Video of the service presentation (3/7/22)

The cluster comprises the following computing components:

• 9 general computing servers.
• 1 “fat node” for jobs requiring a lot of memory.
• 6 servers for GPU computing.

Users only have direct access to the login node, which has more limited performance and should not be used for computing.
All nodes are interconnected by a 10Gb network.
There is distributed storage accessible from all nodes with 220 TB capacity connected via a dual 25Gb fiber network.

To access the cluster, you must request it in advance through the incident form. Users without access permission will receive a “wrong password” message.

Access is made via SSH to the login node (172.16.242.211):

ssh <username>@hpc-login2.inv.usc.es

Users' HOME in the cluster is on the shared file system, so it is accessible from all nodes in the cluster. The path is defined in the environment variable $HOME.
Each node has a local scratch partition of 1 TB, which is deleted upon completion of each job. It can be accessed using the environment variable $LOCAL_SCRATCH in scripts.
For data that needs to be shared among user groups, a request must be made to create a folder in the shared storage that will only be accessible by group members.

* storage is shared

The shared file system performs poorly when working with many small-sized files. To improve performance in such scenarios, you should create a file system on an image file and mount it to work directly on it. The procedure is as follows:

• Create the image file in your home:

## truncate image.name -s SIZE_IN_BYTES
truncate example.ext4 -s 20G

• Create a file system on the image file:

## mkfs.ext4 -T small -m 0 image.name
## -T small optimized options for small files
## -m 0 Do not reserve space for root 
mkfs.ext4 -T small -m 0 example.ext4

• Mount the image (using SUDO) with the script mount_image.py :

## By default, it will be mounted at /mnt/images/<username>/ in read-only mode.
sudo mount_image.py example.ext4

• To unmount the image, use the script umount_image.py (using SUDO)

sudo umount_image.py

The mount script has the following options:

--mount-point path   <-- (optional) With this option, it creates subdirectories below /mnt/images/<username>/<path>
--rw                  <-- (optional) By default, it is mounted readonly; with this option, it is mounted readwrite.

The unmount script has the following options:

only accepts as an optional parameter the same path you used for mounting with the option 
--mount-point  <-- (optional)

From your local machine to the cluster:

scp filename <username>@hpc-login2:/<path>

From the cluster to your local machine:

scp filename <username>@<hostname>:/<path>

SCP Manual Page

To transfer multiple files or to navigate through the file system.

<hostname>:~$ sftp <user_name>@hpc-login2
sftp>
sftp> ls
sftp> cd <path>
sftp> put <file>
sftp> get <file>
sftp> quit

SFTP Manual Page

RSYNC Documentation

Requires installation of the sshfs package.
Allows, for example, to mount the user's home from hpc-login2:

## Mount
sshfs  <username>@ctdeskxxx.inv.usc.es:/home/<username> <mount_point>
## Unmount
fusermount -u <mount_point>

SSHFS Manual Page

All nodes have the basic software installed by default with AlmaLinux 8.4, particularly:

• GCC 8.5.0
• Python 3.6.8
• Perl 5.26.3

On GPU nodes, additionally:

• nVidia Driver 510.47.03
• CUDA 11.6
• libcudnn 8.7

To use any other software not installed in the system or another version, there are three options:

1. Use Modules with the already installed modules (or request the installation of a new module if not available)
2. Use a container (uDocker or Apptainer/Singularity)
3. Use Conda

A module is the simplest solution to use software without modifications or dependencies that are difficult to meet.
A container is ideal when the dependencies are complicated and/or the software is highly customized. It is also the best solution if what you seek is reproducibility, ease of distribution, and teamwork.
Conda is the best solution if you need the latest version of a library or program or packages not available otherwise.

Lmod Documentation

# View available modules:
module avail
# Load a module:
module <module_name>
# Unload a module:
module unload <module_name>
# View loaded modules in your environment:
module list
# ml can be used as an abbreviation for the command module:
ml avail
# To obtain information about a module:
ml spider <module_name>

uDocker Manual
uDocker is installed as a module, so it is necessary to load it into the environment:

ml udocker

Apptainer/Singularity Documentation
Apptainer/Singularity is installed on each node's system, so no action is required to use it.

Conda Documentation
Miniconda is the minimal version of Anaconda and only includes the conda environment manager, Python, and a few necessary packages. From there, each user only downloads and installs the packages they need.

