Explore ARCExplore ARC

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.

MIDAS researchers’ papers accepted at ACM KDD data science conference in London

By | General Interest, Happenings, News, Research

Several U-M faculty affiliated with MIDAS will participate in the KDD2018 Conference in London in August. The meeting is held by the Associate for Computing Machinery’s Special Interest Group in Knowledge Discovery and Data Mining (KDD).

U-M researchers had the following papers accepted:

Learning Adversarial Networks for Semi-Supervised Text Classification via Policy Gradient
Yan Li (U-M); Jieping Ye (U-M)

TINET: Learning Invariant Networks via Knowledge Transfer
Chen Luo (Rice University); Zhengzhang Chen (NEC Laboratories America); Lu-An Tang (NEC Laboratories America); Anshumali Shrivastava (Rice University); Zhichun Li (NEC Laboratories America); Haifeng Chen (NEC Laboratories America); Jieping Ye (U-M)

Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts
Jiaqi Ma(U-M); Zhe Zhao (Google); Xinyang Yi (Google); Jilin Chen (Google); Lichan Hong (Google); Ed Chi (Google)

Learning Credible Models
Jiaxuan Wang (U-M); Jeeheh Oh (U-M); Haozhu Wang (U-M); Jenna Wiens (U-M)

Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories
Ian Fox (U-M); Lynn Ang (U-M); Mamta Jaiswal (U-M); Rodica Pop-Busui (U-M); Jenna Wiens (U-M)

ActiveRemediation: The Search for Lead Pipes in Flint, Michigan
Jacob Abernethy (Georgia Institute of Technology); Alex Chojnacki (U-M); Arya Farahi (U-M); Eric Schwartz (U-M); Jared Webb (Brigham Young University)

Career Transitions and Trajectories: A Case Study in Computing
Tara Safavi (U-M); Maryam Davoodi (Purdue University); Danai Koutra (U-M)

In addition, U-M Professor Jieping Ye will present at the event’s Artificial Intelligence in Transportation tutorial, and U-M Assistant Professor Qiaozhu Mei will speak as part of Deep Learning Day.

Cluster and storage maintenance set for Aug. 5-9

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

To accommodate updates to software, hardware, and operating systems, Flux, Armis, ConFlux, Flux Hadoop, and their storage systems (/home and /scratch) will be unavailable starting at 9 a.m. Sunday, August 5th and returning to service on Thursday, August 9th. These updates will improve the performance and stability of ARC-TS services.  We try to encapsulate the required changes into two maintenance periods per year and work to complete these tasks quickly, as we understand the impact of the maintenance on your research.

During this time, the following maintenance tasks are planned:

  • Operating system, compiler, and software updates (All clusters).
  • InfiniBand networking updates (firmware and software) (Flux/Armis/ConFlux)
  • Resource manager and job scheduling software updates (All clusters).
  • Lmod default software version changes (Flux/Armis/ConFlux)
  • Upgrade HPC systems to CUDA 9.X (Flux/Armis/ConFlux)
  • Update software of the Lustre file systems that provide /scratch (Flux)
  • Update Elastic Storage Server (ConFlux)
  • Enable 32-bit file IDs on home and software volumes (Flux/Armis)
  • Network switch maintenance (Turbo)

For Flux and Armis HPC jobs, you can use the command “maxwalltime” to discover the amount of time remaining until the beginning of the maintenance. Jobs requesting more walltime than remains before the maintenance will be queued and started after the maintenance is completed.

All Flux, Armis, ConFlux, and Flux Hadoop filesystems will be unavailable during the maintenance. We encourage you to copy any data that might be needed during that time from Flux prior to the start of the maintenance.

Turbo storage will be unavailable starting at 6 a.m Monday, August 6th and will return to service at 10 a.m.

We will post status updates on our Twitter feed ( https://twitter.com/arcts_um ) throughout the course of the maintenance and send an email to all HPC and Hadoop users when the maintenance has been completed.  Updates will also be compiled at http://arc-ts.umich.edu/summer-2018-maintenance/. Please contact hpc-support@umich.edu if you have any questions.

 

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.

