News

Redesigned ARC websites to launch mid-April

Redesigned versions of the Advanced Research Computing websites will be launched in mid-April. The redesign includes the sites for ARC, Advanced Research Computing – Technology Services (ARC-TS), Consulting for Statistics, Computing and Analytics Research (CSCAR), the Michigan Institute for Computational Discovery and Engineering (MICDE) and the Michigan Institute for Data Science (MIDAS).

The goals are to enhance sharing of information between ARC units, and to make navigation easier. The URLs will not change, and users will be redirected from frequently used existing pages to the new versions.

The target date for launch is April 18, 2016. Please contact Dan Meisler, ARC Communications Manager, at dmeisler@umich.edu with any questions.

White papers due for MIDAS Challenge Initiatives June 30

The second round of the Michigan Institute for Data Science (MIDAS) Challenge Initiatives is open, with the deadline for initial funding submissions on June 30.

MIDAS is seeking proposals in Data Science for Health Science (download RFP) and Social Science (download RFP).

Proposals will be funded at a level of approximately $1.25M each. A successful research proposal will involve a multi-disciplinary team engaged in research that will both have disruptive impact on a relevant thrust application and significantly advance the methodological foundations of data science. The ultimate intent of the MIDAS challenge initiatives is to stimulate research activities that can be leveraged into successful external funding proposals from government, private foundations, or industry.

For more information, visit the Challenge Initiative RFP page.

Software Carpentry workshop at U-M — May 2-3

A Software Carpentry workshop will be held at the U-M Medical School May 2 and 3. These workshops are free and open to anyone on campus; the sessions are suitable for researchers in the humanities and social sciences. Register here.

This hands-on workshop will cover basic concepts and tools, including program design, version control, data management, and task automation. Participants will be encouraged to help one another and to apply what they have learned to their own research problems.

Who: The course is aimed at graduate students, postdocs, and other researchers across the University of Michigan. You don’t need to have any previous knowledge of the tools that will be presented at the workshop.

Where: Furstenberg 2710 (2nd floor of Med Sci II).

U-M software package HOOMD-blue chosen as benchmark for new GPU performance by NVIDIA

HOOMD-blue, a University of Michigan-produced software package for particle simulation, was chosen as one of seven benchmark applications to demonstrate the speed of NVIDIA’s new Tesla P100 GPU.

HOOMD-blue was developed by Prof. Sharon Glotzer’s research group. It lets users define particle initial conditions and interactions in a high-level Python script. Python job scripts provide the flexibility to create custom initialization routines, control simulation parameters, and perform in situ analysis.

Webinars title “Introduction to HOOMD-blue” and “Using HOOMD-blue for Polymer Simulations and Big Systems” are available for viewing.

 

Unused cycles on Flux HPC cluster now available to U-M undergraduates at no cost

Undergraduates working on research that requires high performance computing resources can now use the Flux HPC cluster at no cost.

Flux is the shared computing cluster available across campus, operated by Advanced Research Computing – Technology Services (ARC-TS). Under ARC-TS’s new Flux for Undergraduates program, student groups and individuals with faculty sponsors can access unused computing cycles on Flux for free.

The first student group to take advantage of this program is the Michigan Data Science Team, which was created in Fall 2015 with the goal of helping U-M students enter Big Data competitions. The team enters competitions through sites like Kaggle, and is one of the first such teams affiliated with a university.

The group’s organizer, Jonathan Stroud, a Computer Science and Engineering graduate student, said team members were maxing out the capabilities of their laptops when they first started.

“For the first couple of competitions, we made sure we picked a problem that people could do on their laptops,” Stroud said. “Still, every night before bed, they would set up their experiments and they ran all night.”

L-R: Anthony Kremin, Ben Bray, Wei Lee, Curtis Fenner, Jimmy Hsu, Alex Chojnacki, Alexander Zaitzeff, Jonathan Stroud, Jared Webb, Tianpei Xie, Helena Zeng, Xiang Li, Xinyu Tan, Jianming Sang, Guangsha Shi

L-R: Anthony Kremin, Ben Bray, Wei Lee, Curtis Fenner, Jimmy Hsu, Alex Chojnacki, Alexander Zaitzeff, Jonathan Stroud, Jared Webb, Tianpei Xie, Helena Zeng, Xiang Li, Xinyu Tan, Jianming Sang, Guangsha Shi

He said success in the data science competitions typically depends on trying several approaches simultaneously, which can be taxing on computing resources. Stroud said the team typically uses software such as Python, R, and Matlab. Team members come from a wide range of disciplines, including Engineering, Applied Math, Physics, and one from the Music School, Stroud said.

Jacob Abernethy, assistant professor of Electrical Engineering and Computer Science, is the group’s faculty advisor. He wrote some funding for the group into his NSF CAREER proposal that was awarded in 2015. He said after the group’s first competition, he surveyed the students as to what worked and what didn’t. He said one of the clearest responses was the need for more robust computing resources.

“Our top two competitors talked about maxing out the resources on not only their own laptop, but also on the clusters provided them by their advisors,” Abernethy said. “It became clear that we needed to talk about Flux.”