# Get miniconda
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
# Install it 
bash Miniconda3-latest-Linux-x86_64.sh
# Initialize miniconda for the bash shell
~/miniconda3/bin/conda init bash

The queue manager in the cluster is SLURM .

hpc-login2 ~]# check_status.sh
=============================================================================================================
  NODE     STATE                        CORES IN USE                           MEMORY USAGE     GPUS(Usage/Total)
=============================================================================================================
 hpc-fat1    up   0%[--------------------------------------------------]( 0/80) RAM:  0%     ---
 hpc-gpu1    up   2%[||------------------------------------------------]( 1/36) RAM: 47%   V100S (1/2)
 hpc-gpu2    up   2%[||------------------------------------------------]( 1/36) RAM: 47%   V100S (1/2)
 hpc-gpu3    up   0%[--------------------------------------------------]( 0/64) RAM:  0%   A100_40 (0/2)
 hpc-gpu4    up   1%[|-------------------------------------------------]( 1/64) RAM: 35%   A100_80 (1/1)
 hpc-node1   up   0%[--------------------------------------------------]( 0/36) RAM:  0%     ---
 hpc-node2   up   0%[--------------------------------------------------]( 0/36) RAM:  0%     ---
 hpc-node3   up   0%[--------------------------------------------------]( 0/48) RAM:  0%     ---
 hpc-node4   up   0%[--------------------------------------------------]( 0/48) RAM:  0%     ---
 hpc-node5   up   0%[--------------------------------------------------]( 0/48) RAM:  0%     ---
 hpc-node6   up   0%[--------------------------------------------------]( 0/48) RAM:  0%     ---
 hpc-node7   up   0%[--------------------------------------------------]( 0/48) RAM:  0%     ---
 hpc-node8   up   0%[--------------------------------------------------]( 0/48) RAM:  0%     ---
 hpc-node9   up   0%[--------------------------------------------------]( 0/48) RAM:  0%     ---
=============================================================================================================
TOTAL: [Cores : 3/688] [Mem(MB): 270000/3598464] [GPU: 3/ 7]
 
hpc-login2 ~]$ sinfo -e -o "%30N  %20c  %20m  %20f  %30G " --sort=N
# There is an alias for this command:
hpc-login2 ~]$ check_resources
NODELIST                        CPUS                  MEMORY                AVAIL_FEATURES        GRES                           
hpc-fat1                        80                    1027273               cpu_intel             (null)                         
hpc-gpu[1-2]                    36                    187911                cpu_intel             gpu:V100S:2                    
hpc-gpu3                        64                    253282                cpu_amd               gpu:A100_40:2                  
hpc-gpu4                        64                    253282                cpu_amd               gpu:A100_80:1(S:0)             
hpc-node[1-2]                   36                    187645                cpu_intel             (null)                         
hpc-node[3-9]                   48                    187645                cpu_intel             (null)
 
# To view current resource usage: (CPUS (Allocated/Idle/Other/Total))
hpc-login2 ~]$ sinfo -N -r -O NodeList,CPUsState,Memory,FreeMem,Gres,GresUsed
# There is an alias for this command:
hpc-login2 ~]$ check_usage
NODELIST            CPUS(A/I/O/T)       MEMORY              FREE_MEM            GRES                GRES_USED
hpc-fat1            80/0/0/80           1027273             900850              (null)              gpu:0,mps:0
hpc-gpu3            2/62/0/64           253282              226026              gpu:A100_40:2       gpu:A100_40:2(IDX:0-
hpc-gpu4            1/63/0/64           253282              244994              gpu:A100_80:1(S:0)  gpu:A100_80:1(IDX:0)
hpc-node1           36/0/0/36           187645              121401              (null)              gpu:0,mps:0
hpc-node2           36/0/0/36           187645              130012              (null)              gpu:0,mps:0
hpc-node3           36/12/0/48          187645              126739              (null)              gpu:0,mps:0
hpc-node4           36/12/0/48          187645              126959              (null)              gpu:0,mps:0
hpc-node5           36/12/0/48          187645              128572              (null)              gpu:0,mps:0
hpc-node6           36/12/0/48          187645              127699              (null)              gpu:0,mps:0
hpc-node7           36/12/0/48          187645              127002              (null)              gpu:0,mps:0
hpc-node8           36/12/0/48          187645              128182              (null)              gpu:0,mps:0
hpc-node9           36/12/0/48          187645              127312              (null)              gpu:0,mps:0

A node is the computing unit of SLURM and corresponds to a physical server.

