Tutorial: Learning to use the Community Earth System Model on Flux — May 6

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If you are interested in learning how to operate the Community Earth System Model (CESM) global climate modeling system using U-M computing resources, please consider attending an upcoming interactive tutorial:

Time/Date/Location: 1:30 – 3:30 p.m., Wednesday, May 6, Space Research Building Auditorium (Room 2246).

Presenter: Mark Flanner, Assistant Professor, AOSS

Topics:

  • Compile CESM on the U of M computing cluster Flux
  • Run a basic configuration of CESM
  • Visualize model output and compute global averages of key model states.
  • Learn how to implement a simple source code change
  • Explore some of the numerous configurations of CESM

RSVP: Contact Prof. Flanner at flanner@umich.edu.

 

Intel Xeon Phi cards now available on Flux

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Eight Intel Xeon Phi 5110p cards are now available from ARC-TS as a technology preview. These are known as Many Integrated Core or MIC architectures, and consist of accelerator cards that fit into a Flux compute nodes. A code can then offload portions or all of the work of a compute job to the card. This can often result in improved performance.
 
As a technology preview, there is no additional cost for using the Phis. Anyone with an active Flux allocation can test the Phis, as long as the jobs are less than 24 hours long.
 
Read posts on the Flux HPC blog to get started and to see an example of the speedup a Phi can provide in Linear Algebra.

REMINDER: U-M to host XSEDE Boot Camp on MPI, Open MP, OpenACC, and more — June 16-19

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XSEDE, along with the Pittsburgh Supercomputing Center and the National Center for Supercomputing Applications at the University of Illinois will be presenting a Hybrid Computing workshop.

This 4 day event will include MPI, OpenMP, OpenACC and accelerators and run June 16-19. We will conclude with a special hybrid exercise contest that will challenge the students to apply their skills over the following 3 weeks and be awarded the Second Annual XSEDE Summer Boot Camp Championship Trophy.

Due to demand, this workshop will be telecast to several satellite sites, including U-M. This workshop is NOT available via a webcast. The workshop will be telecast at 1255 North Quad.

Registration is required: visit the XSEDE registration site.

Agenda (all times Eastern):

Tuesday, June 16
11:00 Welcome
11:15 Computing Environment
11:45 Intro to Parallel Computing
12:30 Intro to OpenMP
1:30 Lunch Break
2:30 Exercise 1
3:15 More OpenMP
4:30 Exercise 2
5:00 Adjourn

Wednesday, June 17
11:00 Intro to OpenACC
12:00 Exercise 1
12:30 Introduction to OpenACC (cont.)
1:00 Lunch Break
2:00 Exercise 2
2:45 Introduction to OpenACC (cont.)
3:00 Using OpenACC with CUDA Libraries
3:30 Advanced OpenACC
4:00 OpenMP 4.0 Sneek Peek
5:00 Adjourn

Thursday, June 18
11:00 Introduction to MPI
1:00 Lunch break
2:00 Intro Exercises
3:10 Intro Exercises Review
3:15 Scalable Programming: Laplace code
3:45 Laplace Exercise
5:00 Adjourn

Friday, June 19
11:00 Laplace Exercise Review
12:30 Laplace Solution
1:00 Lunch break
2:00 Advanced MPI
3:00 Outro to Parallel Computing
4:00 Hybrid Computing
4:30 Hybrid Competition
5:00 Adjourn

Michigan State offering two workshops: “Train the Trainer” on teaching adults to program (May 27-28), and Parallel Programming and Optimization on Intel Xeon Phi (June 4-5)

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Michigan State University’s Institute for Cyber-Enabled Research (ICER) is offering the following workshops in the coming weeks:

  • May 27-28Train the Trainers taught by Greg Wilson, creator and Executive Director of Software Carpentry. During this 2-day course  participants will learn how to teach adults to program. In addition, participants will who wish to become certified as Software Carpentry instructors by completing 2 additional exercises after the course.
  • June 4-5, 2015Parallel Programming and Optimization with the the Intel Xeon Phi Coprocessor: Intel is offering a 2-day workshop at MSU. Join us for this updated and expanded series of software developer trainings in parallel programming using the Intel® Xeon PhiTM coprocessor. Registration is available on a first come first serve basis and a limited number of spots are available for second day of the workshop.

