TITANI CLUSTER MANUAL

This document is intended to give some basic notes in order to work with the TITANI High Performance Green Computing Cluster of the Civil Engineering School (ETSECCPB) located in the CaminsTECH facilities.

TITANI is a high performance green computer cluster formed by a set of Dell servers. It is a multitasking and multiuser environment configured with CentOS 7 Linux operating system and uses the SLURM open-source workload manager (slurm.schedmd.com). TITANI is oriented towards users that execute programs with high computing performance requirements of memory and processing time. The cluster has two main parts:

• A frontend/access server:
  • This is the server where users can launch their simulations (jobs), but they actually run on the computing servers. Every user has his personal workspace and useful tools such as compilers, editors, etc.
  • Name: titani-nx
• Six compute servers/nodes (5 nodes of 24 cores [c1…c5] and 1 node of 40 cores [c6]):
  • Every compute server executes submitted jobs from the fronted server.
  • Name: cN, where N is the number of the compute server (e.g. c1, c2…)

As a Green Computing Cluster, the compute servers are powered on while jobs are running (when a node is idle for 30 minutes it will be powered off). An overview of the architecture of TITANI and its characteristics is shown below:

In order to work with TITANI cluster, it is required that the user asks for an account with the UTGAC IT Services (https://caminstech.upc.edu/es/calculintensiu). Once the account is created, UPC users will be able to gain access to the service with their UPC username and password, otherwise a local username and password will be given. Connection to the server is only allowed from computers in Camins networks (buildings B0, B1, B2, C1, C2, D1, D2 and the computer rooms of the Civil Engineering School) or from anywhere using the UPCLink VPN client ( VPN UPC Manuals).

You can use any tool that supports Secure Shell (SSH) to connect to the service.

• In Linux almost all distributions have a preinstalled ssh client.
• In MacOSX there is a ssh client in Terminal.
• In Windows for example, use Putty (ssh client) or Mobaxterm (cygwin terminal with a SSH and SFTP client that includes Graphical Access).

Configure connection as follows:

To run programs using graphical output, such as Abaqus CAE or Matlab, use:

• Mobaxterm (cygwin terminal with a SSH and SFTP client)
  • http://mobaxterm.mobatek.net/
• x2go client
  • http://wiki.x2go.org/doku.php/doc:installation:x2goclient

Both options (terminal and graphical access) use the SSH protocol for secure connections.

Add a New Session with the parameters in the table below.

Your preferences screen should look like this:

Once you insert your username and password you will see your personal desktop:

In order to download/transfer files from/to TITANI, it is recommended to use a tool such as winSCP configuring it as follows.

TITANI Cluster is a CentOS 7 Linux operating system. These are some basic commands in a Linux environment (shell) that you will find useful when working on it.

For more detailed information about Linux Shell commands take a look at http://linuxcommand.org/lc3_learning_the_shell.php

Each user has a personal disk space to store files in TITANI. The user’s home directory has the following path:

/users/USERNAME 

It is recommended to use this space to store code, executable and data files. The initial disk quota assigned is 10 GB per user; however should you need to increase the amount of space, it is possible to request this from UTGAC IT Services (https://caminstech.upc.edu/es/on-trobar-nos). To check the available quota use the next command:

 > quota -s 

Additionally, every user has temporary disk space (scratch) available.

 /scratch/USERNAME 

This directory does not have any quota restrictions, for this reason, it is suggested to use this temporary scratch directory to run jobs sent to the batch queues and to store execution results. All compute nodes have access to this NFS mounted scratch directory. The amount of space available is 4.4 TB and it is shared by all users in the cluster, therefore it is requested that users consider others by not overloading the disk. In the case that a user occupies too much space and affects the jobs of others, the system administrator could delete files without notice. Please note: files in the scratch not changed in more than 32 days will be deleted automatically every day at 11 PM.

