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U-M prepares for SC18 conference in Dallas

By | General Interest, Happenings, News

University of Michigan researchers and IT staff are gearing up for extensive participation in the Supercomputing ‘18 conference (SC18) in Dallas from Nov. 11-16.

SC “Perennial” Quentin Stout, U-M professor of Electrical Engineering and Computer Science and one of only 19 people who have been to every Supercomputing conference, will once again co-present a tutorial titled Parallel Computing 101.

And with the recent announcement of a new HPC cluster on campus called Great Lakes, IT staff from Advanced Research Computing – Technology Services (ARC-TS) will be making multiple presentations around the conference on the details of the new supercomputer.

U-M will be at exhibit booth 1204A, adjacent to the Michigan State University booth, highlighting our computational and data-intensive research as well as the comprehensive set of tools and services we provide to our researchers. Representatives from all ARC units will be at the booth: ARC-TS, the Michigan Institute for Data Science (MIDAS), the Michigan Institute for Computational Discovery and Engineering (MICDE), and Consulting for Statistics, Computing and Analytics Research (CSCAR).

The booth will also feature two demonstrations: one will focus on the Open Storage Research Infrastructure or OSiRIS, the multi-institutional software-defined data storage system, and the Services Layer At The Edge (SLATE) project, both of which are supported by the NSF; the other will test conference-goers’ ability to detect “fake news” stories compared to an artificial intelligence system created by researchers supported by MIDAS.

U-M Activities

Sunday, Nov. 11

8:30 a.m. – 5 p.m.
Tutorial: Parallel Computing 101: Prof. Stout and Associate Professor Christiane Jablonowski of the U-M Department of Climate and Space Sciences and Engineering will provide a comprehensive overview of parallel computing.

8:30am – noon
Introduction to Kubernetes. Presented by Bob Killen, Research Cloud Administrator, and Scott Paschke, Research Cloud Solutions Designer, both from ARC-TS. Containers have shifted the way applications are packaged and delivered. Their use in data science and machine learning is skyrocketing with the beneficial side effect of enabling reproducible research. This rise in use has necessitated the need to explore and adopt better container-centric orchestration tools. Of these tools, Kubernetes – an open-source container platform born within Google — has become the de facto standard. This half-day tutorial will introduce researchers and sys admins who may already be familiar with container concepts to the architecture and fundamental concepts of Kubernetes. Attendees will explore these concepts through a series of hands-on exercises and leave with the leg-up in continuing their container education, and gain a better understanding of how Kubernetes may be used for research applications.

Tuesday, Nov. 13

Brock Palen, Director of ARC-TS, will speak about the new Great Lakes HPC cluster:

  • DDN booth (3123), 1:30 p.m.
  • Mellanox booth (3207), 3:30 p.m.
  • Dell booth (3218), 4:30 p.m.

11 a.m. – noon
Todd Raeker, Research Technology Consultant for ARC-TS, will be at the Globus booth (4201) to talk about U-M researchers’ use of the service.

12:15pm – 1:15pm
Birds of a Feather: Meeting HPC Container Challenges as a Community. Bob Killen, Research Cloud Administrator at ARC-TS, will give a lightning talk as part of this session that will present, prioritize, and gather input on top issues and budding solutions around containerization of HPC applications.

2 p.m.
Sharon Broude Geva, Director of ARC, will be live on the SC18 News Desk discussing ARC HPC services, Women in HPC, and the Coalition for Scientific Academic Computation (CASC). The stream will be available from the Supercomputing Twitter account: https://twitter.com/Supercomputing

5:15pm – 6:45pm
Birds of a Feather: Ceph Applications in HPC Environments: Ben Meekhof, HPC Storage Administrator at ARC-TS, will give a lightning talk on Ceph and OSiRIS as part of this session. More details at https://www.msi.umn.edu/ceph-hpc-environments-sc18

7 p.m.
ARC is a sponsor of the Women in HPC Reception. See the event description for more details and to register. Sharon Broude Geva, Director of ARC, will give a presentation.

Wednesday, Nov. 14

1 – 2 p.m.
Todd Raeker, Research Technology Consultant for ARC-TS, will be at the Globus booth (4201) to talk about U-M researchers’ use of the service.

1:30 p.m.
Brock Palen, Director of ARC-TS, will speak the SLURM booth (1242) about the new Great Lakes HPC cluster:

5:15 – 6:45 p.m.
Birds of a Feather: Cloud Infrastructure Solutions to Run HPC Workloads: Bob Killen, Research Cloud Administrator at ARC-TS, will present at this session aimed at architects, administrators, software engineers, and scientists interested in designing and deploying cloud infrastructure solutions such as OpenStack, Docker, Charliecloud, Singularity, Kubernetes, and Mesos.