# Show information about a node:
hpc-login2 ~]$ scontrol show node hpc-node1
NodeName=hpc-node1 Arch=x86_64 CoresPerSocket=18 
   CPUAlloc=0 CPUTot=36 CPULoad=0.00
   AvailableFeatures=cpu_intel
   ActiveFeatures=cpu_intel
   Gres=(null)
   NodeAddr=hpc-node1 NodeHostName=hpc-node1 Version=21.08.6
   OS=Linux 4.18.0-305.el8.x86_64 #1 SMP Wed May 19 18:55:28 EDT 2021 
   RealMemory=187645 AllocMem=0 FreeMem=166801 Sockets=2 Boards=1
   State=IDLE ThreadsPerCore=1 TmpDisk=0 Weight=1 Owner=N/A MCS_label=N/A
   Partitions=defaultPartition 
   BootTime=2022-03-01T13:13:56 SlurmdStartTime=2022-03-01T15:36:48
   LastBusyTime=2022-03-07T14:34:12
   CfgTRES=cpu=36,mem=187645M,billing=36
   AllocTRES=
   CapWatts=n/a
   CurrentWatts=0 AveWatts=0
   ExtSensorsJoules=n/s ExtSensorsWatts=0 ExtSensorsTemp=n/s

Partitions in SLURM are logical groups of nodes. There is only one partition in the cluster to which all nodes belong, so it is not necessary to specify it when submitting jobs.

# Show information about the partitions:
hpc-login2 ~]$ sinfo
defaultPartition*    up   infinite     11   idle hpc-fat1,hpc-gpu[3-4],hpc-node[1-9]
# When ctgpgpu7 and 8 are added to the cluster, they will appear as nodes hpc-gpu1 and 2 respectively.

Jobs in SLURM are resource assignments to a user during a specified time. Jobs are identified by a sequential number or JOBID.
A job (JOB) consists of one or more steps (STEPS), each consisting of one or more tasks (TASKS) that use one or more CPUs. There is one STEP for each program that runs sequentially in a JOB and one TASK for each program that runs in parallel. Therefore, in the simplest case, such as launching a job consisting of executing the hostname command, the JOB has a single STEP and a single TASK.

The queue to which each job is sent defines the priority, limits, and also the “relative cost” for the user.

# Show the queues
hpc-login2 ~]$ sacctmgr show qos
# There is an alias that shows only the most relevant information:
hpc-login2 ~]$ check_queues
      Name   Priority                        MaxTRES     MaxWall            MaxTRESPU MaxJobsPU MaxSubmitPU 
---------- ---------- ------------------------------ ----------- -------------------- --------- ----------- 
   regular        100      cpu=200,gres/gpu=1,node=4  4-04:00:00       cpu=200,node=4        10          50 
interacti+        200                         node=1    04:00:00               node=1         1           1 
    urgent        300              gres/gpu=1,node=1    04:00:00               cpu=36         5          15 
      long        100              gres/gpu=1,node=4  8-04:00:00                              1           5 
     large        100             cpu=200,gres/gpu=2  4-04:00:00                              2          10 
     admin        500                                                                                       
     small        100        cpu=6,gres/gpu=0,node=2  6-00:00:00              cpu=400        400         800 
     short        150                   cpu=6,node=2    04:00:00                              40         100 

# Priority: is the relative priority of each queue.
# DenyonLimit: the job does not run if it does not meet the queue limits.
# UsageFactor: the relative cost for the user of running a job in that queue.
# MaxTRES: limits per job.
# MaxWall: maximum time a job can run.
# MaxTRESPU: global limits per user.
# MaxJobsPU: Maximum number of jobs a user can have running.
# MaxSubmitPU: Maximum number of jobs a user can have queued and running at any time.

By default, if a job is submitted without specifying anything, the system sends it to the default QOS (regular) and assigns it a node, one CPU, and 4 GB of RAM. The time limit for job execution is that of the queue (4 days and 4 hours). This is very inefficient; ideally, you should specify at least three parameters when submitting jobs:

1. The number of nodes (-N or --nodes), tasks (-n or --ntasks), and/or CPUs per task (-c or --cpus-per-task).
2. The memory (--mem) per node or the memory per CPU (--mem-per-cpu).
3. The estimated execution time of the job (--time)

Additionally, it may be interesting to add the following parameters:

By default, the resource allocation method among nodes is block allocation (all available cores in a node are allocated before using another). The default allocation method within each node is cyclic allocation (the required cores are evenly distributed among the available sockets on the node).

When a job is submitted to the queue system, the first thing that happens is that it checks if the requested resources fall within the limits set in the corresponding queue. If it exceeds any limit, the submission is canceled.
If resources are available, the job runs immediately, but if not, it is queued. Each job has an assigned priority that determines the order in which jobs in the queue are executed when resources become available. The priority of each job is weighted by 3 factors: the time spent waiting in the queue (25%), the fixed priority of the queue (25%), and the user's fair share (50%).
The fair share is a dynamic calculation made by SLURM for each user and is the difference between the assigned resources and the resources consumed over the last 14 days.

hpc-login2 ~]$ sshare -l 
      User  RawShares  NormShares    RawUsage   NormUsage   FairShare 
---------- ---------- ----------- ----------- -----------  ---------- 
                         1.000000     2872400                0.500000 
                    1    0.500000     2872400    1.000000    0.250000 
user_name         100    0.071429        4833    0.001726    0.246436

# RawShares: is the amount of resources in absolute terms assigned to the user. It is the same for all users.
# NormShares: Is the previous amount normalized to the total assigned resources.
# RawUsage: Is the amount of seconds/cpu consumed by all the user's jobs.
# NormUsage: Previous amount normalized to the total of seconds/cpu consumed in the cluster.
# FairShare: The FairShare factor between 0 and 1. The more the cluster is used, the closer it will be to 0 and the lower the priority.