 /local_scratch 

When a job is sent to a batch queue it will run in one of the cluster nodes (c1-c6). Users can use the /local_scratch directory (450 GB of space physically allocated in every node disk). The local scratch space is a storage space unique to each individual node where a job is running and therefore disk access is faster than in home or scratch directories (NFS network disks). Using /local_scratch improves programs performance when they have a lot of input/output operations (a large amount of reading/writing files). To use this temporary directory it will be necessary for every user to create a folder inside /local_scratch directory and copy the files to run a job. Once the job finishes it is required to copy the result files back to the user home or scratch personal directory, as the local scratch directory will no longer be accessible. Don’t forget to include these steps in the shell script that is sent to the batch queue.

1. Data files should be copied from the home directory or personal scratch to the local scratch space.
2. Run the program.
3. Copy the output files to the original directory.

It is important to keep in mind that all users must remove excess files by themselves. Preferably this should be done within the user's batch job when the computation has finished. Leaving files in the local scratch directory could be an impediment to other users who are trying to run their jobs. Simply delete all files when the job ends. Example: The following script creates/deletes a temporary directory in /local_scratch using the username and the SLURM JOBID as the directory name, in this way it is possible to send more than one job using /local_scratch at the same time.

#!/bin/bash
MYSCRATCHDIR=/local_scratch/$USER/$SLURM_JOB_ID
mkdir -p $MYSCRATCHDIR
cp -r /users/$USER/sourcedir/* $MYSCRATCHDIR
cd $MYSCRATCHDIR
$MYSCRATCHDIR/program
cp -r $MYSCRATCHDIR /users/$USER/resultsdir
rm -rf $MYSCRATCHDIR

Fortran compilers installed:

C/C++ compilers installed:

Additionally installed, are the Intel Cluster Toolkit for Linux (version 3.2) and Intel HPC Toolkit (version 2021.1) which include the following tools:

• Intel MPI Library
• Intel Math Kernel Library
• Intel Trace Analyzer and Collector
• Intel MPI Benchmarks (IMB)
• Intel Debugger for Linux
• Intel VTune

NOTE: Remember to load your selected compiler software environment before to use it. (Section 5.4)

The Intel and GNU the MPI libraries (Message Passing Interface) are installed in TITANI through its OpenMPI implementation (see modules section to choose the version in use). These libraries are interfaces for parallel computing designed for programs that take advantage of multiple cores. The following link provides a user’s guide for MPI programming in Fortran. https://caminstech.upc.edu/sites/default/files/ManualMPIFortran.pdf

Use the following commands to compile a program prepared for parallelization with OpenMP:

GNU Compilers:

• GNU Fortran:

f95 –o program_name –fopenmp program_code.f

gfortran –o program_name –fopenmp program_code.f

• GNU C/C++:

gcc –o program_name –fopenmp program_code.c

g++ –o program_name –fopenmp program_code.cpp

Intel Compilers (Intel 14.0 - 2013 version):

• Intel Fortran:

 ifort –o program_name –openmp program_code.f 

• Intel C/C++:

icc –o program_name –openmp program_code.c

icpc –o program_name –openmp program_code.cpp

Intel Compilers (Intel OneAPI 2021.1 version):

• Intel Fortran:

 ifort –o program_name –qopenmp program_code.f90 

• Intel C++/DPC++:

icc –o program_name –qopenmp program_code.cpp

dpcpp –o program_name –qopenmp program_code.cpp

Use the following commands to compile a program prepared for parallelization with MPI:

GNU Compilers:

• GNU Fortran MPI:

mpif90 –o program_name program_code.f

mpifc –o program_name program_code.f

• GNU C/C++ MPI:

mpicc –o program_name program_code.c

mpicxx –o program_name program_code.cpp

Intel Compilers (all versions):

• Intel Fortran:

 mpiifort –o program_name program_code.f 

• Intel C/C++:

mpiicc –o program_name program_code.c

mpiicpc –o program_name program_code.cpp

Execute the following command to display more help regarding this commands:

