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ÚFAL Grid Engine (LRC)

LRC (Linguistic Research Cluster) is the name of ÚFAL's computational grid/cluster. The cluster is built on top of SLURM and is using Lustre for data storage.

See Milan Straka's intro to Slurm (and Spark if you want):

Currently there are following partitions (queues) available for computing:

Node list by partitions

The naming convention is straightforward for CPU nodes - nodes in each group are numbered. For GPU nodes the format is: [t]dll-XgpuN where X gives total number of GPUs equipped and N is just enumerating the order of the node with the given configuration.
The prefix t is for nodes at Troja and dll stands for Deep Learning Laboratory.


Node name Thread count Socket:Core:Thread RAM (MB)
achilles[1-8] 32 2:8:2 128810
hector[1-8] 32 2:8:2 128810
helena[1-8] 32 2:8:2 128811
paris[1-8] 32 2:8:2 128810
hyperion[2-8] 64 2:16:2 257667


Node name Thread count Socket:Core:Thread RAM (MB)
iridium 16 2:4:2 515977
orion[1-8] 40 2:10:2 128799


Node name Thread count Socket:Core:Thread RAM (MB) Features GPU type
tdll-3gpu[1-4] 64 2:16:2 128642 gpuram48G gpu_cc8.6 NVIDIA A40
tdll-8gpu[1,2] 64 2:16:2 257666 gpuram40G gpu_cc8.0 NVIDIA A100
tdll-8gpu[3-7] 32 2:8:2 253725 gpuram16G gpu_cc7.5 NVIDIA Quadro P5000


Node name Thread count Socket:Core:Thread RAM (MB) Features GPU type
dll-3gpu[1-5] 64 2:16:2 128642 gpuram48G gpu_cc8.6 NVIDIA A40
dll-4gpu[1,2] 40 2:10:2 187978 gpuram24G gpu_cc8.6 NVIDIA RTX 3090
dll-4gpu3 62 1:32:2 515652 gpuram48G gpu_cc8.9 NVIDIA L40
dll-4gpu4 30 1:16:2 257616 gpuram48G gpu_cc8.6 NVIDIA A40
dll-8gpu[1,2] 64 2:16:2 515838 gpuram24G gpu_cc8.0 NVIDIA A30
dll-8gpu[3,4] 32 2:8:2 257830 gpuram16G gpu_cc8.6 NVIDIA RTX A4000
dll-8gpu[5,6] 40 2:10:2 385595 gpuram16G gpu_cc7.5 NVIDIA Quadro RTX 5000
dll-10gpu1 32 2:8:2 257830 gpuram16G gpu_cc8.6 NVIDIA RTX A4000
dll-10gpu[2,3] 32 2:8:2 257830 gpuram11G gpu_cc6.1 NVIDIA GeForce GTX 1080 Ti

Submit nodes

In order to submit a job you need to login to one of the head nodes:


Basic usage

Batch mode

The core idea is that you write a batch script containing the commands you wish to run as well as a list of SBATCH directives specifying the resources or parameters that you need for your job.
Then the script is submitted to the cluster with:

sbatch myJobScript.sh

Here is a simple working example:

#SBATCH -J helloWorld					  # name of job
#SBATCH -p cpu-troja					  # name of partition or queue (default=cpu-troja)
#SBATCH -o helloWorld.out				  # name of output file for this submission script
#SBATCH -e helloWorld.err				  # name of error file for this submission script

# run my job (some executable)
sleep 5
echo "Hello I am running on cluster!"

After submitting this simple code you should end up with the two files (helloWorld.out and helloWorld.err) in the directory where you called the sbatch command.

Here is the list of other useful SBATCH directives:

#SBATCH -D /some/path/                        # change directory before executing the job   
#SBATCH -N 2                                  # number of nodes (default 1)
#SBATCH --nodelist=node1,node2...             # execute on *all* the specified nodes (and possibly more)
#SBATCH --cpus-per-task=4                     # number of cores/threads per task (default 1)
#SBATCH --gres=gpu:1                          # number of GPUs to request (default 0)
#SBATCH --mem=10G                             # request 10 gigabytes memory (per node, default depends on node)

If you need you can have slurm report to you:

#SBATCH --mail-type=begin        # send email when job begins
#SBATCH --mail-type=end          # send email when job ends
#SBATCH --mail-type=fail         # send email if job fails
#SBATCH --mail-user=<YourUFALEmailAccount>

As usuall the complete set of options can be found by typing:

man sbatch

Rudolf's template

The main point is for log files to have the job name and job id in them automatically.