1. sbatch
2. salloc
3. srun

1. SBATCH
Used to submit a script to the queue system. It is batch processing and non-blocking.

# Create the script:
hpc-login2 ~]$ vim example_job.sh
    #!/bin/bash
    #SBATCH --job-name=test            # Job name
    #SBATCH --nodes=1                    # -N Run all processes on a single node   
    #SBATCH --ntasks=1                   # -n Run a single task   
    #SBATCH --cpus-per-task=1            # -c Run 1 processor per task       
    #SBATCH --mem=1gb                    # Job memory request
    #SBATCH --time=00:05:00              # Time limit hrs:min:sec
    #SBATCH --qos=urgent                 # Queue
    #SBATCH --output=test_%j.log       # Standard output and error log
 
    echo "Hello World!"
 
hpc-login2 ~]$ sbatch example_job.sh 

2. SALLOC
Used to immediately obtain a resource assignment (nodes). As soon as it is obtained, the specified command or a shell is executed by default.

# Get 5 nodes and launch a job.
hpc-login2 ~]$ salloc -N5 myprogram
# Get interactive access to a node (Press Ctrl+D to end access):
hpc-login2 ~]$ salloc -N1 
# Get exclusive interactive access to a node
hpc-login2 ~]$ salloc -N1 --exclusive

3. SRUN
Used to launch a parallel job (it is preferable to using mpirun). It is interactive and blocking.

# Launch a hostname on 2 nodes
hpc-login2 ~]$ srun -N2 hostname
hpc-node1
hpc-node2

To specifically request a GPU allocation for a job, you must add to sbatch or srun the options:

There are also options --gpus-per-socket, --gpus-per-node, and --gpus-per-task,
Examples:

## View the list of nodes and GPUs:
hpc-login2 ~]$ check_resources
## Request 2 arbitrary GPUs for a JOB, add:
--gpus=2
## Request one A100 of 40G on one node and one A100 of 80G on another, add:
--gres=gpu:A100_40:1,gpu:A100_80:1 

## List all jobs in the queue
hpc-login2 ~]$ squeue
## List the jobs of a user            
hpc-login2 ~]$ squeue -u <login>
## Cancel a job:
hpc-login2 ~]$ scancel <JOBID>
## List recent jobs
hpc-login2 ~]$ sacct -b
## Detailed historical information about a job:
hpc-login2 ~]$ sacct -l -j <JOBID>
## Debug information for a job for troubleshooting:
hpc-login2 ~]$ scontrol show jobid -dd <JOBID>
## View resource usage of a running job:
hpc-login2 ~]$ sstat <JOBID>

By default, these are the exit codes of the commands:

SRUN:
By default, stdout and stderr are redirected from all TASKS to the stdout and stderr of srun, and stdin is redirected from the stdin of srun to all TASKS. This can be changed with:

And the options are:

• all: default option.
• none: Nothing is redirected.
• taskid: Only redirects from/to the specified TASK id.
• filename: Redirects everything from/to the specified file.
• filename pattern: Same as filename but with a file defined by a pattern

SBATCH:
By default “/dev/null” is open in the script's stdin, and stdout and stderr are redirected to a file named “slurm-%j.out”. This can be changed with:

The filename_pattern reference is here .

Jobs can be configured to send emails under certain circumstances using these two parameters (BOTH ARE REQUIRED):

hpc-login2 ~]# squeue -l
JOBID PARTITION     NAME     USER      STATE       TIME  NODES NODELIST(REASON)
6547  defaultPa  example <username>  RUNNING   22:54:55      1 hpc-fat1
 
## View the usage state of the cluster's queues:
hpc-login2 ~]$ check_queues_status.sh
JOBS PER USER:
--------------
       user.one:  3
       user.two:  1
 
JOBS PER QOS:
--------------
             regular:  3
                long:  1
 
JOBS PER STATE:
--------------
             RUNNING:  3
             PENDING:  1
==========================================
Total JOBS in cluster:  4

Most common states (STATE) of a job:

• R RUNNING Job currently has an allocation.
• CD COMPLETED Job has terminated all processes on all nodes with an exit code of zero.
• F FAILED Job terminated with non-zero exit code or other failure condition.
• PD PENDING Job is awaiting resource allocation.

Full list of possible job states .

If a job is not running, a reason will appear below REASON: List of reasons a job may be waiting to execute.