“Current and Future Directions in Advanced Cyberinfrastructure,” James Barr von Oehsen, Clemson U. — April 21, Michigan Union

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Dr. James Barr von Oehsen, Executive Director of Cyberinfrastructure Technology Information Group at Clemson University, will be on campus to deliver a seminar.

Time/Date: 10 a.m., Tuesday, April 21
Location: Wolverine Room, Michigan Union
Title: Current and Future Directions in Advanced Cyberinfrastructure

Abstract: Access to advanced computing, distributed data, and cloud infrastructures have become increasingly critical to research and education endeavors in a wide range of disciplines. As campus IT and research computing groups around the country build and expand infrastructure components to address the needs of their users, coordination between and across campuses and cloud services has become critical to create successful, sustainable, scalable, and flexible solutions that can be implemented in multiple settings. In this presentation Dr. von Oehsen will give an overview of the projects he is involved with as executive director of the Clemson Cyberinfrastructure Technology Integration (CITI) group as well as future directions he sees advanced computing/cyberinfrastructure taking within this ecosystem.

Bio: Dr. James Barr von Oehsen is the Executive Director of the Cyberinfrastructure Technology Integration (CITI) group within Clemson Computing and Information Technology (CCIT) and the Center of Excellence for Next Generation Computing (CoENGC).  As executive director, Dr. von Oehsen is responsible for providing strategic leadership in advancing Clemson University’s research and scholarly achievements through next generation computing, networking, data technologies, and creative learning environments. Prior to joining CCIT he was employed by the Center for Advanced Engineering Fibers and Films, CAEFF, an NSF-funded engineering research center, where he directed the development of parallel finite element code for modeling polymer processes. He has worked as a computational scientist at Clemson for over twelve years. While at CAEFF he was also responsible for building the advanced computing environment (including storage, web services, databases, network hardware, and visualization). His research interests are in high performance computing, high throughput computing, mathematical modeling, parallel programming, campus level distributed cloud environments, hardware architecture, and next generation creative learning environments. Dr. von Oehsen has a PhD from Rutgers, the State University of New Jersey, in Algebraic Topology.

MIDAS micro Big Data Analytics workshop — April 21-24

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The Michigan Institute for Data Science (MIDAS) is organizing a micro Big Data Analytics workshop to openly discuss, share and collaborate on developing the foundation for developing a new Compressive Big Data Analytics (CBDA) theory. The highlights of the workshop are seminars by Dr. Saeid Amiri (Nebraska) and Dr. Ejaz Ahmed (Brock U).

Compressive Big Data Analytics (CBDA):

This is a high-risk/high-potential-impact idea. Basically, we are working on developing the foundations of a Compressive Big Data Analytics (CBDA) framework involving iterative generation of random (sub)samples from a Big Data collection, using classical techniques to develop model-based or non-parametric inference, repeat the (re)sampling and inference steps many times, and finally employ bootstrapping techniques to quantify probabilities, estimate likelihoods, or assess accuracy of findings. We are looking for collaborators with students that can help in the algorithmic development, deriving upper bound error estimates and demonstrating the application of this technique on several large, heterogeneous and multi-source datasets.

We expect that the CBDA approach may provide a scalable solution avoiding some of the Big Data management and analytics challenges. CBDA sampling is conducted on the data-element level, not on the case level, and the sampled values are not necessarily consistent across all data elements (e.g., high-throughput random sampling from cases and variables within cases).  We are now investigating the theoretical properties (e.g., asymptotics, as sample sizes increase to infinity, but the data has sparse conditions) of model-free inference entirely based on the complete dataset without any parametric or model-limited restrictions.

