NVIDIA accepting applications for Graduate Fellowship Program

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NVIDIA has launched its 16th Annual Graduate Fellowship Program, which awards grants and technical support to graduate students who are doing outstanding GPU-based research.

This year NVIDIA is especially seeking doctoral students pushing the envelope in artificial intelligence, deep neural networks, autonomous vehicles, and related fields. The Graduate Fellowship awards are now up to $50,000 per student. These grants will be awarded in the 2017-2018 academic year.

Since its inception in 2002, the NVIDIA Graduate Fellowship Program has awarded over 130 Ph.D. graduate students with grants that have helped accelerate their research efforts.

The NVIDIA Graduate Fellowship Program is open to applicants worldwide. The deadline for submitting applications is Jan. 16, 2017. Eligible graduate students will have already completed their first year of Ph.D. level studies in the areas of computer science, computer engineering, system architecture, electrical engineering or a related area. In addition, applicants must also be engaged in active research as part of their thesis work.

For more information on eligibility and how to apply, visit http://research.nvidia.com/relevant/graduate-fellowship-program or email fellowship@nvidia.com.

Blue Waters accepting proposals for allocations, fellowships, and undergrad internships

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The GLCPC (Great Lakes Consortium for Petascale Computation) recently posted its call for proposals. Researchers from member institutions (including the University of Michigan) are eligible to apply for a Blue Waters allocation.  The application deadline is Friday, December 2nd.  More information can be found at: http://www.greatlakesconsortium.org/2016cfp.htm

Applications are also being accepted for Blue Waters Fellowships. Applications are due February 3, 2017. More information is available at: https://bluewaters.ncsa.illinois.edu/fellowships

Applications are now being accepted for Blue Waters undergraduate internships. Applications are due February 3, 2017.  More information is available at: https://bluewaters.ncsa.illinois.edu/internships

U-M professor Quentin Stout, a veteran of all 28 Supercomputing conferences, reflects on SC through the years

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Quentin Stout and Christian Jablonowski teaching the Parallel Computing 101 tutorial at SC07.

Quentin Stout, University of Michigan Professor of Computer Science and Engineering (CSE) and Climate and Space Sciences and Engineering (CLaSP), has attended all 28 of the Supercomputing conferences since the event begin in 1988. Stout is one of less than 20 so-called “SC Perennials” to have attended every one. He, along with Christiane Jablonowski, associate professor in CLaSP, have taught the Introduction to Parallel Computing tutorial at the conference for many years and are teaching it again this year. Stout, who has been at U-M since 1984, recently answered some questions about the evolution of the field of computer science and the area of supercomputing over the decades.

Question: What was the first SC conference like, and how has it changed over the years?

Stout: The first conference, in 1988, had about 1,500 people, compared to the over 10,000 now. Its focus was on supercomputing and the large centers at DOE, NASA, NSF, etc., along with the companies that were making these systems. There were also some researchers from academia and a few industrial users. The largest supercomputer user, NSA, had people at the conference, but they didn’t have a booth and their badge listed “Fort Meade” as their affiliation.

Over the years it has greatly broadened its scope to have a much broader international focus and more participation by universities, cluster vendors of all sizes, networking, storage, commercial software, educational efforts, etc. …

Originally I went to learn more about the field, meet people, see what the emerging areas were, and learn about the latest machines. I still go for these reasons, but now machines and software are improving in a more evolutionary fashion than the somewhat revolutionary changes at the beginning. Going from serial computers to vector or parallel ones was more exciting and groundbreaking than going from 100,000 cores to 1,000,000, though the latter is still challenging. Some things have stayed the same: the parties are still good, and companies are still entering and leaving the supercomputing area. For quite some time, if I brought home a coffee mug from a company, the company would go bankrupt in a few years. More recently, IBM developed the BlueGene series of machines, and grabbed the #1 spot in the top 500 rating of machines, but then dropped out of the market because it wasn’t selling enough machines to recoup the tremendous design cost.

One thing that has happened in computing field, not just the conference, is that scientific computing has a far smaller share of the market, even if you only consider the market for large systems. There have always been large database systems in corporations, but data analytics has greatly expanded the possibilities for profit, and hence there is more investment.

Question: What do you predict for the future of supercomputing?