 $ command -help 

TITANI uses the SLURM open-source workload manager to manage cluster resources (nodes, processors, cores, memory). It provides a queue management system oriented to send jobs for their execution and uses a job scheduler to allocate the simulations to the compute nodes. The job scheduler sends the job from the TITANI frontend server to any compute node depending on the instant cluster resource usage. When all resources are busy, newer jobs will wait until older jobs finish their processes. Therefore, to execute a job it is required to send it to a predefined batch queue according the program specific needs. The batch queues (called partitions in SLURM) are described below in detail as well as its maximum CPU run time, CPU maximum wall time, maximum number of cores allowed, maximum number of simultaneous jobs allowed to be executed or queued, memory limitations and other restrictions. Consider the following recommendations to send jobs to the queues:

• Each queue imposes restrictions per user for the limit of executing and pending jobs.
• It is forbidden to link jobs execution that means the same shell script executes one job after another executed before.
• Contact the UTG IT Services to ask for access to restricted queues in the particular case you need to increase the limits imposed for each queue.

Queues’ characteristics could be modified regarding its maximum number of jobs and the memory limits according the users’ needs, the computers’ workload, among other circumstances, therefore it is recommended to consult the following link to have the most up to date characteristics:

http://caminstech.upc.edu/faq/titani

There are two queues (partitions) defined in TITANI: serial and parallel.

• SERIAL queue: This is the default queue and it only allows to submit serial jobs, namely that only use a core in their execution. Every user can run 15 jobs simultaneously and has 10 pending jobs.
• PARALLEL queue: This queue only allows to submit parallel jobs that is to say that use more than a core in their execution. Every user can run 3 jobs simultaneously and has 3 pending jobs. It is allowed to use 48 cores per user, in a job or in total with all his jobs.
• EXPRESS queue: This queue only allows to submit fast parallel jobs (maximum 24 hours in the queue) that is to say that use more than a core in their execution. Every user can run 6 jobs simultaneously and has 6 pending jobs. It is allowed to use 48 cores per user, in a job or in total with all his jobs.

The Serial queue uses C3, C4, C5 and C6 computing nodes (112 cores) and a maximum of 36 cores to execute their jobs. The Parallel queue uses C2, C3, C4, C5 and C6 computing nodes (136 cores) and a maximum of 112 cores can be used. The Express queue uses C1, C2, C4, C5 and C6 computing nodes (136 cores) and a maximum of 96 cores can be used.

The following table reflects a summary of the characteristics of each queue. This information has been retrieved by the time this tutorial has been created, consult the most up to date information at the link mentioned before.

As mentioned before to run simulations at TITANI is necessary to send jobs to the computing queues of the cluster to use the compute nodes. In fact, it is not allowed to execute high CPU usage on the frontend server because it has not enough resources to do theses taks. The sbatch command is used to send jobs to a defined queue of TITANI. It is required to make a shell script. This script would look like the following:

   
#!/bin/bash
<path>/program_to_execute

Where <path> is the full name of the directory where the program to run is. Use the following syntax:

 > sbatch –p queue_name <shell_script_name> 

In the directory where the sbatch command was launched, SLURM creates one output file called slurm-<JOBID>.out containing all output and errors of the execution. <JOBID> is the own ID of each sent job or job identifier.

By default, the sbatch command sends a job to the serial queue and asks to the job scheduler for 2048MB of RAM memory. If you need more memory resources you can use this flag:

  --mem <amount_of_RAM(MB)> 

Please, do not ask for more memory than needed.

Example: submit a job myscript.sh asking for a machine 10GB of RAM.

 sbatch [-p serial] –-mem 10000 myscript.sh

It is also possible and very useful to specify the job parameters inside the shell script. These are the most used:

#!/bin/bash

#SBATCH --partition=<name_of_the_queue>
#SBATCH --job-name=<name_for_the_job>
#SBATCH --output=<output_filename.out>
#SBATCH --error=<error_filename.err>
#SBATCH --mem=<amount_of_RAM(MB)>
#SBATCH --cpus-per-task=<num_of_threads> # OMP programs
#SBATCH --nodes=<num_of_nodes> # MPI programs #SBATCH --ntasks-per-node=<num_of_cores> # MPI programs 

In order to send jobs for parallel execution, namely using more than one core, it must be used the parallel or express queue.