#SBATCH -o %x.%j.out
#SBATCH -e %x.%j.err
#SBATCH -p gpu-troja
#SBATCH --gres=gpu:1
#SBATCH --mem=16G
#SBATCH --constraint="gpuram16G|gpuram24G"

# Print each command to STDERR before executing (expanded), prefixed by "+ "
set -o xtrace

Inspecting jobs

In order to inspect all running jobs on the cluster use:


filter only jobs of user linguist:

squeue -u linguist

filter only jobs on partition gpu-ms:

squeue -p gpu-ms

filter jobs in specific state (see man squeue for list of valid job states):

squeue -t RUNNING

filter jobs running on a specific node:

squeue -w dll-3gpu1

Cluster info

The command sinfo can give you useful information about nodes available in the cluster. Here is a short list of some examples:

List available partitions(queues). The default partition is marked with *:


List detailed info about nodes:

sinfo -l -N

List nodes with some custom format info:

sinfo -N -o "%N %P %.11T %.15f"

CPU core allocation

The minimal computing resource in SLURM is one CPU core. However, CPU count advertised by SLURM corresponds to the number of CPU threads.
If you ask for 1 CPU core with


SLURM will allocate all threads of 1 CPU core.

For example dll-8gpu1 will allocate 2 threads since its ThreadsPerCore=2:

$> scontrol show node dll-8gpu1
$ scontrol show node dll-8gpu1
NodeName=dll-8gpu1 Arch=x86_64 CoresPerSocket=16 
   CPUAlloc=0 CPUTot=64 CPULoad=0.05                                               // CPUAlloc - allocated threads, CPUTot - total threads
   NodeAddr= NodeHostName=dll-8gpu1 Version=21.08.8-2
   OS=Linux 5.15.35-1-pve #1 SMP PVE 5.15.35-3 (Wed, 11 May 2022 07:57:51 +0200) 
   RealMemory=515838 AllocMem=0 FreeMem=507650 Sockets=2 Boards=1
   CoreSpecCount=1 CPUSpecList=62-63                                               // CoreSpecCount - cores reserved for OS, CPUSpecList - list of threads reserved for system
   State=IDLE ThreadsPerCore=2 TmpDisk=0 Weight=1 Owner=N/A MCS_label=N/A          // ThreadsPerCore - count of threads for 1 CPU core
   BootTime=2022-09-01T14:07:50 SlurmdStartTime=2022-09-02T13:54:05
   CurrentWatts=0 AveWatts=0
   ExtSensorsJoules=n/s ExtSensorsWatts=0 ExtSensorsTemp=n/s

In the example above you can see comments at all lines relevant to CPU allocation.


When running srun or sbatch, you can pass -q high/normal/low/preempt-low. These represent priorities 300/200/100/100, with normal (200) being the default. Furthermore, the preempt-low QOS is actually preemptible – if there is a job with normal or high QOS, they can interrupt your preempt-low job.

The preemption has probably not been used by anyone yet; some documentation about it is on https://slurm.schedmd.com/preempt.html, we use the REQUEUE regime (so your job is killed, very likely with some signal, so you could monitor it and for example save a checkpoint; but currently I do not know any details), and then started again when there are resources.

Interactive mode

This mode can be useful for testing You should be using batch mode for any serious computation.
You can use srun command to get an interactive shell on an arbitrary node from the default partition (queue):

srun --pty bash

There are many more parameters available to use. For example:

To get an interactive CPU job with 64GB of reserved memory:

srun -p cpu-troja,cpu-ms --mem=64G --pty bash

To get interactive job with a single GPU of any kind:

srun -p gpu-troja,gpu-ms --gres=gpu:1 --pty bash
srun -p gpu-troja,gpu-ms --nodelist=tdll-3gpu1 --mem=64G --gres=gpu:2 --pty bash
srun -p gpu-troja --constraint="gpuram48G|gpuram40G" --mem=64G --gres=gpu:2 --pty bash

Unexpected Behavior of srun -c1
When you execute a command using srun and pass -c1 like

srun -c1 date

then the command is actually executed twice in parallel. To avoid it, you have to either remove the -c1 or also add explicit -n1.

Delete Job

scancel <job_id> 
scancel -n <job_name> 

To see all the available options type:

man scancel

Basic commands on cluster machines

  # is any such hardware there?
  # more details, incl. running processes on the GPU
  # nvidia-* are typically located in /usr/bin
watch nvidia-smi
  # For monitoring GPU activity in a separate terminal (thanks to Jindrich Libovicky for this!)
  # You can also use nvidia-smi -l TIME
nvcc --version
  # this should tell CUDA version
  # nvcc is typically installed in /usr/local/cuda/bin/
  # dela to vubec neco uzitecneho? :-)
  # theano-* are typically located in /usr/local/bin/
  # shows CUDA capability etc.
ssh dll1; ~popel/bin/gpu_allocations
  # who occupies which card on a given machine

See also


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