PlaFRIM Users Documentation

All nodes are connected through a 10Gbit/s Ethernet network that may also be used for the BeeGFS storage. The only exception are kona nodes since they only have 1Gbit/s Ethernet.

Additional HPC networks are available between some nodes:

• OmniPath 100Gbit/s (3 separate networks):

  • bora nodes (and devel[01-02]), also used for BeeGFS storage.

  

  • miriel[01-43] (and devel03).

  

  • sirocco[07-17] and all kona only

• Mellanox InfiniBand HDR 200Gbit/s between diablo[01-09].

  

• InfiniBand QDR 40Gbit/s between all miriel nodes and sirocco[01-06] (and devel03).

  

  Beware that miriel and devel03 have TrueScale/InfiniPath hardware that requires its own software stack (PSM) for best performance, while Mellanox InfiniBand in sirocco requires the usual Verbs library. These technologies are compatible but the performance will be suboptimal between miriel and sirocco.

CPU

2x 18-core Cascade Lake Intel Xeon Skylake Gold 6240 @ 2.6 GHz (CPU specs).

By default, Turbo-Boost and Hyperthreading are disabled to ensure the reproducibility of the experiments carried out on the nodes.

Memory

192 GB (5.3 GB/core) @ 2933 MT/s.

Network

OmniPath 100 Gbit/s.

10 Gbit/s Ethernet.

Storage

Local disk (/tmp) of 1 To (SATA Seagate ST1000NX0443 @ 7.2krpm).

BeeGFS over 100G OmniPath.

These nodes are in best effort and without support, and will be removed from the platform when failing to start.

CPU

2x 12-core Haswell Intel Xeon E5-2680 v3 @ 2.5 GHz. Haswell specs.

By default, Turbo-Boost and Hyperthreading are disabled to ensure the reproducibility of the experiments carried out on the nodes.

Memory

128 GB (5.3 GB/core) @ 2933 MT/s.

Network

OmniPath 100 Gbit/s on miriel[001-043].

InfiniBand QDR 40 Gbit/s (TrueScale/InfiniPath).

10 Gbit/s Ethernet.

Storage

Local disk (/tmp) of 300 GB (SATA Seagate ST9500620NS @ 7.2krpm).

BeeGFS over 10G Ethernet.

CPU

2x 32-core AMD Zen2 EPYC 7452 @ 2.35 GHz on diablo01-04 (CPU specs).

2x 64-core AMD Zen2 EPYC 7702 @ 2 GHz on diablo05 (CPU specs).

2x 64-core AMD Zen3 EPYC 7763 @ 2.45 GHz on diablo06-09 (CPU specs).

By default, Turbo-Boost and Hyperthreading are disabled to ensure the reproducibility of the experiments carried out on the nodes.

Memory

256 GB (4 GB/core, with a single 128GB NUMA node per CPU) @ 2133 MT/s (diablo01-04).

1 TB (8 GB/core, with four 128GB NUMA nodes per CPU) @2133 MT/s (diablo05).

1 TB (8 GB/core, with a single 512GB NUMA node per CPU) @3200 MT/s (diablo06-09)

Network

Mellanox InfiniBand HDR 200 Gbit/s.

10 Gbit/s Ethernet.

Storage

Local disk (/tmp) of 1 TB (SATA Seagate ST1000NM0008-2F2 @ 7.2krpm) (diablo01-05).

Local disk (/scratch) of 4 TB (RAID0 of 4 HPE SATA-6G HDD @ 7.2krpm) (diablo06-09).

BeeGFS over 10G Ethernet.

CPU

2x 32-core AMD Zen2 EPYC 7452 @ 2.35 GHz (CPU specs).

By default, Turbo-Boost and Hyperthreading are disabled to ensure the reproducibility of the experiments carried out on the nodes.

Memory

256 GB (4 GB/core) @ 3200 MT/s.

Network

10 Gbit/s Ethernet.

Storage

BeeGFS over 10G Ethernet.

CPU

2x 28-core ARM Cavium ThunderX2 CN9975 v2.1 @ 2.0 GHz (CPU specs).

By default, Turbo-Boost is disabled to ensure the reproducibility of the experiments carried out on the nodes.

However Hyperthreading is enabled on this node.

Memory

256GB (4.6 GB/core) @ 2666 MT/s.

Network

10 Gbit/s Ethernet.

Storage

Local disk (/tmp) of 128 GB (SATA Seagate ST1000NM0008-2F2 @ 7.2krpm).

BeeGFS over 10G Ethernet.

Some old sirocco nodes have old GPUs that do not work with CUDA12. If you load the CUDA 12 module there, it will fail to work with the CUDA 11 driver. An easy way to load the appropriate cuda module is:

cudaversion=$(nvidia-smi -q | grep CUDA | awk {'print $4'})
module load compiler/cuda/$cudaversion

CPU

2x 12-core Haswell Intel Xeon E5-2680 v3 @ 2.5 GHz (Haswell specs).

By default, Turbo-Boost and Hyperthreading are disabled to ensure the reproducibility of the experiments carried out on the nodes.

Memory

128 GB (5.3 GB/core) @ 2133 MT/s.

GPUs

4 NVIDIA K40M (12GB) on sirocco[01-02,05].

3 NVIDIA K40M (12GB) on sirocco[03-04].

Network

Mellanox InfiniBand QDR 40 Gbit/s.

10 Gbit/s Ethernet.

Storage

Local disk (/tmp) of 1 TB (SATA Seagate ST91000640NS @ 7.2krpm).

BeeGFS over 10G Ethernet.

View the node's internals

CPU

2x 10-core Ivy-Bridge Intel Xeon E5-2670 v2 @ 2.5 GHz (Ivy Bridge specs).

By default, Turbo-Boost and Hyperthreading are disabled to ensure the reproducibility of the experiments carried out on the nodes.

Memory

128 GB (6.4 GB/core) @ 1866 MT/s.

GPUs

2 NVIDIA K40m (12GB).

Network

Mellanox InfiniBand QDR 40 Gbit/s.

10 Gbit/s Ethernet.

Storage

Local disk (/tmp) of 1 TB (SATA Seagate ST1000NM0023 @ 7.2krpm).

BeeGFS over 10G Ethernet.

CPU

2x 16-core Broadwell Intel Xeon E5-2683 v4 @ 2.1 GHz (Broadwell specs).

By default, Turbo-Boost and Hyperthreading are disabled to ensure the reproducibility of the experiments carried out on the nodes.

Memory

256 GB (8GB/core) @ 2133 MT/s.

GPUs

2 NVIDIA P100 (16GB).

Network

Omnipath 100 Gbit/s.