The command used would be the following syntax:

> sbatch –p parallel <parallel_script_name>

By default, the sbatch command asks to the job scheduler for 2048MB of RAM memory per node. If you need more memory resources you can use this flag:

--mem <amount_of_RAM_per_node(MB)>

> sbatch –p parallel –c <num_threads> omp_script
> sbatch –p parallel –-cpus-per-task=<num_threads> omp_script

A omp_script file would be for instance:

#!/bin/bash
<path>/omp_program_to_execute

Example: OMP program using 4 threads and 12 GB of RAM in the parallel queue:

> sbatch –p parallel –c 4 –-mem=12000 omp_script

Example: OMP program using 4 threads to the parallel queue specifying the parameters in the shell script omp_script:

#!/bin/bash
#SBATCH --partition=parallel
#SBATCH --mem=8000
#SBATCH --cpus-per-task=4
<path>/omp_program_to_execute

Shell command:

 > sbatch omp_script 

> sbatch –p parallel –-ntasks-per-node=<num_of_cores> mpi_script
> sbatch –p parallel -–nodes=<num_of_nodes> 
                     –-ntasks-per-node=<num_of_cores> mpi_script

A mpi_script file would be for instance:

#!/bin/bash
mpirun <path>/mpi_program_to_execute

Example: MPI program using 8 cores in the parallel queue:

> sbatch –p parallel –-ntasks-per-node=8 mpi_script 

Example: MPI program using 8 cores in the parallel queue specifying the parameters in the shell script mpi_script:

#!/bin/bash
#SBATCH --partition=parallel
#SBATCH –-ntasks-per-node=8
mpirun <path>/mpi_program_to_execute

Shell command:

> sbatch mpi_script

Example: MPI program using 2 nodes and 4 cores per node in the parallel queue:

> sbatch –p parallel –-nodes=2 –-ntasks-per-node=4 mpi_script 

Example: MPI program using 2 nodes: 4 cores and 4 GB per node in the parallel queue specifying the parameters in the shell script:

#!/bin/bash
#SBATCH --partition=parallel
#SBATCH --nodes=2
#SBATCH --mem=4000 #SBATCH –-ntasks-per-node=4

mpirun <path>/mpi_program_to_execute

> sbatch mpi_script

The status of the service is shown by main commands sinfo and squeue:

> sinfo
PARTITION AVAIL  TIMELIMIT  NODES  STATE NODELIST
serial*	   up     infinite    3    idle~ c[3-5]
serial*	   up     infinite    1    mix 	 c2
serial*	   up     infinite    1    alloc c1
parallel   up	  infinite    3	   idle~ c[3-5]
parallel   up 	  infinite    1    mix 	 c2
parallel   up	  infinite    1    alloc c1

This command shows the state of the compute nodes, in this example:

Node c1 is powered on and has been fully allocated. Node c2 is getting started and has been allocated by one or more jobs (but not fulfilled). Nodes c3, c4 and c5 are idle and in power saving mode. Note that “~” means that is powered off (energy saving mode) and “#” that is powering on. To show the current job list use the command squeue:

> squeue
JOBID PARTITION     NAME       USER       ST   TIME     NODES NODELIST(REASON)
621   parallel	  bucleINT    mike.smith  R    18:28:08	  1	c1
622   parallel	  IntelMPI    david.garc  R    18:25:48	  1	c1
625   serial 	  bucleCua    jack.sparr  R    18:20:26	  1 	c2

State “R” means that the job is running. Sometimes powered off nodes must to be started in order to process a job, the average up time is about 5 minutes so the state of the job is “CF” (configuring). Please be patient.

