[LA-SiGMA] HPC Training: Introduction to IPython

[Feng Chen]

Dear All,

The Schedule for the Spring 2016 HPC Training is available at http://www.hpc.lsu.edu/training/tutorials.php.

Our next HPC training will be held on Wednesday, March. 16 at 9:00 AM in 307 Frey Computing Service Center and broadcast through WebEx for remote users. Note that all HPC trainings will start at 9:00AM.

Wednesday, March 16, 2016: Introduction to IPython
IPython has three main components:

*         An enhanced interactive Python shell.

*         A decoupled two-process communication model, which allows for multiple clients to connect to a computation kernel, most notably the web-based notebook

*         An architecture for interactive parallel computing.
In this training we will use IPython web-based notebook connecting to computation kernel running on LSU HPC cluster. We'll introduce a data mining code to mine the Australia Football League (AFL) 2014 record from their statistics website http://afltables.com/afl/seas/2014.html. Then we majorly focus on hands-on visualization of this data, plot each game, and overall team performance. Finally we sort all player's record and try to answer questions like this: Who are the best players and how are they contributing to the winning team.
Prerequisites: LSU HPC account (we'll use philip); SSH client such as Putty for Windows.

Next HPC Training:

Wednesday, March 30, 2016: Distributed Job Execution
While the steadily increasing core counts of modern day processors continue to favor large-scale distributed and multi-threaded applications, the demand for serial processing has surged as non-traditional fields, such as computational biology, increasingly adapt computational methods. This presents a problem on clusters tuned for massively parallel processing. One solution is to tune the job scheduler to carve out resources primarily for serial jobs which complicates system management. The other, taken here, is to adapt existing shell features to facilitate running many essentially embarrassingly parallel tasks under one job script. Running tasks in the background works well for small numbers of tasks, but recent requirements for managing 10's of thousands of such tasks required a better approach - motivating the creation of WQ. It consists of a Python script that implements a run-time aware distribution-worker model, a PBS script with an absolute minimum number of moving parts, and a script or command to execute a single task. This presentation reviews existing shell job control features, then discusses the implementation of WQ. Examples will include purely serial, multi-threaded, and small MPI tasks.
Prerequisites: Beginner to intermediate level knowledge of shell scripting and PBS job submission is assumed, but not required.

Please visit http://www.hpc.lsu.edu/training/tutorials.php for more details and register using the link provided.
Users who plan on joining remotely will be provided with a WebEx Link in their registration confirmation email. Please see the system requirements at https://grok.lsu.edu/Categories.aspx?parentCategoryId=3381.

Please distribute to faculty, staff and students that might be interested.

Thanks,

Feng Chen, PhD
IT Consultant
High Performance Computing
Louisiana State University
329 Frey Computing Services Center, Baton Rouge, LA  70803
office 225-578-2924
fchen14 at lsu.edu<mailto:fchen14 at lsu.edu>



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