10 Gbit/s Ethernet.

Storage

Local disk (/tmp) of 300 GB (SAS WD Ultrastar HUC156030CSS204 @ 15krpm).

BeeGFS over 10G Ethernet.

CPU

2x 16-core Skylake Intel Xeon Gold 6142 @ 2.6 GHz (CPU specs).

By default, Turbo-Boost and Hyperthreading are disabled to ensure the reproducibility of the experiments carried out on the nodes.

Memory

384 GB (12 GB/core) @ 2666 MT/s.

GPUs

2 NVIDIA V100 (16GB).

Network

Omnipath 100 Gbit/s.

10 Gbit/s Ethernet.

Storage

Local disk (/scratch) of 750 GB (NVMe Samsung).

BeeGFS over 10G Ethernet.

CPU

2x 20-core Skylake Intel Xeon Gold 6148 @ 2.4GHz (CPU specs).

By default, Turbo-Boost and Hyperthreading are disabled to ensure the reproducibility of the experiments carried out on the nodes.

Memory

1 TB (25.6 GB/core) @ 1866 MT/s.

GPUs

2 NVIDIA V100 (16GB).

Network

Omnipath 100 Gbit/s.

10 Gbit/s Ethernet.

Storage

Local disk (/tmp) of 1 TB (SAS Seagate ST300MP0026 @ 15krpm).

BeeGFS over 10G Ethernet.

CPU

2x 20-core Cascade Lake Intel Xeon Gold 5218R CPU @ 2.10 GHz (CPU specs).

By default, Turbo-Boost and Hyperthreading are disabled to ensure the reproducibility of the experiments carried out on the nodes.

Memory

192 GB (4.8GB/core) @ 3200 MT/s.

GPUs

2 NVIDIA Quadro RTX8000 (48GB).

Network

10 Gbit/s Ethernet.

Storage

No local storage.

BeeGFS over 10G Ethernet.

CPU

2x 24-core AMD Zen2 EPYC 7402 @ 2.80 GHz (CPU specs).

By default, Turbo-Boost and Hyperthreading are disabled to ensure the reproducibility of the experiments carried out on the nodes.

Memory

512GB (10.6GB/core) @ 3200 MT/s.

GPUs

2 NVIDIA A100 (40GB).

Network

10 Gbit/s Ethernet.

Storage

Local disk (/scratch) of 3.5 TB (RAID0 of 2 SAS SSD TOSHIBA KRM5XVUG1T92 Rm5 Mixed Use).

BeeGFS over 10G Ethernet.

CPU

2x 32-core AMD Zen3 EPYC 7513 @ 2.60 GHz (CPU specs).

By default, Turbo-Boost and Hyperthreading are disabled to ensure the reproducibility of the experiments carried out on the nodes.

Memory

512GB (8GB/core) @ 3200 MT/s.

GPUs

2 NVIDIA A100 (40GB).

Network

10 Gbit/s Ethernet.

Storage

Local disk (/scratch) of 4 TB (RAID0 of 4 HPE SATA-6G HDD @ 7.2krpm).

BeeGFS over 10G Ethernet.

under BETA TESTING

CPU

2x 28-core Intel IceLake Xeon Gold 6330 @ 2 GHz.

By default, Turbo-Boost and Hyperthreading are disabled to ensure the reproducibility of the experiments carried out on the nodes.

Memory

256GB (4.6GB/core) @ 3200 MT/s.

GPUs

2 Intel DataCenter GPU Flex 170 (16GB).

Network

10 Gbit/s Ethernet.

Storage

Local disk (/scratch) of 1 TB (SATA HDD @ 7.2krpm).

No BeeGFS over 10G Ethernet yet (FIXME).

under BETA TESTING

CPU

2x 56-core Intel SapphireRapids Xeon Platinum 8480+ @ 2 GHz.

Hyperthreading is currently enabled (FIXME and turboboost).

Memory

512GB (4.6GB/core).

GPUs

2 Intel DataCenter GPU Max 1100 (48GB).

Network

10 Gbit/s Ethernet.

Storage

No BeeGFS over 10G Ethernet yet (FIXME).

CPU

64-core Intel Xeon Phi 7230 @ 1.3 GHz (4 hyperthreads per core). (Airmont core specs)

By default, Turbo-Boost is disabled to ensure the reproducibility of the experiments carried out on the nodes.

However Hyperthreading is enabled on these nodes.

Memory

96GB of DRAM (1.5GB/core) @ 2400 MT/s.

16GB of MCDRAM on-package (0.25GB/core).

Network

Only 1 Gbit/s Ethernet.

Omnipath 100 Gbit/s.

Storage

Local disk (/scratch) of 800 GB (SSD Intel SSDSC2BX80).

BeeGFS over 1G Ethernet.

KNL configuration

kona01 is in Quadrant/Flat: 64 cores, 2 NUMA nodes for DRAM and MCDRAM.

kona02 is in Quadrant/Cache: 64 cores, 1 NUMA node for DRAM with MCDRAM as a cache in front of it.

kona03 is in SNC-4/Flat: 4 clusters with 16 cores and 2 NUMA nodes each.

kona04 is in SNC-4/Cache: 4 clusters with 16 cores, 1 DRAM NUMA node and MCDRAM as a cache.

CPU

4x 24-core Intel Xeon E7-8890 v4 @ 2.2GHz (CPU specs).

By default, Turbo-Boost and Hyperthreading are disabled to ensure the reproducibility of the experiments carried out on the nodes.

Memory

1 TB (10.7 GB/core) @ 1600 MT/s.

Network

10 Gbit/s Ethernet.

Storage

Local disk (/tmp) of 280 GB (SAS @ 15krpm).

BeeGFS over 10G Ethernet.

CPU

12x 8-core Intel Ivy-Bridge Xeon E5-4620 v2 @ 2.6 GHz (Ivy Bridge specs)

By default, Turbo-Boost and Hyperthreading are disabled to ensure the reproducibility of the experiments carried out on the nodes.

Memory

3TB (32GB/core) @ 1600MT/s.

Network

10 Gbit/s Ethernet.

Storage

No local storage.

BeeGFS over 10G Ethernet.

You can see the list of all the nodes in the hardware documentation section.