To show complete information of your jobs (running or not) use the following command:

> scontrol show job <JOBID>

Example:

> scontrol show job 622
JobId=622 JobName=IntelMPICua.sh
   UserId=david.garcia(10104) GroupId=users(1000)
   Priority=4294901708 Nice=0 Account=users QOS=normal
   JobState=RUNNING Reason=None Dependency=(null)
   Requeue=1 Restarts=0 BatchFlag=1 Reboot=0 ExitCode=0:0
   RunTime=18:34:02 TimeLimit=3-12:00:00 TimeMin=N/A
   SubmitTime=2016-05-18T13:58:36
   EligibleTime=2016-05-18T13:58:36
   StartTime=2016-05-18T13:58:37 EndTime=2016-05-22T01:58:37
   PreemptTime=None SuspendTime=None SecsPreSuspend=0
   Partition=parallel AllocNode:Sid=titani-nx:29074
   ReqNodeList=(null) ExcNodeList=(null)
   NodeList=c1 
   BatchHost=c1
   NumNodes=1 NumCPUs=12 CPUs/Task=12 ReqB:S:C:T=0:0:*:*
   TRES=cpu=12,mem=24576,node=1
   Socks/Node=* NtasksPerN:B:S:C=0:0:*:* CoreSpec=*
   MinCPUsNode=12 MinMemoryCPU=2G MinTmpDiskNode=0
   Features=(null) Gres=(null) Reservation=(null)
   Shared=OK Contiguous=0 Licenses=(null) Network=(null)
   Command=/users/david.garcia/programs/IntelMPICua.sh
   WorkDir=/users/david.garcia/programs
   StdErr=/users/david.garcia//programs/slurm-622.out
   StdOut=/users/david.garcia//programs/slurm-622.out

The command to cancel a job is scancel:

> scancel <JOBID>

As seen, different applications and compilers are installed into the TITANI cluster. Initially, when a user logs in the system Intel Compilers and MPI environment configurations are loaded and ready to be used by default.

In the future if new software versions that require new environment variables are installed it may be necessary to load new configuration profiles.

You can list the available configurations with this command:

> module avail
----------------------------------------------------------------------------- /etc/modulefiles ---------------------------------------------------------------------------------
cmake/3.2.1     cmake/3.6.2     gcc/6.2.0      gcc/7.4.0      intel/Composer-2013   intel/mpi-2013    mpi/openmpi-2.0.1   mpi/openmpi-4.0.4   mpi/openmpi-x86_64  openfoam/v1812

You can load one of them:

> module load gcc/6.2.0

You can back to the initial environment:

> module purge

The best option to load the Intel OneAPI environment is by using the following command:

> source intel-oneapi.sh

Note that it is not possible to go back to the initial environment without closing the bash session.

Listed below are the scientific programs installed in TITANI and the basic user manual to send jobs to the queues.

Abaqus is a calculation program for finite elements analysis. It is oriented to solve problems such as solid mechanics, linear and nonlinear problems in static and dynamic ranges.

Abaqus/Standard and Abaqus/Explicit are available for the analysis and Abaqus/CAE for data pre-processing and post-processing. Abaqus licenses are limited, for that reason we encourage to make responsible and moderate use of them. It is recommended not to send more than two jobs per user using Abaqus/Standard and Abaqus/Explicit, and just one job for Abaqus/CAE. If a user exceeds these limits, the system administrator is allowed to delete one of them without any previous warning.

To execute Abaqus/CAE and have a visualization or post processing of the obtained data, it is required a Graphical Acces (see section 2.2) to get the visualization window. Abaqus/CAE command:

> abaqus cae -mesa

To check the available licenses at any time you can use the following command:

> abaqus-licenses

To send an Abaqus job to the serial queue is necessary to use a shell script like this:

#!/bin/bash
unset SLURM_GTIDS
abaqus job=job_name input=input_filename.inp interactive

Shell command:

> sbatch [-p serial] <abaqus_script_name>

To execute Abaqus 2020 version the command abaqus2020 should be used instead of abaqus.