Stout: The most “super” computers aren’t really single computers, but systems such as Google where they are continually processing a vast number of queries, answering them in fractions of a second by using sophisticated algorithms that combining myriad sources from throughout the world, all run on highly tuned systems that keep running even though they have so many components that they are always having to deal with faulty ones. The production users of supercomputers tend to submit a job, let it run for a long time, analyze the results (perhaps using sophisticated graphics), fix some errors or change some parameters, repeat. This isn’t the same as systems which are constantly ingesting data, analyzing it using algorithms that incorporate learning components, responding to increasingly complex queries. Academics, including some at U-M, are involved in this, but it is difficult to create even a scaled down version of a complete system in an academic computing center. You can view IBM’s Watson as being in this arena, and IBM is now betting that Watson will be a large part of its future.

Here’s an interesting cycle in computing: for over a decade some computational scientists and engineers have been using GPUs (graphics processing units). They are very difficult to use, and only applicable to certain types of problems, but inexpensive in terms of flops/$. However, many scientific computations require double precession arithmetic, which isn’t needed for graphics. Companies like NVIDIA, responding to the scientific computing market, began producing more expensive GPUs with double precision, and now systems such as U-M’s Flux computing cluster include GPUs on some of their boards.

However, there is a very rapidly growing demand for “deep learning.” The computationally intensive components of this can be run on GPUs relatively easily, but they don’t need double precision, just speed and plenty of parallelism. This summer NVIDIA released a new high-end chip with good double precision performance, but also added half precision, since that is all that is needed for deep learning. Deep learning might well surpass scientific computing as a GPU market.

 [NOTE: Visit the University of Michigan at SC16 at booth 1543.]


U-M prepares for SC16 conference in Salt Lake City

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University of Michigan researchers and professional research IT staff will participate in the SC16 conference in Salt Lake City from Nov. 13-17 in a number of ways, including demonstrations, presentations and tutorials. Please join us at booth 1543 if you’re at the conference, or at one of the following events:

Sunday, Nov. 13
8:30 a.m. – 5 p.m.: Quentin Stout (EECS) and Christiane Jablonowski (CLASP) will teach the “Parallel Computing 101” tutorial.

Monday, Nov. 14 through Thursday, Nov. 17
U-M will exhibit at booth #1543 alongside Michigan State University. The booth will include an ongoing demonstration of the OSiRIS networking and storage project; information on the Yottabyte Research Cloud; and a presentation on ConFlux.

Tuesday, Nov. 15
10:30 a.m.: Todd Raeker, Research Technology Consultant, ARC-TS, will give a talk on ConFlux at the NVIDIA booth (#2231).
11 a.m.: Project PI Shawn McKee (Physics) will give a presentation on OSiRIS at the U-M booth (#1543).
2:15 p.m.: Nilmini Abeyratne, a Ph.D student in computer science, will present “Low Design-Risk Checkpointing Storage Solution for Exascale Supercomputers” at the Doctoral Showcase.
1 – 5 p.m.: Todd Raeker, Research Technology Consultant, ARC-TS, will participate in the IBM Power8 University Group Meeting.
3 p.m.: Representatives from Yottabyte and ARC-TS will give a presentation on the Yottabyte Research Cloud.
3:30 – 5 p.m., Sharon Broude Geva, Director of Advanced Research Computing, will participate in a panel titled “HPC Workforce Development: How Do We Find Them, Recruit Them, and Teach Them to Be Today’s Practitioners and Tomorrow’s Leaders?

Wednesday, Nov. 16
10 a.m.: Representatives from Yottabyte and ARC-TS will give a presentation on the Yottabyte Research Cloud.
11 a.m.: Ben Meekhof, HPC Storage Administrator, ARC-TS, will give a presentation on OSiRIS at the U-M booth (#1543).
1 p.m.: Todd Raeker, Research Technology Consultant, ARC-TS, will give a talk on ConFlux at the U-M booth (#1543).
5:15 – 7 p.m.: Ben Meekhof, HPC Storage Administrator, ARC-TS, will present at a “Birds of a Feather” meeting on “Ceph in HPC Environments.”

Thursday, Nov. 17
11 a.m.: Project PI Shawn McKee (Physics) will give a presentation on OSiRIS at the U-M booth (#1543).
1 p.m.: Todd Raeker, Research Technology Consultant, ARC-TS, will give a talk on ConFlux at the U-M booth (#1543).

