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NICS/NIMBioS XSEDE HPC Monthly Workshops

XSEDE logo .
NICS logo .
The National Institute for Computational Sciences (NICS) at UT and NIMBioS host an XSEDE workshop on different topics each month related to high performance computing.

Location: Hallam Auditorium (Room 206) at NIMBioS

The in-person workshops are presented using the Wide Area Classroom (WAC) training platform telecast to several satellite sites in the U.S., including NIMBioS.

Upcoming Workshops:

  • February 7-8 – HPC Monthly Workshop: Big Data
    Registration link. Deadline: 9:00 a.m. EST Feb 5, 2018.
  • March 6 – HPC Monthly Workshop: OpenACC
  • April 3-4 – HPC Monthly Workshop: MPI
  • May 1-2 – HPC Monthly Workshop: Big Data
  • August 7 – HPC Monthly Workshop: OpenMP
  • September – 5-6 HPC Monthly Workshop: Big Data
  • October 2-3 – HPC Monthly Workshop: MPI
  • November 6 – HPC Monthly Workshop: OpenACC
  • December 4-5 – HPC Monthly Workshop: Big Data


Big Data. this session focuses on topics such as Hadoop and Spark.

MPI. This session is intended to give C and Fortran programmers a hands-on introduction to MPI programming. Attendees leave with a working knowledge of how to write scalable codes using MPI, the standard programming tool of scalable parallel computing.

GPU Programming Using OpenACC. OpenACC is the accepted standard using compiler directives to allow quick development of GPU capable codes using standard languages and compilers. It has been used with great success to accelerate real applications within very short development periods. This workshop assumes knowledge of either C or Fortran programming and has a hands-on component using the Bridges computing platform at the Pittsburgh Supercomputing Center.

The National Science Foundation's Extreme Science and Engineering Discovery Environment (XSEDE) is a virtual organization of integrated advanced digital resources available to U.S. researchers. The workshops are co-organized by XSEDE and the Pittsburgh Supercomputing Center (PSC).

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NSF logo. NIMBioS is sponsored by the National Science Foundation through NSF Award #DBI-1300426, with additional support from The University of Tennessee, Knoxville. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
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