To have the state of cluster go to https://www.plafrim.fr/state/

To allocate a specific category of node with SLURM, you need to specify the node features. To display the list, call the command:

$ sinfo -o "%60f %N"
AVAIL_FEATURES                                               NODELIST
miriel,intel,haswell,omnipath,infinipath                     miriel[001-043]
miriel,intel,haswell,infinipath                              miriel[044,048,050-053,056-058,060-064,066-073,075-076,078-079,081,083-086]
sirocco,intel,broadwell,omnipath,nvidia,tesla,p100           sirocco[07-13]
sirocco,amd,nvidia,ampere,a100                               sirocco21
amd,zen2,zonda                                               zonda[01-21]
bora,intel,cascadelake,omnipath                              bora[001-043]
amd,zen3,diablo,bigmem,mellanox                              diablo[06-09]
sirocco,intel,skylake,omnipath,nvidia,tesla,v100             sirocco[14-16]
sirocco,intel,skylake,omnipath,nvidia,tesla,v100,bigmem      sirocco17
sirocco,amd,zen3,nvidia,ampere,a100                          sirocco[22-24]
arm,cavium,thunderx2                                         arm01
brise,intel,broadwell,bigmem                                 brise
amd,zen2,diablo,mellanox                                     diablo[01-03]
amd,zen2,diablo,bigmem,mellanox                              diablo05
kona,intel,knightslanding,knl,omnipath                       kona[01-04]
sirocco,intel,haswell,mellanox,nvidia,tesla,k40m             sirocco[01-05]
Ivy                                                          sirocco06
sirocco,intel,skylake,nvidia,quadro,rtx8000                  sirocco[18-20]
souris,sgi,ivybridge,bigmem                                  souris
visu                                                         visu01
mistral                                                      mistral[02-03,06]

We will see below how to use specific nodes for a job, as an example, to reserve a bora node, you need to call

$ salloc -C bora

sinfo has many parameters, for example:

-N (--Node)

Print information in a node-oriented format.

-l (--long)

Print more detailed information.

$ sinfo -l
PARTITION AVAIL  TIMELIMIT   JOB_SIZE ROOT OVERSUBS     GROUPS  NODES       STATE NODELIST
routage*     up 3-00:00:00 1-infinite   no       NO        all     42    drained* miriel[001,005,008,010,016-017,020,022,024,027,043-045,048,050-053,057,060,062-064,067-071,073,075-076,078-079,081,083-088],sirocco[03-04]
routage*     up 3-00:00:00 1-infinite   no       NO        all      1  allocated* bora011
routage*     up 3-00:00:00 1-infinite   no       NO        all      3       down* miriel[019,038,056]
routage*     up 3-00:00:00 1-infinite   no       NO        all      1    draining zonda03
routage*     up 3-00:00:00 1-infinite   no       NO        all      4     drained bora009,miriel004,zonda[01-02]
routage*     up 3-00:00:00 1-infinite   no       NO        all      3       mixed miriel[002-003],sirocco07
routage*     up 3-00:00:00 1-infinite   no       NO        all     17   allocated bora[001-007],diablo[03-04],sirocco[01,14-17],zonda[04-06]
[…]

$ sinfo -N
NODELIST   NODES PARTITION STATE
arm01          1  routage* idle
bora001        1  routage* alloc
bora002        1  routage* alloc
bora003        1  routage* alloc
bora004        1  routage* alloc
bora005        1  routage* alloc
bora006        1  routage* alloc
bora007        1  routage* alloc
bora008        1  routage* idle
bora009        1  routage* drain
[…]

Nodes which are grouped by features, as explained above. To use a miriel node, you need to call

$ salloc -C miriel

If you only want to use a miriel node with omnipath, you need to call

$ salloc -C "miriel&omnipath"

The default time for a job is 1 hour, to specify another time duration, you need to use --time=HH:MM:SS, e.g for a 30 minutes job, you need to call

$ salloc --time=0:30:0

If you do not specify any constraints, SLURM will try first to allocate nodes that do not have any advanced features. The idea is to avoid allocating rare nodes with advanced features (GPUs, large memory, high-speed network, etc) unless really needed.

The current weights for nodes are as follows:

• zonda
• miriel
• bora
• diablo
• sirocco
• arm01
• visu01
• kona
• brise
• souris

It means zonda nodes are allocated first when possible, while souris is only allocated when requested or when all other nodes are busy.

salloc allows to run jobs with different steps, and use at each step all or a subset of resources.

$ salloc -N 3
salloc: Granted job allocation 1155503
salloc: Waiting for resource configuration
salloc: Nodes sirocco[01-03] are ready for job

The command squeue can be used to have a look at the job state:

$ squeue --job 17397
JOBID  PARTITION NAME  USER     ST  TIME  NODES NODELIST(REASON)
17397  routage   bash  bouchoui R   1:05  2     miriel[007-008]

In the same shell terminal, run srun your_executable (the command will use all the allocated resources).

$ srun hostname
sirocco01.plafrim.cluster
sirocco02.plafrim.cluster
sirocco03.plafrim.cluster

$ srun -N 1 hostname
sirocco01.plafrim.cluster

You can connect to the first node using

$ srun --pty bash -i

You can also login to one of the allocated nodes by using ssh however all slurm's variables environment will not be set.

$ ssh miriel007

Once connected to a node with ssh, if you want to run a command on all allocated resources, you must run the srun command with the jobid option followed by the id associated to your job.

@miriel007~$ srun --jobid=17397 hostname
miriel007
miriel008

One can also define the following arguments to the command srun

-N 1 (or --nodes=1)

the node count, by default it is equal to 1.

-n 1 (or --ntasks=1)

number of tasks, by default it is equal to 1, otherwise it must be equal to or less than the number of cores of the node

--exclusive

allocate node(s) in exclusive mode

You can also use directly srun without a salloc.

$ srun --pty bash -i
$ hostname
miriel004.plafrim.cluster

The option --pty also works when asking more than one node. You will be connected to the first node. To see which are nodes are part of the job, you can look at the environment variable SLURM_JOB_NODELIST.

When using srun, the environment variables may not be set properly, for example the variable MODULEPATH will not be set properly wrt the allocated node. You need to use --export=NONE, for example

$ srun --export=NONE -C haswell /bin/bash -lc 'yourcommand'

$ srun --export=NONE -C haswell /bin/bash -lc 'echo $HOSTNAME $MODULEPATH $SLURM_JOB_ID && cat /usr/share/Modules/init/.modulespath && module -t av compiler'

$ srun --export=NONE -C ivybridge /bin/bash -lc 'echo $HOSTNAME $MODULEPATH $SLURM_JOB_ID && cat /usr/share/Modules/init/.modulespath && module -t av compiler'

$ cat script-slurm.sl
#!/usr/bin/env bash
# Job name
#SBATCH -J TEST_Slurm
# Asking for one node
#SBATCH -N 1
#SBATCH -n 4
# Standard output
#SBATCH -o slurm.sh%j.out
# Standard error
#SBATCH -e slurm.sh%j.err

echo "=====my job information ===="
echo "Node List: " $SLURM_NODELIST
echo "my jobID: " $SLURM_JOB_ID
echo "Partition: " $SLURM_JOB_PARTITION
echo "submit directory:" $SLURM_SUBMIT_DIR
echo "submit host:" $SLURM_SUBMIT_HOST
echo "In the directory:" $PWD
echo "As the user:" $USER

module purge
module load compiler/gcc
srun -n4 hostname

Launch the job using the command sbatch

$ sbatch script-slurm.sl
Submitted batch job 7421

to get information about the running jobs

$ squeue

and more...