Singularity updated on the Flux and Armis clusters

By | General Interest, Happenings, News

The latest stable version of Singularity has been deployed on the ARC-TS clusters.

Singularity version 2.5.0 release includes fixes for several high and medium severity security issues. Full release notes can be found at the Singularity github project page.

We have also added default mount bindings that will map in the /home and /scratch directories into any Singularity container when it’s run on the cluster.

For information on how to use Singularity on the ARC-TS clusters see http://arc-ts.umich.edu/software/singularity/

ARC-TS joins Cloud Native Computing Foundation

By | General Interest, Happenings, News

Advanced Research Computing – Technology Services (ARC-TS) at the University of Michigan has become the first U.S. academic institution to join the Cloud Native Computing Foundation (CNCF), a foundation that advances the development and use of cloud native applications and services. Founded in 2015, CNCF is part of the Linux Foundation.

CNCF announced ARC-TS’s membership at the KubeCon and CloudNativeCon event in Copenhagen. A video of the opening remarks by CNCF Executive Director Dan Kohn can be viewed on the event website.

“Our membership in the CNCF signals our commitment to bringing cloud computing and containers technology to researchers across campus,” said Brock Palen, Director of ARC-TS. “Kubernetes and other CNCF platforms are becoming crucial tools for advanced machine learning, pipelining, and other research methods. We also look forward to bring an academic perspective to the foundation.”

ARC-TS’s membership and participation in the group signals its adoption and commitment to cloud-native technologies and practices. Users of containers and other CNCF services will have access to experts in the field.

Membership gives the U-M research community input into in the continuing development of cloud-native applications, and within CNCF-managed and ancillary projects. U-M is the second academic institution to join the foundation, and the only one in the U.S.

Yottabyte Research Cloud certified for CUI data

By | Data, General Interest, Happenings, News

Advanced Research Computing – Technology Services (ARC-TS) is pleased to announce that the Yottabyte Research Cloud (YBRC) computing platform is now certified to accept data designated as Controlled Unclassified Information (CUI). This includes certification for YBRC and its associated services, enabling secure data analysis on Windows and Linux virtual desktops as well as secure hosting of databases and data ingestion.

For more information on CUI, see the U-M Research Ethics and Compliance CUI webpage and Sensitive Data Guide: Controlled Unclassified Information (CUI). CUI regulations apply to federal non-classified information requiring security controls; an example of CUI data often used in research is data from the Centers for Medicare and Medicaid Services.

The new capability ensures the security of CUI data through the creation of firewalled network enclaves, allowing CUI data to be analyzed safely and securely in YBRC’s flexible, robust and scalable environment.  Within each network enclave, researchers have access to Windows and Linux virtual desktops that can contain any software required for their analysis pipeline.

This capability also extends to our database and ingestion services:

  • Structured databases:  MySQL/MariaDB, and PostgreSQL.
  • Unstructured databases: Cassandra, MongoDB, InfluxDB, Grafana, and ElasticSearch.
  • Data ingestion: Redis, Kafka, RabbitMQ.
  • Data processing: Apache Flink, Apache Storm, Node.js and Apache NiFi.
  • Other data services are available upon request.

The CUI certification extends YBRC’s existing capabilities for handling sensitive data; the service can also take HIPAA data, Export Controlled REsearch (ITAR, EAR), Personally Identifiable Information, and more. Please see Sensitive Data Guide: Yottabyte Research Cloud for more information.

YBRC is supported by U-M’s Data Science Initiative launched in 2015 and was created through a partnership between Yottabyte and ARC-TS. These tools are offered to all researchers at the University of Michigan free of charge, provided that certain usage limits are not exceeded. Large-scale users who outgrow the no-cost allotment may purchase additional YBRC resources. All interested parties should contact hpc-support@umich.edu.

ARC-TS continues to expand Machine Learning and GPU capability

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

Advanced Research Computing – Technology Services (ARC-TS) is pleased to announce the addition of 12 new NVIDIA TITANV Volta class GPUs to our Flux HPC computing cluster.

