Overview
Access
For all ALICE users
All ALICE users can access part of the resources on node802 using partition "amd_short".
For MI users
ALICE users from MI also have access to partition "mem_mi". This partition is only accessible for MI users.
Jobs submitted to this partition should always get a higher priority than jobs submitted to amd-short. If you notice any issues with this, please contact the ALICE Helpdesk.
Hardware
The basic hardware configuration of node802 is available here: https://pubappslu.atlassian.net/wiki/spaces/HPCWIKI/pages/37519378/About+ALICE#Hardware-Description
Hyperthreading is active
Local node scratch
The two 10TB HDDs were combined into a single volume of about 20TB mounted at /scratchdata
You can use the local scratch on node802 in the same way that you would use it on other ALICE nodes.
Software
AMD-specific software stack
Because this node uses AMD CPUs, there is a new and separate AMD software stack.
The main ALICE software stack was compiled with Intel CPUs and should not be used for jobs on node802.
New software can be added to the stack by contacting the ALICE Helpdesk.
It might be necessary to recompile your own software for running on node802.
This is also the case for conda and Python virtual environment
One option to do this is to create a short slurm batch job specifically for compiling your software, setting up your conda/Python environments, etc. If you only need to do this once, then there is no need to make this part of your production batch job.
To get a list of currently available modules in the AMD software stack when you are on a login node, use
module load ALICE/AMD
and thenmodule avail
If you want to go back to the Intel software stack, use
module load ALICE/Intel
For a Slurm batch script that you submit to node802, you can also use
module load ALICE/default
which will load the correct software stack based on the CPU architecture of the node.You can include
module load ALICE/default
in all your batch scripts if you like
Your own scripts/programmes
Because a separate software stack is necessary for an AMD machine, you should not compile jobs with GNU-compilers or the likes on the login nodes which are Intel based
You should always compile such scripts/software as part of your job
If you do not make any changes you can compile your program on node802 the first time you run it as part of a job. In this first job, you copy the compiled program back to your shared storage or home directory. For the next job, you use the already compiled version (see example below).
You can still use the login nodes for testing/debugging. In this case, you need to compile on the login nodes, run your test and for your job, compile on the compute node again.
Example
Here is an example of how a Slurm batch script could look like for using the node, including a HelloWorld OpenMP program to demonstrate the compiling and use of the local scratch storage.
If you are new to HPC, ALICE or Slurm, have a look at the https://pubappslu.atlassian.net/wiki/spaces/HPCWIKI/pages/5963809 first.
Batch script
#!/bin/bash #SBATCH --partition=mem_mi #SBATCH --job-name=test_job #SBATCH --time=0-00:02:00 #SBATCH --output=%x_%j.out #SBATCH --nodes=1 #SBATCH --ntasks=5 #SBATCH --cpus-per-task=3 #SBATCH --mem=10G #SBATCH --mail-user="your-email-address" #SBATCH --mail-type="ALL" module load ALICE/default module load OpenMPI/4.0.5-GCC-9.3.0 echo "#### Test started" # return the name of the node echo "## Which node is this: $HOSTNAME" # check the number of cores (ntasks*cpus-per-task) echo "How many cores do I have access to: ${SLURM_CPUS_ON_NODE}" # Just to check that the AMD software stack is loaded echo "Am I loading the from the right module path" echo ${MODULEPATH%%:*} # get the current working directory CWD=$(pwd) echo "## Where am I: ${CWD}" # check out the nodes local scratch echo "## My local scratch space on the node is: ${SCRATCH}" cd $SCRATCH echo "## Let us go there: $(pwd)" # In case the file has already been compiled # and stored in $CWD, the following six lines # are not necessary echo "## Let us copy the C script to it" cp $CWD/omp_hello.c $SCRATCH/ echo "## Is the file there?" ls -la omp_hello.c echo "## Now we compile it on the node" gcc -o omp_hello_amd -fopenmp omp_hello.c # In case the file is already compiled # the next four lines would copy it # and check that it is there: #echo "## Let us copy the compiled C programme to it" #cp $CWD/omp_hello_amd $SCRATCH/ #echo "## Is the file there?" #ls -la omp_hello_amd echo "## Let us run it" export OMP_NUM_THREADS=$SLURM_CPUS_PER_TASKS srun ./omp_hello_amd # Copy those files back to shared scratch or home # that should be kept for later. # Here, it is just the compiled C programme. # It does not need to be copied back of course # if it came from shared scratch or home. echo "## Saving files that should be saved." cp $SCRATCH/omp_hello_amd $CWD/ echo "## Now that this is done, I want to go home" cd $CWD echo "## Good to be back $(pwd)" echo "#### Test finished"
OpenMP script
Here is the content of the file omp_hello.c from https://computing.llnl.gov/tutorials/openMP/samples/C/omp_hello.c
/****************************************************************************** * * FILE: omp_hello.c * * DESCRIPTION: * * OpenMP Example - Hello World - C/C++ Version * * In this simple example, the master thread forks a parallel region. * * All threads in the team obtain their unique thread number and print it. * * The master thread only prints the total number of threads. Two OpenMP * * library routines are used to obtain the number of threads and each * * thread's number. * * AUTHOR: Blaise Barney 5/99 * * LAST REVISED: 04/06/05 * ******************************************************************************/ #include <omp.h> #include <stdio.h> #include <stdlib.h> int main (int argc, char *argv[]) { int nthreads, tid; /* Fork a team of threads giving them their own copies of variables */ #pragma omp parallel private(nthreads, tid) { /* Obtain thread number */ tid = omp_get_thread_num(); printf("Hello World from thread = %d\n", tid); /* Only master thread does this */ if (tid == 0) { nthreads = omp_get_num_threads(); printf("Number of threads = %d\n", nthreads); } } /* All threads join master thread and disband */ }