$ scontrol show job <jobid>

to delete a running job

$ scancel <jobid>

to watch the output of the job 17421

$ cat slurm.sh17421.out
=====my job information ====
Node List:  zonda05
my jobID:  1461714
Partition:  routage
submit directory: /home/furmento
submit host: devel03.plafrim.cluster
In the directory: /home/furmento
As the user: furmento
zonda05.plafrim.cluster
zonda05.plafrim.cluster
zonda05.plafrim.cluster
zonda05.plafrim.cluster

There are 2 commands:

• squeue

  $ squeue -o "%.18i %.9P %.8j %.8u %.2t %.10M %.6D %.3C %.20R" --job 17397
  

  The different header are:

  • JOBID The job identifier
  • PARTITION The partition on which the job is running, use sinfo to display all partitions on the cluster.
  • NAME the name of job, to define or change the name (in batch mode) use -J name_of_job.
  • USER the login of the job owner
  • ST the state of submitted job PENDING, RUNNING, FAILED, COMPLETED, ... etc.
  • TIME The time limit for the job (NOTE : if the user doesn't define the time limit for his job, the default time limit of the partition will be used).
  • NODE Size of nodes.
  • NODELIST List of nodes used.

  The different job states are:

  • PD (pending): Job is awaiting resource allocation,
  • R (running): Job currently has an allocation,
  • CA (cancelled): Job was explicitly cancelled by the user or system administrator,
  • CF (configuring): Job has been allocated resources, but is waiting for them to become ready,
  • CG (completing): Job is in the process of completing. Some processes on some nodes may still be active,
  • CD (completed): Job has terminated all processes on all nodes,
  • F (failed): Job terminated with non-zero exit code or other failure condition,
  • TO (timeout): Job terminated upon reaching its time limit,
  • NF (node failure): Job terminated due to failure of one or more allocated nodes.

• scontrol.

  $ scontrol show job 1155454
  JobId=1155454 JobName=plafrim-master-plafrim-gcc.sl
  UserId=furmento(10193) GroupId=storm(11118) MCS_label=N/A
  Priority=1 Nice=0 Account=(null) QOS=normal
  JobState=RUNNING Reason=None Dependency=(null)
  Requeue=1 Restarts=0 BatchFlag=1 Reboot=0 ExitCode=0:0
  RunTime=00:06:15 TimeLimit=01:00:00 TimeMin=N/A
  SubmitTime=2021-01-19T08:34:06 EligibleTime=2021-01-19T08:34:06
  AccrueTime=2021-01-19T08:34:06
  StartTime=2021-01-19T08:34:06 EndTime=2021-01-19T09:34:06 Deadline=N/A
  SuspendTime=None SecsPreSuspend=0 LastSchedEval=2021-01-19T08:34:06
  Partition=routage AllocNode:Sid=devel02.plafrim.cluster:300429
  ReqNodeList=(null) ExcNodeList=(null)
  NodeList=sirocco08
  BatchHost=sirocco08
  NumNodes=1 NumCPUs=24 NumTasks=1 CPUs/Task=1 ReqB:S:C:T=0:0:*:*
  TRES=cpu=24,node=1,billing=24
  Socks/Node=* NtasksPerN:B:S:C=0:0:*:* CoreSpec=*
  MinCPUsNode=1 MinMemoryNode=0 MinTmpDiskNode=0
  Features=sirocco DelayBoot=00:00:00
  OverSubscribe=NO Contiguous=0 Licenses=(null) Network=(null)
  Command=/home/furmento/buildbot/plafrim-master-plafrim-gcc.sl
  WorkDir=/home/furmento/buildbot
  StdErr=/home/furmento/buildbot/slurm-1155454.out
  StdIn=/dev/null
  StdOut=/home/furmento/buildbot/slurm-1155454.out
  Power=
  

Note that scontrol show job may show information about completed jobs only during 5 minutes after they completed.

The sirocco nodes have GPUs (see the Hardware Documentation section). You will need to specify the given constraints if you want a specific GPU card.

It is advised to use the exclusive parameter to make sure nodes are not used by another job at the same time.

$ srun --exclusive -C sirocco --pty bash -i

@sirocco08.plafrim.cluster:~> module load compiler/cuda

@sirocco08.plafrim.cluster:~> nvidia-smi
Tue Jan 19 10:52:07 2021
+-------------------------------------------------------------------+
| NVIDIA-SMI 460.27.04 Driver Version: 460.27.04 CUDA Version: 11.2 |
+-------------------------------------------------------------------+
| 0 Tesla P100-PCIE... | On | ...
| 1 Tesla P100-PCIE... | On | ...
[...]

To kill all running jobs in batch session, use the scancel command with the login name option or with the list of job’s id (separated by space):

$ scancel -u <user>

or

$ scancel jobid_1 ... jobid_N

The command squeue can be used to get the job ids.

$ squeue
JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON)
2545 longq test1 bee R 4:46:27 1 miriel007
2552 longq test2 bee R 4:46:47 1 miriel003
2553 longq test1 bee R 4:46:27 1 miriel004

Interactive jobs can also be killed by exiting the current shell.

It is possible to run a job with several nodes and launch different programs on different set of nodes.

Here an example of such a multiprogram configuration file.

############################################################
# srun multiple program configuration file
#
# srun -n8 -l –multi-prog silly.conf
############################################################
4-6 hostname
1,7 echo task:%t
0,2-3 echo offset:%o

To submit such a file, use the following command

$ srun -n8 -l --multi-prog silly.conf

You will get a output similar to

4: miriel004.plafrim.cluster
6: miriel004.plafrim.cluster
5: miriel004.plafrim.cluster
7: task:7
1: task:1
2: offset:1
3: offset:2
0: offset:0

As explained here, you are not supposed to submit many large jobs that will occupy most of the platform at the same time. PlaFRIM does not strictly prevent you from submitting many jobs because many small jobs are fine. However users submitting large/long jobs are expected to manually take care of avoiding flooding the platform and reserving all nodes.

