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.

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
ivb[1-24]	24	ProLiant SL230s G8		ivb avx	2x10 core Xeon E5 2680 v2 2.80GHz	256GB DDR3-1667	FDR
ivb[25-48]	24	ProLiant SL230s G8		ivb avx	2x10 core Xeon E5 2680 v2 2.80GHz	64GB DDR3-1667	FDR
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[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
dgx[01-02]	2	Nvidia DGX-1	2018	bdw avx avx2 volta	2x20 core Xeon E5-2698 v4 @ 2.2GHz	512GB DDR4-2133	EDR	8x V100
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

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[01-02]	Volta	5120	16GB	7.0	volta	v100