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= | = Clusters Ada and Lovelace - Instituto de Física UFRGS = | ||
The clusters are located at Instituto de Física da UFRGS, in Porto Alegre. | |||
=== Software | == Infraestruture == | ||
=== Management Software === | |||
<pre> | <pre> | ||
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</pre> | </pre> | ||
=== Hardware | === Hardware in the ada nodes === | ||
<pre> | <pre> | ||
CPU: x86_64 | CPU: x86_64 | ||
RAM: | RAM: varies between 8 GB - 16 GB | ||
GPU: | GPU: some nodes have NVIDIA CUDA | ||
Storage: storage | Storage: storage with 50GB quota per user | ||
</pre> | </pre> | ||
=== | === Hardware in the lovelace nodes === | ||
<pre> | <pre> | ||
OS: Debian 8 ( | CPU: Ryzen (32 and 2*24 cores) | ||
RAM: varia entre 64 GB | |||
GPU: two nodes have NVIDIA CUDA | |||
Storage: storage with 50GB quota per user | |||
</pre> | |||
=== Software in the nodes === | |||
<pre> | |||
OS: Debian 8 (in cluster ada) | |||
OS: Debian 11 (in cluster lovelace) | |||
Basic packages installed: | |||
GCC | |||
gfortran | |||
python2 | python2 | ||
python3 | python3 | ||
</pre> | </pre> | ||
== | == How to use == | ||
=== | === Conect to cluster-slurm === | ||
The clusters are accessible through server cluster-slurm.if.ufrgs.br (ou ada.if.ufrgr.br). To access through a unix-like system use: | |||
<pre> | <pre> | ||
ssh | ssh <user>@cluster-slurm.if.ufrgs.br | ||
</pre> | </pre> | ||
or | |||
<pre> | <pre> | ||
ssh | ssh <user>@ada.if.ufrgs.br | ||
</pre> | </pre> | ||
Under windows you may use winscp. | |||
If you are not registered, ask for registration sending an email to fisica-ti@ufrgs.br | |||
=== Using softwares in the cluster === | |||
To execute a software in a cluster job this program must: | |||
1. Be already installed | |||
OU | OU | ||
2. | 2. Be copied to the user home | ||
Ex: | Ex: | ||
<pre> | <pre> | ||
scp | scp my_programm <user>@cluster-slurm.if.ufrgs.br:~/ | ||
</pre> | </pre> | ||
If you are compiling your program in the cluster, one option is to user <code>gcc</code>. | |||
Ex: | Ex: | ||
<pre> | <pre> | ||
scp -r source-code/ usuario@cluster-slurm.if.ufrgs.br:~/ | scp -r source-code/ usuario@cluster-slurm.if.ufrgs.br:~/ | ||
ssh | ssh <user>@cluster-slurm.if.ufrgs.br:~/ | ||
cd source-code | cd source-code | ||
gcc main.c funcoes.c | gcc main.c funcoes.c | ||
</pre> | </pre> | ||
This will generate file <code>a.out</code>, which is the executable. | |||
Being accessible by methods 1 or 2, the program can be executed in the cluster through one <strong>JOB</strong>. | |||
OBS: | OBS: If you execute your executable without submitting as <strong>JOB</strong>, it will be executed in the server, not in the nodes. This is not recommended since the server computational capabilities are limited and you will be slowing down the server for everyone else. | ||
=== Criating and executing a Job === | |||
Slurm manages jobs and each job represents a program or task being executed. | |||
To submit a new job, you must create a script file describing the requisites and characteristics of the Job. | |||
A typical example of the content of a submission script is below | |||
Ex: <code>job.sh</code> | Ex: <code>job.sh</code> | ||
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<pre> | <pre> | ||
#!/bin/bash | #!/bin/bash | ||
#SBATCH -n 1 # | #SBATCH -n 1 # Number of cpus to be allocated (Despite the # these SBATCH lines are compiled by the slurm manager!) | ||
#SBATCH -N 1 # | #SBATCH -N 1 # Nummber of nodes to be allocated (You don't have to use all requisites, comment with ##) | ||
#SBATCH -t 0-00:05 # | #SBATCH -t 0-00:05 # Limit execution time (D-HH:MM) | ||
#SBATCH -p long # | #SBATCH -p long # Partition to be submitted | ||
#SBATCH --qos qos_long # QOS | #SBATCH --qos qos_long # QOS | ||
# | # Your program execution commands | ||
./a.out | ./a.out | ||
</pre> | </pre> | ||
In option --qos, use the partition name with "qos_" prefix: | |||
partition: short -> qos: qos_short -> limit 2 weeks | |||
partition: long -> qos: qos_long -> limit de 3 month | |||
If you run on GPU, specify the "generic resource" gpu in cluster ada: | |||
<pre> | <pre> | ||
#!/bin/bash | #!/bin/bash | ||
#SBATCH -n 1 | #SBATCH -n 1 | ||
#SBATCH -N 1 | #SBATCH -N 1 | ||
#SBATCH -t 0-00:05 | #SBATCH -t 0-00:05 | ||
#SBATCH -p long | #SBATCH -p long | ||
#SBATCH --qos qos_long # QOS | #SBATCH --qos qos_long # QOS | ||
#SBATCH --gres=gpu:1 | #SBATCH --gres=gpu:1 | ||
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</pre> | </pre> | ||
To ask for a specific gpu: | |||
<pre> | <pre> | ||
#SBATCH --constraint="gtx970" | #SBATCH --constraint="gtx970" | ||
</pre> | </pre> | ||
To submit the job, execute: | |||
<pre> | <pre> | ||
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</pre> | </pre> | ||
== | == Usefull commands == | ||
* | * To list jobs: | ||
squeue | squeue | ||
* | * To list all jobs running in the cluster now: | ||
sudo squeue | sudo squeue | ||
* | * To delete a running job: | ||
scancel [job_id] | scancel [job_id] | ||
* | * To list available partitions: | ||
sinfo | sinfo | ||
* | * To list gpu's in the nodes: | ||
sinfo -o "%N %f" | sinfo -o "%N %f" | ||
* | * To list characteristic of all nodes: | ||
sinfo -Nel | sinfo -Nel | ||
Edição das 15h48min de 14 de março de 2022
Clusters Ada and Lovelace - Instituto de Física UFRGS
The clusters are located at Instituto de Física da UFRGS, in Porto Alegre.