If you have many big jobs to submit, there are ways to defer them so that they occupy smaller parts of the platform at different times.

If you usually submit many jobs in a loop, just add a delay between them. For instance, instead of submitting 100 jobs of 72 hours each, you may for instance submit 10 of them now, and then wait for 72 hours and submit 10 more, etc. This is easy to implement in a script that may run in a screen or tmux on the devel nodes.

You may also ask SLURM to defer a job until another one has ended. The following lines submit jobs 123 and 124 immediately. 125 is submitted but it won't run until 123 has completed, etc.

$ sbatch job1.sl
Submitted batch job 123
$ sbatch job2.sl
Submitted batch job 124
$ sbatch --dependency afterany:123 job3.sl
Submitted batch job 125
$ sbatch --dependency afterany:124 job4.sl
Submitted batch job 126
$ sbatch --parsable --dependency afterany:125 job3.sl
127
$ sbatch --parsable --dependency afterany:126 job4.sl
128

The sbatch option --parsable makes the output line even more simple, allowing to script this entire submission process.

To avoid launching large/long jobs during office hours, you can also specify the time when the job can start.

$ sbatch --begin=20:00 job1.sl

See the manpage of sbatch for more options to specify the start time.

• Use a specific function to backup and another function to restore from a backup.
• Backup in a temporary file (or several files in a temporary directory), then rename (atomic operation) the file or directory with a final name to stamp the backup.
  • If the application is stopped during the backup, the temporary backup will be ignored, the previous stamped backup will be used.
• When your application starts, it should first check if a backup is available, and if yes, use it to restart from it.
• Backing up should typically be done after a MPI barrier to make sure all nodes are synchronized.
• Backup frequency should be adapted to its duration (data writing on disk). A rough idea is that an application should not run for more than 30 mins to 1 hour without doing a backup.

The Environment Modules package is a tool that simplifies shell initialization and lets users easily modify their environment during the session with module files.

Each module file contains the information needed to configure the shell for an application. Once the Modules package is initialized, the environment can be modified on a per-module basis using the module command which interprets module files. Typically module files instruct the module command to alter or set shell environment variables such as PATH, MANPATH, etc. Module files may be shared by many users on a system and users may have their own collection to supplement or replace the shared module files.

Modules can be loaded and unloaded dynamically and atomically, in an clean fashion.

To ease module management, they are sorted according to the architecture of the nodes, and grouped in categories

The different architectures are:

• generic for modules which can run on all nodes
• intel/haswell for the nodes miriel and sirocco[01-05]
• intel/broadwell for the nodes sirocco[07-13]
• intel/skylake for the nodes bora
• intel/knightslanding for the nodes kona

When connecting to a node via salloc, the environment variable MODULEPATH contains the directory generic and the node-specific directory manufacturer/chip

Within each architecture, modules are grouped with the following module naming policy

/category/module/option/version

the number of options being between 0 and as many as needed.

For example

• partitioning/scotch/int32/6.0.4
• partitioning/scotch/int64/6.0.4

• Modules managed by the technical team (MPI, GCC and CUDA compilers) are available in /cm/shared/modules
• User-managed modules are available in /cm/shared/dev/modules
• All users can install modules, one only needs to be added to the Unix group plafrim-dev (ticket to plafrim-support AT inria.fr)

Path to development headers (for compiling)

prepend-path CPATH ...
prepend-path FPATH ...
prepend-path INCLUDE ...
prepend-path C_INCLUDE_PATH ...
prepend-path CPLUS_INCLUDE_PATH ...
prepend-path OBJC_INCLUDE_PATH ...

Path to libraries (for linking)

prepend-path LIBRARY_PATH ...

Path to tools and libraries (for running)

prepend-path PATH ...
prepend-path LD_LIBRARY_PATH ...

pkg-config to simplifying build systems (point to directories with .pc files)

prepend-path PKG_CONFIG_PATH $prefix/lib/pkgconfig

Manpages

append-path  MANPATH   $man_path
append-path  MANPATH   $man_path/man1
append-path  MANPATH   $man_path/man3
append-path  MANPATH   $man_path/man7

Some modules define specific variables, likely because one random project ever decided they need them...

setenv            CUDA_INSTALL_PATH   $root
setenv            CUDA_PATH           $root
setenv            CUDA_SDK            $root
prepend-path      CUDA_INC_PATH       $root/include

setenv          HWLOC_HOME          $prefix

setenv         MPI_HOME          $prefix
setenv         MPI_RUN           $prefix/bin/mpirun
setenv         MPI_NAME          $name
setenv         MPI_VER           $version

The default is in (reverse) alphabetical order

$ module avail formal/sage
--- /cm/shared/dev/modules/generic/modulefiles ---
formal/sage/7.0 formal/sage/8.9 formal/sage/9.0

$ module load formal/sage
$ module list
Currently Loaded Modulefiles:
1) formal/sage/9.0

Another default version may be enforced. Useful if your last beta release isn't stable or backward compatible but still needed for some hardcore users.

$ cat /cm/shared/dev/modules/generic/modulefiles/hardware/hwloc/.version
#%Module1.0#
set ModulesVersion "2.1.0"

$ module_grep starpu
runtime/starpu/1.3.2/mpi
runtime/starpu/1.3.2/mpi-fxt
runtime/starpu/1.3.3/mpi
runtime/starpu/1.3.3/mpi-cuda
runtime/starpu/1.3.3/mpi-cuda-fxt
runtime/starpu/1.3.3/mpi-fxt

StarPU has different module files which ONLY differ in the prereq commands and the prefix setting.