Follow ARC on Twitter at https://twitter.com/ARC_UM for updates.

Beta cluster available for learning Slurm; new scheduler to be part of upcoming cluster updates

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

New HPC resources to replace Flux and updates to Armis are coming.  They will run a new scheduling system (Slurm). You will need to learn the commands in this system and update your batch files to successfully run jobs. Read on to learn the details and how to get training and adapt your files.

In anticipation of these changes, ARC-TS has created the test cluster “Beta,” which will provide a testing environment for the transition to Slurm. Slurm will be used on Great Lakes; the Armis HIPAA-aligned cluster; and a new cluster called “Lighthouse” which will succeed the Flux Operating Environment in early 2019.

Currently, Flux and Armis use the Torque (PBS) resource manager and the Moab scheduling system; when completed, Great Lakes and Lighthouse will use the Slurm scheduler and resource manager, which will enhance the performance and reliability of the new resources. Armis will transition from Torque to Slurm in early 2019.

The Beta test cluster is available to all Flux users, who can login via ssh at ‘beta.arc-ts.umich.edu’. Beta has its own /home directory, so users will need to create or transfer any files they need, via scp/sftp or Globus.

Slurm commands will be needed to submit jobs. For a comparison of Slurm and Torque commands, see our Torque to Slurm migration page. For more information, see the Beta home page.

Support staff from ARC-TS and individual academic units will conduct several in-person and online training sessions to help users become familiar with Slurm. We have been testing Slurm for several months, and believe the performance gains, user communications, and increased reliability will significantly improve the efficiency and effectiveness of the HPC environment at U-M.

The tentative time frame for replacing or transitioning current ARC-TS resources is:

  • Flux to Great Lakes, first half of 2019
  • Armis from Torque to Slurm, January 2019
  • Flux Operating Environment to Lighthouse, first half of 2019
  • Open OnDemand on Beta, which replaces ARC Connect for web-based job submissions, Jupyter Notebooks, Matlab, and additional software packages, fall 2018

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/.

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.

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.

Interdisciplinary Committee on Organizational Studies (ICOS) Big Data Summer Camp, May 14-18

By | Data, Educational, General Interest, Happenings, News
Social and organizational life are increasingly conducted online through electronic media, from emails to Twitter feed to dating sites to GPS phone tracking. The traces these activities leave behind have acquired the (misleading) title of “big data.” Within a few years, a standard part of graduate training in the social sciences will include a hefty dose of “using of big data,” and we will all be utilizing terms like API and Python.
This year ICOS, MIDAS, and ARC are again offering a one-week “big data summer camp” for doctoral students interested in organizational research, with a combination of detailed examples from researchers; hands-on instruction in Python, SQL, and APIs; and group work to apply these ideas to organizational questions.  Enrollment is free, but students must commit to attending all day for each day of camp, and be willing to work in interdisciplinary groups.

The dates of the camp are all day May 14th-18th.

https://ttc.iss.lsa.umich.edu/ttc/sessions/interdisciplinary-committee-on-organizational-studies-icos-big-data-summer-camp-3/ 

U-M launches Data Science Master’s Program

By | Educational, General Interest, Happenings, News

The University of Michigan’s new, interdisciplinary Data Science Master’s Program is taking applications for its first group of students. The program is aimed at teaching participants how to extract useful knowledge from massive datasets using computational and statistical techniques.

The program is a collaboration between the College of Engineering (EECS), the College of Literature Science and the Arts (Statistics), the School of Public Health (Biostatistics), the School of Information, and the Michigan Institute for Data Science.

“We are very excited to be offering this unique collaborative program, which brings together expertise from four key disciplines at the University in a curriculum that is at the forefront of data science,” said HV Jagadish, Bernard A. Galler Collegiate Professor of Electrical Engineering and Computer Science, who chairs the program committee for the program.

“MIDAS was a catalyst in bringing  faculty from multiple disciplines together to work towards the development of this new degree program,”  he added.

MIDAS will provide students in this program with interdisciplinary collaborations, intellectual stimulation, exposure to a broad range of practice, networking opportunities, and space on Central Campus to meet for formal and informal gatherings.

For more information, see the program website at https://lsa.umich.edu/stats/masters_students/mastersprograms/data-science-masters-program.html, and the program guide (PDF) at https://lsa.umich.edu/content/dam/stats-assets/StatsPDF/MSDS-Program-Guide.pdf.

Applications are due March 15.

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.)