Saeid Amiri

Presenter: Dr. Saeid Amiri (http://statistics.unl.edu/saeid-amiri)

Visit: April 21-23, 2015

Title: Random Subspace Scientific Inference based on High dimensional data

Seminar: Tuesday, April 21, 2015, 4:00-5:00 p.m., Palmer Commons, Great Lakes Room North

Abstract: Extraction of valuable information from  Big data (n>>p) in high dimensions  (p>>n) and the subsequent scientific inference using such derived information present considerable challenges in many medical, biological, social and data-driven sciences. In this talk, I will present statistical learning and unsupervised machine learning techniques for the low dimension data  and discuss a new sub-space alternative approach. We will illustrate an extension method  for higher-dimensions and big data based on random subspaces.   We provide a series of arguments to justify the new technique and will provide examples involving real and simulated data to compare our method with other related techniques.

Ejaz Ahmed

Presenter: Dr. Ejaz Ahmed (http://statistics.unl.edu/saeid-amiri) (http://www.brocku.ca/mathematics-science/departments-and-centres/mathematics/people/professors/syed-ejaz-ahmed), distinguished prof at Brock U/Canada.

Visit: April 23-24, 2015

Title: TBD

Seminar: Friday, April 24, 2015, TBD (morning), place TBD

Abstract: TBD

XSEDE15 conference seeks student participation

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High school and college students are encouraged to participate in the XSEDE15 Student Program.

XSEDE15, the fourth conference of XSEDE, the Extreme Science and Engineering Discovery Environment, will be held July 26-30, 2015, at the Marriott Renaissance Grand Hotel in downtown St. Louis, Missouri. XSEDE15 will showcase the discoveries, innovations, challenges and achievements of those who utilize and support XSEDE resources and services, as well as other digital resources and services, throughout the world.

By participating in the Student Program, high school, undergraduate, or graduate students can:

  • Meet computational and domain scientists from around the country who use high-performance systems in their research.
  • Compete in a student poster contest that shares your research involving advance cyberinfrastructure.
  • Attend introductory tutorials tailored for students new to computational science or more advanced tutorials designed to help you get the most out of XSEDE resources.
  • Present your research by submitting a paper to the XSEDE15 Technical Program.
  • Compete in a team-based student modeling and simulation challenge.

Pending final approval, the National Science Foundation may provide limited funding to support student travel, lodging, and/or registration costs for attending XSEDE15.

For details on the Student Program, see the Call for Participation:
https://conferences.xsede.org/technical-program/student-program

To apply for travel funding, see the XSEDE15 Student Travel Grant Application:
https://www.surveymonkey.com/s/XSEDE15TravelGrantApp

To submit your poster or paper, see the XSEDE15 website:
https://www.xsede.org/xsede15

Please contact Jenett Tillotson, XSEDE15 Student and EOT Program Chair at jtillots@iu.edu if you have questions.

REMINDER: Info session: Graduate Studies in Computational and Data Sciences at U-M — April 15

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Learn about graduate programs that will prepare you for success in computationally intensive fields, and enjoy pizza and pop! Presentations will describe the following programs:

  • The Ph.D. in Scientific Computing is open to all Ph.D. students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their studies. It is a joint degree program, with students earning a Ph.D. from their current departments, “… and Scientific Computing” — for example, “Ph.D. in Aerospace Engineering and Scientific Computing.”
  • The Graduate Certificate in Computational Discovery and Engineering trains graduate students in computationally intensive research so they can excel in interdisciplinary HPC-focused research and product development environments. The certificate is open to all students currently pursuing Master’s or Ph.D. degrees at the University of Michigan.
  • The new Graduate Certificate in Data Science is focused on developing core proficiencies in data analytics: 1) Modeling — Understanding of core data science principles, assumptions and applications; 2) Technology — Knowledge of basic protocols for data management, processing, computation, information extraction, and visualization; 3) Practice — Hands-on experience with real data, modeling tools, and technology resources.