> prereq compiler/cuda/10.1
> prereq trace/fxt/0.3.9
< set     prefix          /cm/shared/dev/modules/generic/apps/runtime/starpu/1.3.3/gcc@9.2.0-hwloc@2.1.0-openmpi@4.0.2
> set     prefix          /cm/shared/dev/modules/generic/apps/runtime/starpu/1.3.3/gcc@8.2.0-hwloc@2.1.0-openmpi@4.0.1-cuda@10.1
> set     prefix          /cm/shared/dev/modules/generic/apps/runtime/starpu/1.3.3/gcc@8.2.0-hwloc@2.1.0-openmpi@4.0.1-cuda@10.1-fxt@0.3.9

Create a module file with all the cases

if {![ is-loaded compiler/gcc ]} {
   module load compiler/gcc
}
if {![ is-loaded hardware/hwloc ]} {
   module load hardware/hwloc/2.1.0
}

conflict runtime/starpu

set cuda ""
set fxt ""
if {[ is-loaded compiler/cuda/10.1 ]} {
  set cuda -cuda@10.1
}
if {[ is-loaded trace/fxt/0.3.9 ]} {
  set fxt -fxt@0.3.9
}

set     name            starpu
set     version         1.3.3

set     prefix /cm/shared/dev/modules/generic/apps/runtime/$name/$version/gcc@$env(GCC_VER)-hwloc@2.1.0-openmpi@4.0.1${cuda}${fxt}

Different environments lead to a different version of StarPU to be used.

https://modules.readthedocs.io/en/latest/modulefile.html

The module tools/module_cat provides the following tools

• module_list to list the existing categories
• module_add, module_init, module_add, module_rm to modify the environment variable MODULEPATH which defines the folders in which to look for modules
• module_perm to set the correct permissions on a given directory
• module_search to search all modules whose name have the given string, e.g module_search hwloc

Load your environment using the appropriate modules.

Currently, we provide the following OpenMPI implementations

$ module avail mpi/openmpi
mpi/openmpi/2.0.4 mpi/openmpi/3.1.4 mpi/openmpi/4.0.1
mpi/openmpi/4.0.1-intel mpi/openmpi/4.0.2 mpi/openmpi/4.0.2-testing
mpi/openmpi/4.0.3 mpi/openmpi/4.0.3-mlx

To use the 4.0.3 version

$ module load mpi/openmpi/4.0.3

To run a MPI program, you can use mpirun.

$ salloc -N 3

$ mpirun hostname
miriel040.plafrim.cluster
miriel041.plafrim.cluster
miriel042.plafrim.cluster

$ salloc -n 3

$ mpirun hostname
miriel023.plafrim.cluster
miriel023.plafrim.cluster
miriel023.plafrim.cluster

To compile a MPI application, you can use mpicc

mpicc -o program program.c

To run the program

mpirun --mca btl openib,self program

To launch MPI applications on miriel, sirocco and devel nodes :

$ mpirun -np <nb_procs> --mca mtl psm  ./apps

If you need OmniPath interconnect :

$ mpirun -np <nb_procs> --mca mtl psm2 ./apps

For more details about how these different options work, you can see https://agullo-teach.gitlabpages.inria.fr/school/school2019/slides/mpi.pdf.

Load your environment using the appropriate modules.

$ module avail mpi/intel
[...]
$ module add compiler/gcc compiler/intel mpi/intel

Create a file with the names of the machines that you want to run your job on:

$ srun hostname -s| sort -u > mpd.hosts

To run your application on these nodes, use mpiexec.hydra, and choose the fabrics for intra-node and inter-nodes mpi communcation:

$ export I_MPI_FABRICS=shm:tmi mpiexec.hydra -f mpd.hosts -n $SLURM_NNODES ./a.out

Choose your build and execution environment using modules, for instance:

$ module load compiler/gcc
$ module add compiler/intel
$ module add mpi/intel

Launch with mpiexec.hydra command:

$ srun hostname  -s| sort -u > mpd.hosts

Select the particular network fabrics to be used with the environment variable I_MPI_FABRICS.

I_MPI_FABRICS=<fabric>|<intra-node fabric>:<inter-node fabric>

Where <fabric> := {shm, dapl, tcp, tmi, ofa}

For example, to select shared memory fabric (shm), for intra-node communication mpi process, and tag maching interface fabric (tmi), for inter-node communication mpi process, use the following command:

$ export I_MPI_FABRICS=shm:tmi
$ mpiexec.hydra -f mpd.hosts -n $SLURM_NPROCS ./a.out

The available fabrics on the platform are:

tmi

TMI-capable network fabrics including Intel True Scale Fabric, Myrinet, (through Tag Matching Interface)

ofa

OFA-capable network fabric including InfiniBand (through OFED verbs)

dapl

DAPL-capable network fabrics, such as InfiniBand, iWarp, Dolphin, and XPMEM (through DAPL)

tcp

TCP/IP-capable network fabrics, such as Ethernet and InfiniBand (through IPoIB)

You can also specify a list of fabrics, (The default value is dapl,tcp)with the environment variable I_MPI_FABRICS_LIST. The first fabric detected will be used at runtime:

I_MPI_FABRICS_LIST=<fabrics list>

Where <fabrics list> := <fabric>,…,<fabric>

(for more details visit https://software.intel.com/sites/products/documentation/hpc/ics/impi/41/lin/Reference_Manual/Communication_Fabrics_Control.htm)

Guix can be used in addition to and in parallel with module. There are several reasons why it might be useful to you:

• Guix provides more than 20,000 software packages including: utilities such as tmux, compiler toolchains (GCC, Clang), Python software (Scikit-Learn, PyTorch, NumPy, etc.), HPC libraries (Open MPI, MUMPS, PETSc, etc.).
• Pre-built binaries are usually available for packages you install, which makes installation fast.
• You get to choose when you upgrade or remove packages you've installed for yourself, and can roll back any time you want should an upgrade go wrong.
• You can reproduce the exact same software environment, bit-for-bit, on PlaFRIM and on other machines (laptop, cluster, etc.)
• Software environments can be "packed" as a Docker image for use on other systems.

We maintain a package collection for software developed by Inria research teams such as STORM, HiePACS, and TaDaaM in the Guix-HPC channel. This channel is enabled by default on PlaFRIM via `/etc/guix/channels.scm`, which means that running `guix pull` will give you Guix-HPC packages in addition to packages that come with Guix.

Non-free software such as CUDA, as well as variants of free software packages with dependencies on non-free software (such as starpu-cuda) are available in the Guix-HPC-non-free channel. Read the instructions on how to add guix-hpc-non-free to your ~/.config/guix/channels.scm file.

If you have not updated your Guix repository for quite a while, you may have a timeout error when running guix pull. To solve the problem, you just need to call /usr/local/bin/guix pull. This will update your repository, and you can go back to calling guix pull afterwards.