New MICDE Catalyst Grants to fund research projects in computational science

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micde2016symposiumfrontpageThe Michigan Institute for Computational Discovery & Engineering (MICDE) seeks proposals for innovative research projects in computational science that combine elements of mathematics, computer science, and cyberinfrastructure. Of interest is computational science research in any emerging area, including but not limited to (a) applications such as neuroscience, ecology, environmental science, evolutionary biology, human-made complex systems, urban infrastructure and energy; and (b) frameworks for scientific software, and exascale computing. Priority will be given to high-impact projects with potential to attract external funding. MICDE expects to fund 3-4 one-year projects at up to $100,000 each.

An informational session will be held on Thursday, Nov. 10, 2016 at 2:00 p.m. in Room D of the Michigan League (911 N. University).

For more information go to http://micde.umich.edu/grants/catalyst-grants/

MICDE affiliated faculty Monica Valluri (Astronomy) recognized for her outstanding research and teaching achievements

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valluriMonica Valluri, a Research Associate Professor in the Department of Astronomy, has been honored with the U-M Research Faculty Achievement Award for her outstanding research and teaching career in theoretical galaxy dynamics. She uses numerical calculations and simulations to probe galactic phenomena, including supermassive black holes and dark matter halos,two types of invisible matter whose presence is inferred primarily from their gravitational effects on stars and other visible matter.

Valluri earned a Ph.D. in astrophysics at the Indian Institute of Science in Bangalore, did postdoctoral research at Columbia University and Rutgers University, and joined the U-M faculty in 2007.

In addition to developing a more accurate method to determine the masses of SMBH, Valluri has transformed our understanding of galactic bars — elongated cigar-shaped clusters of orbiting stars that exist in many spiral galaxies, including the Milky Way. She demonstrated the traditional view of how stars move in bars is incomplete and that neglecting the effects of galactic bars can cause large errors in the measurement of black hole masses and host galaxy properties. Her work soon will be applied to data being gathered by the European Space Agency’s Gaia space observatory and is expected to verify or refute important predictions of the dominant paradigm regarding the nature of dark matter.

Valluri has published 42 journal articles. In addition to creating and teaching undergraduate astronomy and earth and space science courses, Valluri has taught at the Michigan Math and Science Scholars camp for high school students on a number of occasions. She has served on five doctoral committees and mentored 17 undergraduates. She also founded and organizes Conversations on Equity and Inclusion in Astrophysics and has served on the astronomy department’s curriculum committee and Michigan Institute for Research in Astrophysics planning committee. Valluri is chair of the American Astronomical Society Division of Dynamical Astronomy and a member of the Astronomical Society of India and International Astronomical Union.

With information from the record.umich.edu

Ann Arbor Deep Learning annual event — Nov. 12

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a2-dlearn2016 is an annual event bringing together deep learning enthusiasts, researchers and practitioners from a variety of backgrounds.

MIDAS is proud to co-sponsor the event, which began last year as a collaboration between the Ann Arbor – Natural Language Processing and Machine Learning: Data, Science and Industry meetup groups.

The event will include speakers from the University of Michigan, University of Toronto, Toyota Research Institute and MDA Information Systems.

Please visit the event website for more information. Registration is required as space is limited.

Info Session: Data Science Services at U-M — Nov. 1

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Representatives of Consulting for Statistics, Computing and Analytics Research (CSCAR) and the U-M Library (UML) will give an overview of services that are now available to support data-intensive research on campus.  As part of the U-M Data Science Initiative, CSCAR and UML are expanding their scopes and adding capacity to support a wide range of research involving data and computation.  This includes consulting, workshops, and training designed to meet basic and advanced needs in data management and analysis, as well as specialized support for areas such as remote sensing and geospatial analyses, and a funding program for dataset acquisitions.  Many of these services are available free of charge to U-M researchers.  

This event will begin with overview presentations about CSCAR and Library system data services.  There will also be opportunities for researchers to discuss individualized partnerships with CSCAR and UML to advance specific data-intensive projects.  Faculty, staff, and students are welcome to attend.  

Time/Date: 4-5 p.m., November 1,
Location: Earl Lewis Room, Rackham Building