Triton quick reference

In this page, you have all important reference information

Modules

Command

Description

module load NAME

load module

module avail

list all modules

module spider NAME

search modules

module list

list currently loaded modules

module show NAME

details on a module

module help NAME

details on a module

module unload NAME

unload a module

module save ALIAS

save module collection to this alias (saved in ~/.lmod.d/)

module restore ALIAS

load saved module collection (faster than loading individually)

module purge

unload all loaded modules (faster than unloading individually)

Common software

  • Python: module load anaconda for the Anaconda distribution of Python 3, including a lot of useful packages. More info.

  • R: module load r for a basic R package. More info.

  • Matlab: module load matlab for the latest Matlab version. More info.

  • Julia: module load julia for the latest Julia version. More info.

Storage

Name

Path

Quota

Backup

Locality

Purpose

Home

$HOME or /home/$username/

hard quota 10GB

Nightly

all nodes

Small user specific files, no calculation data.

Work

$WRKDIR or /scratch/work/$username/

200GB and 1 million files

x

all nodes

Personal working space for every user. Calculation data etc. Quota can be increased on request.

Scratch

/scratch/$dept/$project/

on request

x

all nodes

Department/group specific project directories.

Local temp

/tmp/

limited by disk size

x

single-node

Primary (and usually fastest) place for single-node calculation data. Removed once user’s jobs are finished on the node.

Local persistent

/l/

varies

x

dedicated group servers only

Local disk persistent storage. On servers purchased for a specific group. Not backed up.

ramfs (login nodes only)

$XDG_RUNTIME_DIR

limited by memory

x

single-node

Ramfs on the login node only, in-memory filesystem

Partitions

Partition

Max job size

Mem/core (GB)

Tot mem (GB)

Cores/node

Limits

Use

<default>

If you leave off all possible partitions will be used (based on time/mem)

debug

2 nodes

2.66 - 12

32-256

12,20,24

15 min

testing and debugging short interactive. work. 1 node of each arch.

batch

16 nodes

2.66 - 12

32-256

12, 20,24

5d

primary partition, all serial & parallel jobs

short

8 nodes

4 - 12

48-256

12, 20,24

4h

short serial & parallel jobs, +96 dedicated CPU cores

hugemem

1 node

43

1024

24

3d

huge memory jobs, 1 node only

gpu

1 node, 2-8GPUs

2 - 10

24-128

12

5d

GPU computing

gpushort

4 nodes, 2-8 GPUs

2 - 10

24-128

12

4h

GPU computing

interactive

2 nodes

5

128

24

1d

for sinteractive command, longer interactive work

Use slurm partitions to see more details.

Job submission

Command

Description

sbatch

submit a job to queue (see standard options below)

srun

Within a running job script/environment: Run code using the allocated resources (see options below)

srun

On frontend: submit to queue, wait until done, show output. (see options below)

sinteractive

Submit job, wait, provide shell on node for interactive playing (X forwarding works, default partition interactive). Exit shell when done. (see options below)

srun --pty bash

(advanced) Another way to run interactive jobs, no X forwarding but simpler. Exit shell when done.

scancel <jobid>

Cancel a job in queue

salloc

(advanced) Allocate resources from frontend node. Use srun to run using those resources, exit to close shell when done. Read the description! (see options below)

scontrol

View/modify job and slurm configuration

Command

Option

Description

sbatch/srun/etc

-t, --time=hh:mm:ss

time limit

-t, --time=dd-hh

time limit, days-hours

-p, --partition=partition

job partition. Usually leave off and things are auto-detected.

--mem-per-cpu=n

request n MB of memory per core

--mem=n

request n MB memory per node

-c, --cpus-per-task=n

Allocate *n* CPU’s for each task. For multithreaded jobs. (compare ``–ntasks``: ``-c`` means the number of cores for each process started.)

-N, --nodes=n-m

allocate minimum of n, maximum of m nodes.

-n, --ntasks=n

allocate resources for and start n tasks (one task=one process started, it is up to you to make them communicate. However the main script runs only on first node, the sub-processes run with “srun” are run this many times.)

-J, --job-name=name

short job name

-o output

print output into file output

-e error

print errors into file error

--exclusive

allocate exclusive access to nodes. For large parallel jobs.

--constraint=feature

request feature (see slurm features for the current list of configured features, or Arch under the hardware list). Multiple with --constraint="hsw|skl".

--array=0-5,7,10-15

Run job multiple times, use variable $SLURM_ARRAY_TASK_ID to adjust parameters.