Available versions:

To send to the parallel queue an Abaqus job that uses more than one core is necessary to use a shell script like this:

#!/bin/bash
#SBATCH --cpus-per-task=<NumThreads>
unset SLURM_GTIDS
abaqus job=name input=filename.inp cpus=<NumThreads> interactive

Shell command:

> sbatch -p parallel <abaqus_script_name>

MATLAB is a mathematic software that offers its own programming language, M language, integrated with its development environment. A shell script is required to send jobs to the batch queues. This shell script must have the following format, where program.m would be the filename of the Matlab file to execute:

#!/bin/bash
echo "Running on host: " `hostname`
echo "Starting MATLAB at `date`"

matlab -nodesktop -nodisplay -nosplash < program.m > program.out
echo "MATLAB run completed at `date`"

Shell command:

> sbatch [-p serial] <matlab_script_name>

To send to the parallel queue a Matlab job that uses multithreads is necessary to create a shell script specifying the number of threads:

#!/bin/bash
#SBATCH --cpus-per-task=<number_of_threads>
echo "Running on host: " `hostname`
echo "Starting MATLAB at `date`"

matlab -nodesktop -nodisplay -nosplash < program.m > program.out
echo "MATLAB run completed at `date`"

Shell command:

> sbatch -p parallel <matlab_script_name>

The Parallel Computing Toolbox is available on TITANI. It is possible to use up to 24 workers for shared parallel operations on a single node in the current MATLAB version. Our license does not include MATLAB Distributed Computing Server. Therefore, multi-node parallel operations are not supported.

To send to the parallel queue a Matlab job that uses workers (parpool function) is necessary to create a shell script as shown below:

#!/bin/bash
#SBATCH --ntasks-per-node=6 =<number_of_cores>
echo "Running on host: " `hostname`
echo "Starting MATLAB at `date`"

matlab -nodesktop -nodisplay -nosplash < program.m > program.out
echo "MATLAB run completed at `date`"

Shell command:

> sbatch -p parallel <matlab_script_name> 

OpenFOAM is the free, open source CFD software released and developed primarily by OpenCFD Ltd since 2004. It has a large user base across most areas of engineering and science, from both commercial and academic organisations. OpenFOAM has an extensive range of features to solve anything from complex fluid flows involving chemical reactions, turbulence and heat transfer, to acoustics, solid mechanics and electromagnetics.

Available versions:

We need to prepare OpenFOAM environment before to use it. It is necessary to load a module and to source a script to set the needed variables. This is the example to load version v1812:

$ module load openfoam/v1812
$ source $OPENFOAM_SETUP

It is possible to configure an alias in your .bashrc to do it in a single command:

echo "alias of1812='module load openfoam/v1812; source \$OPENFOAM_SETUP'" >>$HOME/.bashrc

In this case, just login again and type of1812 to be ready to use OpenFOAM.

A shell script is required to send jobs to the batch queues. This shell script must have the following format:

#!/bin/bash
echo "Running on host: " `hostname`
echo "Starting OpanFOAM at `date`"

module load openfoam/v1812
source $OPENFOAM_SETUP

cd <openfoam-working-directory i.e: /users/username/OpenFOAM/username-v1812/run/pitzDaily/>
blockMesh > mesh.out #Use here your OpenFOAM needed commands
simpleFoam > output.out
echo "OpenFOAM run completed at `date`"

Shell command:

> sbatch -p serial <openfoam_script_name> 

OpenFOAM can also be run in parallel, but needs further configuration. This is an example to send a job to the parallel queue:

#!/bin/bash
#SBATCH --nodes=1
#SBATCH --ntasks=5
echo "Running on host: " `hostname`
echo "Starting OpanFOAM at `date`"

module load openfoam/v1812
source $OPENFOAM_SETUP

cd /users/username/OpenFOAM/username-v1812/run/mesh/parallel/cavity
mpirun -np 5 Allrun > output.out
echo "OpenFOAM run completed at `date`"

Shell command:

> sbatch -p parallel <openfoam_script_name>