Once you have pulled the guix-hpc-non-free channel (see above), you have access via Guix to several cuda-toolkit packages as well as CUDA-enabled variants of HPC packages, such as starpu-cuda or chameleon-cuda:

$ guix package --list-available=cuda
chameleon-cuda          1.1.0   debug,out       inria/tainted/hiepacs.scm:32:2
chameleon-cuda-mkl-mt   1.1.0   debug,out       inria/tainted/hiepacs.scm:60:2
cuda-toolkit            8.0.61  out,doc         non-free/cuda.scm:25:2
cuda-toolkit            11.0.3  out             non-free/cuda.scm:143:2
cuda-toolkit            10.2.89 out             non-free/cuda.scm:214:2
pastix-cuda             6.2.1   out             inria/tainted/hiepacs.scm:77:2
qr_mumps-cuda           3.0.4   out             inria/experimental.scm:30:2
starpu-cuda             1.3.9   debug,out       inria/tainted/storm.scm:69:2
starpu-cuda-fxt         1.3.9   debug,out       inria/tainted/storm.scm:116:2

To use them, you need to connect to a machine with CUDA devices and the actual CUDA driver (not provided by Guix) for example with:

salloc -C sirocco -N1

From there, you need to arrange so that /usr/lib64/libcuda.so (the driver) gets loaded by setting LD_PRELOAD. The example below shows how to do that with StarPU, running starpu_machine_display to ensure StarPU detects CUDA devices:

$ LD_PRELOAD="/usr/lib64/libcuda.so" guix shell starpu-cuda -- starpu_machine_display
[starpu][check_bus_config_file] No performance model for the bus, calibrating...
[starpu][benchmark_all_gpu_devices] CUDA 0...
[starpu][benchmark_all_gpu_devices] CUDA 1...
[starpu][benchmark_all_gpu_devices] CUDA 2...
[starpu][benchmark_all_gpu_devices] CUDA 3...
[starpu][benchmark_all_gpu_devices] CUDA 0 -> 1...

[…]

4 STARPU_CUDA_WORKER workers:
        CUDA 0.0 (Tesla K40m 10.1 GiB 03:00.0)
        CUDA 1.0 (Tesla K40m 10.1 GiB 04:00.0)
        CUDA 2.0 (Tesla K40m 10.1 GiB 82:00.0)
        CUDA 3.0 (Tesla K40m 10.1 GiB 83:00.0)
No STARPU_OPENCL_WORKER worker

topology ... (hwloc logical indexes)
numa 0  pack 0  core 0  PU 0    CUDA 0.0 (Tesla K40m 10.1 GiB 03:00.0)  CUDA 1.0 (Tesla K40m 10.1 GiB 04:00.0)  CUDA 2.0 (Tesla K40m 10.1 GiB 82:00.0) CUDA 3.0 (Tesla K40m 10.1 GiB 83:00.0)

bandwidth (MB/s) and latency (us)...
from/to NUMA 0  CUDA 0  CUDA 1  CUDA 2  CUDA 3
NUMA 0  0       10518   10521   9590    9569
CUDA 0  10522   0       10251   9401    9382
CUDA 1  10521   10251   0       8810    8947
CUDA 2  8499    8261    9362    0       10250
CUDA 3  8670    8282    8667    10251   0

[…]

Once you have a software environment that works well on PlaFRIM, you may want to create a self-contained "bundle" that you can send and use on other machines that do not have Guix. With guix pack you can create "container images" for Docker or Singularity, or even standalone tarballs. See the following articles for more information:

• Using Guix Without Being root
• Tarballs, the ultimate container image format
• Faster relocatable packs with Fakechroot

For more information, please see:

• Getting Started (français)
• Guix-HPC web site
• notes from the "Midi de la bidouille", March 2018

Please send any support request to plafrim-guix@inria.fr.

An iRODS Storage Resource is available at the MCIA (mésocentre Aquitain).

It allows to backup your research data.

IMPORTANT : Encryption is not available (without authentication). Data are unencrypted both on the disks and on the network. If necessary, you need to encrypt data by yourself.

Data are scattered over 7 sites (Bordeaux and Pau).

IRODS keeps 3 copies of every file:

• one nearby the storage resource the data was first copied on,
• one at the MCIA (near Avakas),
• one in another storage resource.

Default quota : 500Gb.

Help https://redmine.mcia.univ.bordeaux.fr/projects/irods

One needs an account at the mesocentre. To do so, go to the page to request a Avakas account at inscriptions@mcia.univ-bordeaux.fr

• Connect to the mesocentre to initialize your IRODS account, choose a specific password for iRODS. When loading the module, the iRODS account will be initialised.

  $ ssh VOTRELOGIN@avakas.mcia.univ-bordeaux.fr
  $ module load irods/mcia
  

• On PlaFRIM - Prepare your environment by calling the command iiint and answer the given questions.

  $ module load tools/irods
  $ iinit
  

  • Host: icat0.mcia.univ-bordeaux.fr
  • Port: 1247
  • Zone: MCI
  • Default Resource : siterg-imb (for PlaFRIM) (from another platform, choose siterg-ubx)
  • Password : ...

The IRODS password can only be changed from Avakas

$ module load irods/mcia
$ mcia-irods-password

The list of all available commands can be found here.

• https://irods.org
• https://docs.irods.org

Please do not run such daemons (gitlab runner, jenkins slaves, etc.) in your home account. You should rather use a dedicated PlaFRIM account as described below.

• Only projects hosted on gitlab.inria.fr and using gitlab-runner connected to that server are eligible.
• Only projects that need access to PlaFRIM-specific resources are eligible. This includes GPUs, CPUs within many cores, or multiple nodes for inter-node MPI communication. If the CI only involves building and running sequential codes, usual VMs should be used instead of PlaFRIM.

First, your project must have a well-identified manager who will be responsible for CI jobs submitted on PlaFRIM (what code is executed, how many resources/jobs, etc).

As the manager, you should open a ticket with the following information:

• The URL of the gitlab.inria.fr project you want to perform CI against
• The username for the dedicated PlaFRIM account for CI for this project. If the repository is at gitlab.inria.fr/mygroup/myproject, the PlaFRIM username should be gitlab-mygroup or gitlab-myproject or gitlab-mygroup-myproject.
• Your public SSH key.

As the submitter of the ticket, you will be responsible of the dedicated account. You are responsible for everything your CI scripts do on PlaFRIM. And youe must know who wrote the code that will be tested (e.g. no pull-requests from unknown contributors without human review).

Once the dedicated PlaFRIM account is created, you should verify that the SSH connection to the new account is working and proceed with the next sections below.

This procedure MUST be performed in the dedicated gitlab-ci PlaFRIM account, not in your own personal account!

Use gitlab-runner available in a module:

$ module load tools/gitlab-runner

(several versions are available, new versions may be added if needed).

Register the runner in your gitlab project:

$ gitlab-runner register

Setup the URL and the token found in the gitlab web interface (Settings -> CI/CD -> Runners -> Specific runners -> Set up a specific runner manually). Setup tags such the project name, guix, plafrim, shell, etc. Set shell as executor.

