Series of workshops on GIS and data topics starts Feb. 10, runs through April

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LSA IT and the Clark Library are offering a series of workshops covering a broad range of GIS and data topics, from introductory to the advanced level.  These workshops are open to all members of the University of Michigan community.

A list of workshops and information on registration is available.

Topics include introductions to GIS, data visualization, and using tools such as Python and R.

More information about resources for learning GIS, including semester-length courses, online learning resources, and more, is also available from the MLibrary Research Guide.

Limited space still available in HPC 101 & HPC 201 workshops

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A few seats are still available in the following ARC-sponsored training workshops. Please click on the course titles for more information and to register. All sessions take place in Room B743, East Hall.

HPC 101: High Performance Computing Workshop
Monday, Feb. 9, 1 – 5 p.m.
Wednesday, Feb. 11, 1 – 5 p.m.
Prerequisite: HPC 100 or equivalent
This course will provide an overview of cluster computing in general and how to use the U-M Flux Cluster in particular. Topics to be covered include cluster computing concepts, common parallel programming models, introduction to the Flux Cluster; creating, submitting, observing, and analyzing cluster jobs; common pitfalls and how to avoid them; and some useful tools. We will issue you a temporary allocation to use for the course, or you can use your existing Flux allocations, if any. Short sample programs will be provided, or come to class with your own.

HPC 201: Advanced High Performance Computing Workshop
Friday, Feb. 13, 1 – 5 p.m.
Monday, Feb. 16, 1 – 5 p.m.
Prerequisites: HPC 101 or equivalent
This course will cover some more advanced topics in cluster computing on the U-M Flux Cluster. Topics to be covered include a review of common parallel programming models and basic use of Flux; dependent and array scheduling; advanced troubleshooting and analysis using checkjob, qstat, and other tools; use of common scientific applications including Python, MATLAB, and R in parallel environments; parallel debugging and profiling of C and Fortran code, including logging, gdb (line-oriented debugging), ddt (GUI-based debugging) and map (GUI-based profiling) of MPI and OpenMP programs; and an introduction to using GPUs.