as Google’s TensorFlow.

CSCAR consultants are available by appointment or on a drop-in basis, free of charge. See cscar.research.umich.edu or email cscar@umich.edu for more information.

CSCAR also provides workshops on topics in machine learning and other areas of data science, including sessions on Machine Learning in Python, and an upcoming workshop in March titled “Machine Learning, Concepts and Applications.”

The computing resources available to machine learning and artificial intelligence researchers are significant and diverse. Along with the campus-wide high performance computing cluster known as Flux, the recently announced Big Data cluster Cavium ThunderX will give researchers a powerful new platform for hosting artificial intelligence and machine learning work. Both clusters are provided by Advanced Research Computing – Technology Services (ARC-TS).

All allocations on ARC-TS clusters include access to software packages that support AI/ML research, including TensorFlow, Torch, and Spark ML, among others.

ARC-TS also operates the Yottabyte Research Cloud (YBRC), a customizable computing platform that recently gained the capacity to host and analyze data governed by the HIPAA federal privacy law.

Also, the Michigan Institute for Data Science (MIDAS) (also a unit of ARC) has supported several AI/ML projects through its Challenge Initiative program, which has awarded more than $10 million in research support since 2015.

For example, the Analytics for Learners as People project is using sensor-based machine learning tools to translate data on academic performance, social media, and survey data into attributes that will form student profiles. Those profiles will help link academic performance and mental health with the personal attributes of students, including values, beliefs, interests, behaviors, background, and emotional state.

Another example is the Reinventing Public Urban Transportation and Mobility project, which is using predictive models based on machine learning to develop on-demand, multi-modal transportation systems for urban areas.

In addition, MIDAS supports student groups involved in this type of research such as the Michigan Student Artificial Intelligence Lab (MSAIL) and the Michigan Data Science Team (MDST).

(A version of this piece appeared in the University Record.)