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Advanced Research Computing Training Program

FREE training workshops offered by CCAST/NDSU. Pre-registration is required!

The CCAST Advanced Research Computing Training Program is offering a workshop series to introduce advanced research computing (high-performance computing/supercomputing, high-throughput computing, cloud computing, data storage and management, etc.) to faculty, staff, and students at NDSU and certain other colleges and universities in the states of North Dakota and Minnesota. This training series also aims to provide researchers with the basic knowledge and hands-on skills to make the best use of local and national high-performance computing (HPC) facilities and ultimately to advance their research capabilities.

The training events, offered on Friday from 2:00–4:00 p.m. via Zoom (for the 5 core workshops) and in person (for the 4 special workshops) will include lectures, demonstrations, and hands-on sessions where participants have opportunities to perform simple and not-so-simple tasks on CCAST’s HPC systems. There are no prerequisites, except a willingness to learn. Some familiarity with computer programming and Linux shell scripting would be helpful, but not required. 

The schedule for Fall 2022 is as follows:

Five (5) core training workshops (online via Zoom video conferencing)

  • September  2: Introduction to high-performance computing (HPC)
    Basics of HPC, computer clusters, parallel computing, HPC resources at NDSU and elsewhere, access to CCAST's HPC systems
  • September 16: Linux for HPC: Working with Linux-based HPC systems
    Basics of UNIX/Linux, job scheduler, queue policies, running and monitoring jobs on HPC systems  
  • September 23: How to get your work done faster? Parallel computing
    Parallel programming models, work distribution among CPU cores, parallel scaling performance, tips on running parallel jobs
  • September 30: Linux for HPC: Text processing and shell scripting
    Linux utilities for searching texts and manipulating text files, Bash shell scripting to automate tasks 
  • October 7Accelerated computing with GPUs
    Basics of graphics processing units (GPUs) and GPU programming, running scientific applications on GPU compute nodes

Four (4) special training workshops (in person at NDSU; building/room: Engineering 102)

  • October 14: Running Python on HPC systems
    Running Python codes on HPC systems; available Python versions and where to find them; creating custom Python environments and installing packages; integrating with Jupyter Notebook
  • October 21: HPC for machine learning and big data
    Basics of machine learning and big data; running TensorFlow, Keras, Spark, etc. on CPU/GPU compute nodes

  • October 28: HPC for bioinformatics
    Running popular bioinformatics tools using the batch scheduler; examples will include a basic workflow for mapping high-throughput sequencing reads; Singularity containers  
  • November 4: HPC for materials modeling and simulations
    Running properly and efficiently complex materials modeling software packages (VASP, Gaussian, NAMD, LAMMPS, etc.) on HPC systems
The training is FREE; however, pre-registration is required. Please register by August 31, 2022, by filling out this online form [registration closed; contact CCAST if you have questions]. Prospective participants can register for any workshop; however, registering and attending all the core training workshops is strongly recommended because the workshops are sequential.   
CCAST provides advanced research computing resources, training, and consulting to faculty, students, and staff at NDSU and beyond. The research we enable spans a broad range of disciplines, including science, engineering, agriculture, public health, business, finance, etc.

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Keywords:ccast, hpc, training, linux, unix, computing, "research computing", "advanced research computing", "high performance computing", "parallel computing", "scientific computing", workshop   Doc ID:107964
Owner:Khang H.Group:IT Knowledge Base
Created:2020-12-17 16:17 CSTUpdated:2022-09-19 07:55 CST
Sites:IT Knowledge Base
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