Open a tmux or screen (so that you may later close the SSH connection without killing the runner), and launch the runner:

$ gitlab-runner run

Since the daemon may crash from time to time, you may want to use this convenience module script instead:

$ module load tools/gitlab-ci
$ gitlab-runner-keep-alive

The runner should appear in green in the gitlab web interface (Settings -> CI/CD -> Runners -> Specific runners -> Available specific runners).

Since CI scripts will likely submit batch jobs (with sbatch), they will terminate very quickly, much before the actual SLURM jobs are finished. The gitlab interface will only see the output of SLURM submissions, nothing about their completion with success or failure.

One way to wait for slurm job in the CI script is to pass --wait to sbatch so that it does not return until the job has completed. To submit multiple jobs and let run simultaneously on the cluster, one may use the bash built-in wait:

#!/bin/bash
# submit 3 jobs as background commands
sbatch --wait job1.sl &
sbatch --wait job2.sl &
sbatch --wait job3.sl &
# ask bash to wait for the completion of all background commands
wait

Note that SLURM also supports dependencies between jobs to wait for some completion before starting others:

$ sbatch job1.sl
Submitted batch job 123
$ sbatch job2.sl
Submitted batch job 124
$ sbatch job3.sl --dependency afterany:123:124

Once jobs are terminated, you may use their output files to generate the output of the CI script that will be visible from Gitlab.

TODO: In the future, a wrapper script may be available to submit one or several jobs, wait for their completion, and displays success/failure messages, and optionally dumps the output of each job.

Reproducility can be improved with the usage of tools like GNU Guix whose usage on PlaFRIM is described here.

Reproducibility is possible if the guix identifiers that identify the whole software stack used are kept. The command

guix describe

outputs all the necessary information. Finally, storing it in a file like

Date                job_id         guix_id
26/01/2022 14:55:39 409328  guix-rev#guix_4883d7f#guix-hpc-non-free_c14b2a0#guix-hpc_a30a07f

will allow you to reproduce any software stack used for your tests. By the way, this file is also the right place to keep track of performance metrics... In order not to start from scratch, you could start from this Python example.

Obviously CI jobs should not use 100% of PlaFRIM resources everyday. It means that you cannot submit a 1-hour long job on multiple nodes every 5 minutes just because you push one commit every 5 minutes.

Performing nightly testing (or during the week-end) is preferable since it will not disturb interactive jobs that usually run during office hours.

CI jobs should also never risk of disturbing the platform and users. For instance, like any human, they should not submit too many SLURM jobs, use too many resources, use the network or filesystem too intensively, run jobs on the front-end nodes, etc.

If your CI job performs intensive I/Os, using BeeGFS instead of your home directory is likely a good idea. If so, ask the administrators for a /beegfs/gitlab-myproject directory.

Then tell the gitlab-runner to use it by editing .gitlab-runner/config.toml:

builds_dir = "/beegfs/gitlab-myproject/gitlab-runner/builds"
cache_dir = "/beegfs/gitlab-myproject/gitlab-runner/cache"

Restart the runner to apply these changes.

Gitlab runners currently run on front-end nodes such as devel01. If the machine is ever rebooted, you have to restart your screen or tmux, and re-launch the runner.

TODO: An automatic procedure is under discussion.

Each gitlab CI account on PlaFRIM is dedicated to a single gitlab project so that SLURM logs allow administrators to see how many hours of compute nodes were used by each project.

Although software projects are often managed by multiple users, the PlaFRIM gitlab CI account is currently limited to a single manager that is responsible for the entire account activity.

We encourage projects to designate a permanent researcher as the manager. However, if a manager leaves his/her research team and finds another manager to replace him/her, it is possible to transfer the responsibility by opening a ticket.

New symbols will appear in node states when running sinfo, here's a recap of the new ones :

• ~ : the nodes are shut down
• # : the nodes are currently powering up
• % : the nodes are currently powering down

Example with sinfo :

PARTITION AVAIL  TIMELIMIT  NODES  STATE NODELIST
routage*     up 3-00:00:00      3  idle~ bora[040,044],diablo04 # those nodes are shut down
routage*     up 3-00:00:00      2 alloc# sirocco25,zonda21      # those nodes are powering up
routage*     up 3-00:00:00      2  idle% miriel[087-088]        # those nodes are powering down

idle~ nodes are shut down but can be allocated !

There is no need to modify your submission commands or batch scripts. If one of your allocated nodes isn't ready, your job will still be created as usual (but it will wait for all nodes to be ready before starting, obviously). HOWEVER, you won't be able to connect to any of your nodes until every node of your job is ready (otherwise, ssh will fail saying that you don't have a job on the target node). Don't panic, you still have your allocation, you just need to wait.

If you want salloc to block until all of your nodes are ready, use --wait-all-nodes=1.

You may see some jobs named "keepIdle" in the queue. Do not worry, it is part of the new mechanism. These jobs don't do anything (they are only meant to prevent some nodes for shutting too early), they will leave the queue as soon as they can.

If you ever see too many of the keepIdle jobs in the queue, it may be a bug in the energy saving system, please send the output of squeue and sinfo to the support team.

Nodes that are powering up have a time limit to get ready. If they exceed the limit (currently 10 minutes), their state will be set to down~ and your job will be requeued.

Please let the administrators know when nodes are in the down~ state so they can check what caused the failure and so they can put the node back into service.

If you just need to copy files from PlaFRIM to your laptop, using scp should be enough. Note that scp can only be used from your local computer.

scp myfile plafrim:
scp plafrim:myfile .

Note that IRODS can also be used between PlaFRIM and the MCIA.

If you need to send huge files available on the PlaFRIM nodes, you can also use a command-line client for https://filesender.renater.fr/

Here some explanations on how to install and to use it.

• From your local computer
  • go to https://filesender.renater.fr/?s=user. If you are not already connected to the service, connect first and go to the 'my profile' page by clicking on the rightmost icon in the upper menu.
  • If you do not already have a API secret, create one.
  • Click on the link 'Download Python CLI Client configuration' and save the file filesender.py.ini on your computer.
• On PlaFRIM, install the client

mkdir -p ~/.filesender/ ~/bin/

copy the previously downloaded file in ~/.filesender/filesender.py.ini

wget https://filesender.renater.fr/clidownload.php -O ~/bin/filesender.py
chmod u+x ~/bin/filesender.py
python3 -m venv ~/bin/filesender
source ~/bin/filesender/bin/activate
pip3 install requests urllib3

• On PlaFRIM, use the client

source ~/bin/filesender/bin/activate
filesender.py -r recipient@domain.email File_to_send [File_to_send ...]
deactivate

An email will be sent to recipient@domain.email with a link to download the file.