He said a key method to the machine learning and data science experimentation process is the use of cross-validation, that is, testing the performance of a set of parameters on several subsets of data simultaneously. “This leads to a very obvious need for a distributed system in which we can execute a large number of ‘embarrassingly parallel’ tasks quickly,” Abernethy said.

Being able to use Flux “has been helping us a lot,” Stroud added. “We’ve been contacted by other schools to see how they can do the same thing.”

Jobs submitted under Flux For Undergraduates will run only when unused cycles are available and will be requeued when those resources are needed by standard Flux jobs. To be most efficient, student groups should use short or checkpointed jobs to take advantage of these available cycles.

Student groups can also purchase Flux allocations for jobs that are higher priority or time constrained; those allocations can also work in conjunction with the free Flux for Undergraduates jobs.

“The goal is to provide undergraduates with experience in high performance computing, and access to computational resources for their projects,” said Brock Palen, Associate Director of ARC-TS.

Undergraduate groups and individuals must have sponsorship from a faculty member. To request resources through Flux for Undergraduates, please fill out this form. An abstract of the intended activity must be submitted.

Questions can be directed to arc-contact@umich.edu.

U-M plays leading role in regional big data hub

A “big data brain trust” has been established by the National Science Foundation to bring together industry, government and academia to accelerate this emerging field and harness it to solve some of society’s toughest problems.

The University of Michigan will play a leading role in the new Midwest Big Data Innovation Hub—one of four that NSF has set up across the nation. U-M is one of five universities that will lead the Midwest hub. Professor Brian Athey, co-director of U-M’s Michigan Institute for Data Science, will lead the effort at U-M.

“We’re thrilled to be a part of this effort, and are looking forward to establishing dynamic partnerships that will coordinate big data expertise and resources to improve the region’s quality of life,” said Athey, who is the Michael Savageau Collegiate Professor and chair of the Department of Computational Medicine & Bioinformatics in the U-M Medical School and also a professor of psychiatry and internal medicine.

These hubs aim to develop partnerships that will use big data to address region-specific problems. Athey will lead a subgroup of the Midwest Hub that will address health sciences. H.V. Jagadish, U-M professor of electrical engineering and computer sciences, will lead a subgroup on transportation.

The Midwest Hub will focus its efforts in three areas:

  • Society, including smart cities and communities; network science; and business analytics
  • The natural and built world, including water, food and energy; digital agriculture; transportation; and advanced manufacturing
  • Health care and biomedical research

Other universities involved in the Midwest Hub are Illinois, Indiana, North Dakota and Iowa State. Partners include the city of Detroit, Ford Motor Co., General Motors, Domino’s Pizza, TechTown Detroit, Quicken Loans and the Henry Ford Health System.

The NSF award provides $1.25 million to set up the framework for bringing partners together to develop, plan and support regional big data partnerships and activities to address regional challenges.

“The Big Data Hubs program represents a unique approach to improving the impact of data science by establishing partnerships among like-minded stakeholders,” said Jim Kurose, NSF’s head of Computer and Information Science and Engineering. “In doing so, it enables teams of data science researchers to come together with domain experts, with cities and municipalities, and with anchor institutions to establish and grow collaborations that will accelerate progress in a wide range of science and education domains with the potential for great societal benefit.”

For more information:

Midwest Big Data Hub

Michigan Institute for Data Science

Midwest Big Data Hub press release from the University of Illinois

NSF press release

Call for Papers: IEEE Transactions on Intelligent Vehicles

The IEEE Transactions on Intelligent Vehicles (T-IV) publishes peer-reviewed articles that provide innovative research concepts and application results, report significant theoretical findings and application case studies, and raises awareness of pressing research and application challenges in the areas of intelligent vehicles, and in particular in automated vehicles.

The IEEE Transactions on Intelligent Vehicles will commence publication in 2016, with 4 issues annually.

Prospective authors are invited to submit original contributions or survey papers for review for publication in T-IV. Topics of interest include (but are not limited to):

  • Advanced Driver Assistance Systems
  • Automated Vehicles
  • Active and Passive Vehicle Safety
  • Vehicle Environment Perception
  • Driver State and Intent Recognition
  • Eco-driving and Energy-efficient Vehicles
  • Cooperative Vehicle Systems
  • Collision Avoidance
  • Pedestrian Protection
  • Proximity Detection Technology
  • Assistive Mobility Systems
  • Proximity Awareness Technology
  • Autonomous / Intelligent Robotic Vehicles
  • IV related Image, Radar, Lidar Signal Processing
  • Information Fusion
  • Vehicle Control
  • Human Factors and Human Machine Interaction
  • IV technologies in Electric and Hybrid Vehicles
  • Novel Interfaces and Displays
  • Intelligent Vehicle Software Security

All manuscripts must be submitted through Manuscript Central at http://mc.manuscriptcentral.com/t-iv.

Refer to http://its.ieee.org/2014/10/06/submitting-a-paper/ for general information about electronic submission through Manuscript Central.

Editor-in-Chief: Prof. Ümıt Özgüner, The Ohıo State Unıversity, Department of ECE and Center for Automotive Research (CAR), Columbus, Ohio USA. (ozguner.1@osu.edu)