--gres=gpu

request a GPU, or --gres=gpu:n for multiple

--gres=spindle

request nodes that have disks, spindle:n, for a certain number of RAID0 disks

--mail-type=type

notify of events: BEGIN, END, FAIL, ALL, REQUEUE (not on triton) or ALL. MUST BE used with --mail-user= only

--mail-user=your@email

whome to send the email

srun

-N <N_nodes> hostname

Print allocated nodes (from within script)

Command

Description

slurm q ; slurm qq

Status of your queued jobs (long/short)

slurm partitions

Overview of partitions (A/I/O/T=active,idle,other,total)

slurm cpus <partition>

list free CPUs in a partition

slurm history [1day,2hour,…]

Show status of recent jobs

seff <jobid>

Show percent of mem/CPU used in job

slurm j <jobid>

Job details (only while running)

slurm s ; slurm ss <partition>

Show status of all jobs

sacct

Full history information (advanced, needs args)

Full slurm command help:

$ slurm

Show or watch job queue:
 slurm [watch] queue     show own jobs
 slurm [watch] q   show user's jobs
 slurm [watch] quick     show quick overview of own jobs
 slurm [watch] shorter   sort and compact entire queue by job size
 slurm [watch] short     sort and compact entire queue by priority
 slurm [watch] full      show everything
 slurm [w] [q|qq|ss|s|f] shorthands for above!
 slurm qos               show job service classes
 slurm top [queue|all]   show summary of active users
Show detailed information about jobs:
 slurm prio [all|short]  show priority components
 slurm j|job      show everything else
 slurm steps      show memory usage of running srun job steps
Show usage and fair-share values from accounting database:
 slurm h|history   show jobs finished since, e.g. "1day" (default)
 slurm shares
Show nodes and resources in the cluster:
 slurm p|partitions      all partitions
 slurm n|nodes           all cluster nodes
 slurm c|cpus            total cpu cores in use
 slurm cpus   cores available to partition, allocated and free
 slurm cpus jobs         cores/memory reserved by running jobs
 slurm cpus queue        cores/memory required by pending jobs
 slurm features          List features and GRES

Examples:
 slurm q
 slurm watch shorter
 slurm cpus batch
 slurm history 3hours

Other advanced commands (many require lots of parameters to be useful):

Command

Description

squeue

Full info on queues

sinfo

Advanced info on partitions

slurm nodes

List all nodes

Toolchains

Toolchain

Compiler version

MPI version

BLAS version

ScaLAPACK version

FFTW version

CUDA version

GOOLF Toolchains:

goolf/triton-2016a

GCC/4.9.3

OpenMPI/1.10.2

OpenBLAS/0.2.15

ScaLAPACK/2.0.2

FFTW/3.3.4

goolf/triton-2016b

GCC/5.4.0

OpenMPI/1.10.3

OpenBLAS/0.2.18

ScaLAPACK/2.0.2

FFTW/3.3.4

goolfc/triton-2016a

GCC/4.9.3

OpenMPI/1.10.2

OpenBLAS/0.2.15

ScaLAPACK/2.0.2

FFTW/3.3.4

7.5.18

goolfc/triton-2017a

GCC/5.4.0

OpenMPI/2.0.1

OpenBLAS/0.2.19

ScaLAPACK/2.0.2

FFTW/3.3.4

8.0.61

GMPOLF Toolchains:

gmpolf/triton-2016a

GCC/4.9.3

MPICH/3.0.4

OpenBLAS/0.2.15

ScaLAPACK/2.0.2

FFTW/3.3.4

gmpolfc/triton-2016a

GCC/4.9.3

MPICH/3.0.4

OpenBLAS/0.2.15

ScaLAPACK/2.0.2

FFTW/3.3.4

7.5.18

GMVOLF Toolchains:

gmvolf/triton-2016a

GCC/4.9.3

MVAPICH2/2.0.1

OpenBLAS/0.2.15

ScaLAPACK/2.0.2

FFTW/3.3.4

gmvolfc/triton-2016a

GCC/4.9.3

MVAPICH2/2.0.1

OpenBLAS/0.2.15

ScaLAPACK/2.0.2

FFTW/3.3.4

7.5.18

IOOLF Toolchains:

ioolf/triton-2016a

icc/2015.3.187

OpenMPI/1.10.2

OpenBLAS/0.2.15

ScaLAPACK/2.0.2

FFTW/3.3.4

IOMKL Toolchains:

iomkl/triton-2016a

icc/2015.3.187

OpenMPI/1.10.2

imkl/11.3.1.150

imkl/11.3.1.150

imkl/11.3.1.150

iomkl/triton-2016b

icc/2015.3.187

OpenMPI/1.10.3

imkl/11.3.1.150

imkl/11.3.1.150

imkl/11.3.1.150

iompi/triton-2017a

icc/2017.1.132

OpenMPI/2.0.1

imkl/2017.1.132

imkl/2017.1.132

imkl/2017.1.132

Hardware

Node name

Number of nodes

Node type

Year

Arch (constraint)

