Preparation

1. How to login to MeluXina machine¶

• 1.1 Please take a look if you are using Windows
• 1.2 Please take a look if you are using Linux/Mac

2. Use your username to connect to MeluXina¶

• 2.1 For example, the below example shows the user of u100490

  $ ssh u100490@login.lxp.lu -p 8822
  ### or
  $ ssh meluxina 
  

3. Once you have logged in¶

• 3.1 Once you have logged in, you will be in a default home directory

  [u100490@login02 ~]$ pwd
  /home/users/u100490
  

• 3.2 After that, go to the project directory.

  [u100490@login02 ~]$ cd /project/home/p200301
  [u100490@login02 p200301]$ pwd
  /project/home/p200301
  

4. And please create your own working folder under the project directory¶

• 4.1 For example, here is the user with u100490:

  [u100490@login02 p200301]$ mkdir $USER
  ### or 
  [u100490@login02 p200301]$ mkdir u100490  
  

5. Now it is time to move into your home directory¶

• 5.1 For example, with user home directory u100490

  [u100490@login02 p200301]$cd u100490
  

6. Now it is time to copy the folder which has examples and source files to your home directory¶

• 6.1 For example, with user home directory u100490

  [u100490@login03 u100490]$ cp -r /project/home/p200301/CUDA .
  [u100490@login03 u100490]$ cd CUDA/
  [u100490@login03 CUDA]$ pwd
  /project/home/p200301/u100490/CUDA
  [u100490@login03 CUDA]$ ls -lthr
  total 20K
  -rw-r-----. 1 u100490 p200301   51 Mar 13 15:50 module.sh
  drwxr-s---. 2 u100490 p200301 4.0K Mar 13 15:50 Vector-addition
  drwxr-s---. 2 u100490 p200301 4.0K Mar 13 15:50 Unified-memory
  ...
  ...
  

7. Until now, you are in the login node; now it is time to do the dry run test¶

• 7.1 Reserve the interactive node for running/testing CUDA applications

  $ salloc -A p200301 --res eurocc2-gpu-course-morning --partition=gpu --qos default -N 1 -t 01:00:00
  

  check if your reservation is allocated

  [u100490@login03 ~]$ salloc -A p200301 --res training_part1 --partition=gpu --qos default -N 1 -t 01:00:00
  salloc: Pending job allocation 296848
  salloc: job 296848 queued and waiting for resources
  salloc: job 296848 has been allocated resources
  salloc: Granted job allocation 296848
  salloc: Waiting for resource configuration
  salloc: Nodes mel2131 are ready for job
  

• 7.2 You can also check if you got the interactive node for your computations, for example, here with the user u100490:

  [u100490@mel2131 ~]$ squeue -u u100490
              JOBID PARTITION     NAME     USER    ACCOUNT    STATE       TIME   TIME_LIMIT  NODES NODELIST(REASON)
             304381       gpu interact  u100490   p200301  RUNNING       0:37     01:00:00      1 mel2131
  

8. Now we need to check a simple CUDA application if that is going to work for you:¶

• 8.1 Go to folder Dry-run-test

  [u100490@login03 CUDA]$ cd Dry-run-test/
  [u100490@login03 Dry-run-test]$ ls 
  Hello-world.cu  module.sh
  

9. Finally, we need to load the compiler to test the GPU CUDA codes¶

• 9.1 We need a Nvidia HPC SDK compiler for compiling and testing CUDA code

  $ module load env/staging/2023.1
  $ module load OpenMPI/4.1.5-NVHPC-23.7-CUDA-11.7.0
  $ export NVCC_APPEND_FLAGS='-allow-unsupported-compiler'
  ### or
  $ source module.sh
  

  check if the module is loaded properly

  [u100490@mel2131 ~]$ module load env/staging/2023.1
  [u100490@mel2131 ~]$ module load OpenMPI/4.1.4-NVHPC-22.7-CUDA-11.7.0
  [u100490@mel2131 ~]$ export NVCC_APPEND_FLAGS='-allow-unsupported-compiler'
  [u100490@mel2131 ~]$ module list
  
  Currently Loaded Modules:
  1) env/release/2022.1           (S)   6) numactl/2.0.14-GCCcore-11.3.0  11) libpciaccess/0.16-GCCcore-11.3.0  16) GDRCopy/2.3-GCCcore-11.3.0                  21) knem/1.1.4.90-GCCcore-11.3.0
  2) lxp-tools/myquota/0.3.1      (S)   7) CUDA/11.7.0                    12) hwloc/2.7.1-GCCcore-11.3.0        17) UCX-CUDA/1.13.1-GCCcore-11.3.0-CUDA-11.7.0  22) OpenMPI/4.1.4-NVHPC-22.7-CUDA-11.7.0
  3) GCCcore/11.3.0                     8) NVHPC/22.7-CUDA-11.7.0         13) OpenSSL/1.1                       18) libfabric/1.15.1-GCCcore-11.3.0
  4) zlib/1.2.12-GCCcore-11.3.0         9) XZ/5.2.5-GCCcore-11.3.0        14) libevent/2.1.12-GCCcore-11.3.0    19) PMIx/4.2.2-GCCcore-11.    3.0
  5) binutils/2.38-GCCcore-11.3.0      10) libxml2/2.9.13-GCCcore-11.3.0  15) UCX/1.13.1-GCCcore-11.3.0         20) xpmem/2.6.5-36-GCCcore-11.3.0
  
  Where:
      S:  Module is Sticky, requires --force to unload or purge
  

10. Please compile and test your CUDA application¶

• 10.1 For example, Dry-run-test

  // compilation
  $ nvcc -arch=compute_70 Hello-world.cu -o Hello-World-GPU
  
  // execution
  $ ./Hello-World-GPU
  
  // output
  $ Hello World from GPU!
    Hello World from GPU!
    Hello World from GPU!
    Hello World from GPU!
  

11. Similarly, for the hands-on session, we need to do the node reservation:¶

• 11.1 For example, reservation

  $ salloc -A p200301 --res eurocc2-gpu-course-afternoon --partition=gpu --qos default -N 1 -t 02:30:00
  

  check if your reservation is allocated

  [u100490@login03 ~]$ salloc -A p200301 --res training_part2 --partition=gpu --qos default -N 1 -t 02:30:00
  salloc: Pending job allocation 296848
  salloc: job 296848 queued and waiting for resources
  salloc: job 296848 has been allocated resources
  salloc: Granted job allocation 296848
  salloc: Waiting for resource configuration
  salloc: Nodes mel2131 are ready for job
  

12. We will continue with our Hands-on exercise¶

• 12.1 For example, in the Hello World example, we do the following steps:

  [u100490@mel2063 CUDA]$ pwd
  /project/home/p200301/u100490/CUDA
  [u100490@mel2063 CUDA]$ ls
  [u100490@mel2063 CUDA]$ ls
  Dry-run-test  Matrix-multiplication  Profiling      Unified-memory
  Hello-world   module.sh              Shared-memory  Vector-addition
  [u100490@mel2063 CUDA]$ source module.sh
  [u100490@mel2063 CUDA]$ cd Hello-world
  // compilation
  [u100490@mel2063 CUDA]$ nvcc -arch=compute_70 Hello-world.cu -o Hello-World-GPU 
  
  // execution
  [u100490@mel2063 CUDA]$ ./Hello-World-GPU
  
  // output
  [u100490@mel2063 CUDA]$ Hello World from GPU