Infraestruture
Management Software
Slurm Workload Manager Site :https://slurm.schedmd.com/
Hardware in the ada nodes
CPU: x86_64 RAM: varies between 8 GB - 16 GB GPU: some nodes have NVIDIA CUDA Storage: storage with 50GB quota per user
Hardware in the lovelace nodes
CPU: Ryzen (32 and 2*24 cores) RAM: varia entre 64 GB GPU: two nodes have NVIDIA CUDA Storage: storage with 50GB quota per user
Software in the nodes
OS: Debian 8 (in cluster ada) OS: Debian 11 (in cluster lovelace) Basic packages installed: GCC gfortran python2 python3
How to use
Conect to cluster-slurm
The clusters are accessible through server cluster-slurm.if.ufrgs.br (ou ada.if.ufrgr.br). To access through a unix-like system use:
ssh <user>@cluster-slurm.if.ufrgs.br
or
ssh <user>@ada.if.ufrgs.br
Under windows you may use winscp.
If you are not registered, ask for registration sending an email to fisica-ti@ufrgs.br
Using softwares in the cluster
To execute a software in a cluster job this program must:
1. Be already installed
OU
2. Be copied to the user home
Ex:
scp my_programm <user>@cluster-slurm.if.ufrgs.br:~/
If you are compiling your program in the cluster, one option is to user gcc.
Ex:
scp -r source-code/ usuario@cluster-slurm.if.ufrgs.br:~/ ssh <user>@cluster-slurm.if.ufrgs.br:~/ cd source-code gcc main.c funcoes.c
This will generate file a.out, which is the executable.
Being accessible by methods 1 or 2, the program can be executed in the cluster through one JOB.
OBS: If you execute your executable without submitting as JOB, it will be executed in the server, not in the nodes. This is not recommended since the server computational capabilities are limited and you will be slowing down the server for everyone else.
Criating and executing a Job
Slurm manages jobs and each job represents a program or task being executed.
To submit a new job, you must create a script file describing the requisites and characteristics of the Job.
A typical example of the content of a submission script is below
Ex: job.sh
#!/bin/bash #SBATCH -n 1 # Number of cpus to be allocated (Despite the # these SBATCH lines are compiled by the slurm manager!) #SBATCH -N 1 # Nummber of nodes to be allocated (You don't have to use all requisites, comment with ##) #SBATCH -t 0-00:05 # Limit execution time (D-HH:MM) #SBATCH -p long # Partition to be submitted #SBATCH --qos qos_long # QOS # Your program execution commands ./a.out
In option --qos, use the partition name with "qos_" prefix:
partition: short -> qos: qos_short -> limit 2 weeks
partition: long -> qos: qos_long -> limit de 3 month
If you run on GPU, specify the "generic resource" gpu in cluster ada:
#!/bin/bash #SBATCH -n 1 #SBATCH -N 1 #SBATCH -t 0-00:05 #SBATCH -p long #SBATCH --qos qos_long # QOS #SBATCH --gres=gpu:1 # Comandos de execução do seu programa: ./a.out
To ask for a specific gpu:
#SBATCH --constraint="gtx970"
To submit the job, execute:
sbatch job.sh
Usefull commands
- To list jobs:
squeue
- To list all jobs running in the cluster now:
sudo squeue
- To delete a running job:
scancel [job_id]
- To list available partitions:
sinfo
- To list gpu's in the nodes:
sinfo -o "%N %f"
- To list characteristic of all nodes:
sinfo -Nel