Wednesday, April 15, 5-6 p.m., Cooley G906

Presenters:

  • Ken Powell, Arthur F. Thurnau Professor of Aerospace Engineering
  • Eric Michielssen, Professor of Electrical Engineering and Computer Science
  • Ivo Dinov, Associate Professor of Bioinformatics and Leadership and Effectiveness Science.

There will be time for questions and discussion.

Dr. Michael Mascagni of Florida State U. to present seminars on Monte Carlo Methods — April 13, 14

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Dr. Michael Mascagni, Professor of Computer Science, Mathematics, Scientific Computing and Molecular Biophysics at Florida State University, will be on the U-M campus for two seminars on Monte Carlo Methods:

Monday, April 13, 4:00-5:00 pm
340 West Hall

“Monte Carlo Methods and Partial Differential Equations: Algorithms and Implications for High-Performance Computing”

This talk will focus on the way Monte Carlo’s computational needs fit well on current and future (Exascale) HPC systems. Prof. Mascagni will give a brief overview of the history of the Monte Carlo method for the numerical solution of partial differential equations (PDEs) focusing on the Feynman-Kac formula for the probabilistic representation of the solution of the PDEs. We then take the example of solving the linearized Poisson-Boltzmann equation to compare and contrast standard deterministic numerical approaches with the Monte Carlo method. Monte Carlo methods have always been popular due to the ease of finding computational work that can be done in parallel. Prof. Mascagni look at how to extract parallelism from Monte Carlo methods, and some newer ideas based on Monte Carlo domain decomposition that extract even more parallelism. In light of this, he looks at the implications of using Monte Carlo to on high-performance architectures and algorithmic resilience.

Tuesday, April 14 10:00-11:30 am
2906 Cooley Building (Baer Room), North Campus

“An Introduction to Monte Carlo Methods and Random Number Generation for High-Performance Computing using the SPRNG Random Number Library”

This talk will give a brief overview of Monte Carlo methods and HPC trends focusing on the random number generation requirements they have. We then discuss parallel random number generation, and show the mathematical basis for the Scalable Parallel Random Number Generation (SPRNG) library.  We then discuss the current state of SPRNG, and ongoing work making SPRNG suitable for multicore and GPU acceleration.

“Bach, Big Data, Math and Music,” U-M prof. Daniel Forger — April 15

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Daniel Forger, organist and professor of math, computational medicine and bioinformatics, will present “Bach, Big Data, Math and Music.”

Wednesday, April 15
12:15 p.m.

Community Lounge of the School of Public Health I.
1415 Washington Heights

Bio: Daniel Forger is a Professor of Mathematics and Research Professor of
Computational Medicine and Bioinformatics at the University of
Michigan. He is also an Associate of the American Guild of Organists,
won a McCord Prize in Music, and has studied organ performance with
many teachers, including James Kibbie.

Description:
Trio Sonata #2, BWV 526 by J.S. Bach
Trio Sonata #4, BWV 528 by J.S. Bach

Public Health is currently being revolutionized by mathematical
techniques analyzing “Big Data.” Can similar techniques can be used to
understand music? Forger will argue that organ music has been subject to
“Big Data” for at least 100 years, as modern keyboard action
transforms each note played into a simple on and off command to a
pipe. Forger will also argue that the Bach Trio Sonatas are ideal
candidates for “Big Data” analysis since: 1) Bach is the natural
starting point for musical analysis. 2) The trio sonatas were
important to Bach. and 3) The trio sonatas have a very uniform
structure. Forger has captured this code generated by his performance of
the Trio Sonatas, by hacking into the modern organ in his home, and
analyzed it, using some preliminary mathematical techniques. As he
performs the Trio Sonatas, graphs showing preliminary analysis will be
presented.

These are informal events to which you are invited to bring your lunch
(pack your own or stop by the Glass House Café at SPH). Programs begin
at 12:15 and conclude in time for you to reach your 1:00 appointments.