The new GPUs are spread across three nodes with four cards each. Each card has 12GB of memory, and over 5,100 CUDA cores. These cards bring the new NVIDIA “tensor core” to over 100 Teraflops, which will benefit certain types of machine learning jobs. The new cards will also provide the highest single and double precision performance of any GPU offered on Flux.

The new GPUs will augment our existing K40 and other GPUs, bringing the total GPU count on Flux and Armis to over 50 cards available to the U-M research community. Users of FluxG can access the new TITANV GPUs using the example on our our website or if you have any question, please contact us at hpc-support@umich.edu.

MIDAS Data Science for Music Challenge Initiative announces funded projects

By | Data, General Interest, Happenings, News, Research

From digital analysis of Bach sonatas to mining data from crowdsourced compositions, researchers at the University of Michigan are using modern big data techniques to transform how we understand, create and interact with music.

Four U-M research teams will receive support for projects that apply data science tools like machine learning and data mining to the study of music theory, performance, social media-based music making, and the connection between words and music. The funding is provided under the Data Science for Music Challenge Initiative through the Michigan Institute for Data Science (MIDAS).

“MIDAS is excited to catalyze innovative, interdisciplinary research at the intersection of data science and music,” said Alfred Hero, co-director of MIDAS and the John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science. “The four proposals selected will apply and demonstrate some of the most powerful state-of-the-art machine learning and data mining methods to empirical music theory, automated musical accompaniment of text and data-driven analysis of music performance.”

Jason Corey, associate dean for graduate studies and research at the School of Music, Theatre & Dance, added: “These new collaborations between our music faculty and engineers, mathematicians and computer scientists will help broaden and deepen our understanding of the complexities of music composition and performance.”

The four projects represent the beginning of MIDAS’ support for the emerging Data Science for Music research. The long-term goal is to build a critical mass of interdisciplinary researchers for sustained development of this research area, which demonstrates the power of data science to transform traditional research disciplines.

Each project will receive $75,000 over a year. The projects are:

Understanding and Mining Patterns of Audience Engagement and Creative Collaboration in Large-Scale Crowdsourced Music Performances

Investigators: Danai Koutra and Walter Lasecki, both assistant professors of computer science and engineering

Summary: The project will develop a platform for crowdsourced music making and performance, and use data mining techniques to discover patterns in audience engagement and participation. The results can be applied to other interactive settings as well, including developing new educational tools.

Understanding How the Brain Processes Music Through the Bach Trio Sonatas
Investigators: Daniel Forger, professor of mathematics and computational medicine and bioinformatics; James Kibbie, professor and chair of organ and university organist

Summary: The project will develop and analyze a library of digitized performances of Bach’s Trio Sonatas, applying novel algorithms to study the music structure from a data science perspective. The team’s analysis will compare different performances to determine features that make performances artistic, as well as the common mistakes performers make. Findings will be integrated into courses both on organ performance and on data science.

The Sound of Text
Investigators: Rada Mihalcea, professor of electrical engineering and computer science; Anıl Çamcı, assistant professor of performing arts technology

Summary: The project will develop a data science framework that will connect language and music, developing tools that can produce musical interpretations of texts based on content and emotion. The resulting tool will be able to translate any text—poetry, prose, or even research papers—into music.

A Computational Study of Patterned Melodic Structures Across Musical Cultures
Investigators: Somangshu Mukherji, assistant professor of music theory; Xuanlong Nguyen, associate professor of statistics

Summary: This project will combine music theory and computational analysis to compare the melodies of music across six cultures—including Indian and Irish songs, as well as Bach and Mozart—to identify commonalities in how music is structured cross-culturally.

The Data Science for Music program is the fifth challenge initiative funded by MIDAS to promote innovation in data science and cross-disciplinary collaboration, while building on existing expertise of U-M researchers. The other four are focused on transportation, health sciences, social sciences and learning analytics.

Hero said the confluence of music and data science was a natural extension.

“The University of Michigan’s combined strengths in data science methodology and music makes us an ideal crucible for discovery and innovation at this intersection,” he said.

Contact: Dan Meisler, Communications Manager, Advanced Research Computing
734-764-7414, dmeisler@umich.edu