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Computing Server Workshop [KAGAYAKI] ~ Intro to Massively Computing Server/MPI ~
KAGAYAKI supports for common programming languages such as C/fortran, python etc. Additionally, MATLAB, MaterialsStudio, Mathematica, and other commercial software will be available after July. Let's try to master the useful biggest computing server!

KAGAYAKI is the most biggest Computing Server in JAIST, consisted of 280 compute nodes built with 2.6GHz/128 CPU Cores, 512GB Memory.

  In the first part of this workshop, you will learn the basic usage of KAGAYAKI, such as compiling and submitting jobs, and in the second part provides you the knowledge and skills about Massively Computing and MPI Parallelization.

Please note that questions in English are welcome although the workshop will be basically conducted in *Japanese*.

   Registration:
    
Registration form is in the bottom of the page.
       (The deadline is Jul. 1 (Thu)17:00)

   How to join the Orientation(MS Teams is required):

  1. Click the following link, "MPC Teams"
  2. Join the "MPC" Team.
  3. Join the Channel:  "20210702 [KAGAYAKI] Computing workshop".
  4. Click the Meeting link in the channel

         MPC Teams

  Notice:

  • This workshop will be recorded and published via JAIST LMS system.
  • The server will be replaced with the successor server on the next spring.
    Notice that, all programs need to be migrated, re-compiled for the replacement .
  • The hands-on PC requirement:
    • Access to JAIST network(VPN or fep will be required if you aren't in JAIST. )
    • Terminal software for SSH session
        example: mobaXterm, putty  or WSL2(windows), XQuartz(MacOS)
      * If you aren't familiar with using CUI based editor (vi, emacs, etc), we recommend you install mobaXterm or XQuartz.
Startdate
2021.07.02 - 13:30
Enddate
2021.07.02 - 17:10
場所/Place
プログラム/Program

      13:30-15:20 session1: Basic skills for KAGAYAKI

  • System Summary
  • Usage
  • Hands-on

      15:20-15:30 break

      15:30-17:00 session2: Intro to MPI

  • Programming models for parallel computing    
  • Intro to MPI
  • Hands-on