CPU type

Memory Configuration

Infiniband

GPUs

pe[1-48,65-81]

65

Dell PowerEdge C4130

2016

hsw avx avx2

2x12 core Xeon E5 2680 v3 2.50GHz

128GB DDR4-2133

FDR

pe[49-64,82]

17

Dell PowerEdge C4130

2016

hsw avx avx2

2x12 core Xeon E5 2680 v3 2.50GHz

256GB DDR4-2133

FDR

pe[83-91]

8

Dell PowerEdge C4130

2017

bdw avx avx2

2x14 core Xeon E5 2680 v4 2.40GHz

128GB DDR4-2400

FDR

c[579-628,639-698]

110

ProLiant XL230a Gen9

2017

hsw avx avx2

2x12 core Xeon E5 2690 v3 2.60GHz

128GB DDR4-2666

FDR

c[629-638]

10

ProLiant XL230a Gen9

2017

hsw avx avx2

2x12 core Xeon E5 2690 v3 2.60GHz

256GB DDR4-2400

FDR

skl[1-48]

48

Dell PowerEdge C6420

2019

skl avx avx2 avx512

2x20 core Xeon Gold 6148 2.40GHz

192GB DDR4-2667

EDR

csl[1-48]

48

Dell PowerEdge C6420

2020

csl avx avx2 avx512

2x20 core Xeon Gold 6248 2.50GHz

192GB DDR4-2667

EDR

fn3

1

Dell PowerEdge R940

2020

avx avx2 avx512

4x20 core Xeon Gold 6148 2.40GHz

2TB DDR4-2666

EDR

gpu[1-10]

10

Dell PowerEdge C4140

2020

skl avx avx2 avx512 volta

2x8 core Intel Xeon Gold 6134 @ 3.2GHz

384GB DDR4-2667

EDR

4x V100 32GB

gpu[11-17]

7

Dell PowerEdge XE8545

2021

milan avx avx2 a100

2x24 core AMD EPYC 7413 @ 2.65GHz

503GB DDR4-3200

EDR

4x A100 80GB

gpu[20-22]

3

Dell PowerEdge C4130

2016

hsw avx avx2 kepler

2x6 core Xeon E5 2620 v3 2.50GHz

128GB DDR4-2133

EDR

4x2 GPU K80

gpu[23-27]

5

Dell PowerEdge C4130

2017

hsw avx avx2 pascal

2x12 core Xeon E5-2680 v3 @ 2.5GHz

256GB DDR4-2400

EDR

4x P100

gpu[28-37]

10

Dell PowerEdge C4140

2019

skl avx avx2 avx512 volta

2x8 core Intel Xeon Gold 6134 @ 3.2GHz

384GB DDR4-2667

EDR

4x V100 32GB

dgx[1-7]

7

Nvidia DGX-1

2018

bdw avx avx2 volta

2x20 core Xeon E5-2698 v4 @ 2.2GHz

512GB DDR4-2133

EDR

8x V100

gpuamd1

1

Dell PowerEdge R7525

2021

rome avx avx2 mi100

2x8 core AMD EPYC 7262 @3.2GHz

250GB DDR4-3200

EDR

3x MI100 32GB

Node type

CPU count

48GB Xeon Westmere (2012)

1404

24GB Xeon Westmere + 2x GPU (2012)

120

96GB Xeon Westmere (2012)

288

1TB Xeon Westmere (2012)

48

256GB Xeon Ivy Bridge (2014)

480

64GB Xeon Ivy Bridge (2014)

480

128GB Xeon Haswell (2016)

1224

256GB Xeon Haswell (2016)

360

128GB Xeon Haswell + 4x GPU (2016)

36

GPUs

Card

total amount

nodes

architecture

compute threads per GPU

memory per card

CUDA compute capability

Slurm feature name

Slurm gres name

Tesla K80*

12

gpu[20-22]

Kepler

2x2496

2x12GB

3.7

kepler

teslak80

Tesla P100

20

gpu[23-27]

Pascal

3854

16GB

6.0

pascal

teslap100

Tesla V100

40

gpu[1-10]

Volta

5120

32GB

7.0

volta

v100

Tesla V100

40

gpu[28-37]

Volta

5120

32GB

7.0

volta

v100

Tesla V100

16

dgx[1-7]

Volta

5120

16GB

7.0

volta

v100

Tesla A100

28

gpu[11-17]

Ampere

7936

80GB

8.0

a100