Starting out on Palmetto
When you first logged into Palmetto, you will be presented with a welcome message. This is called the motd, or Message of the day. This message typically contains a quick introduction to Palmetto, links to user guides, and a list of useful commands. Information about upcoming maintenance will also be posted here.
Available storage on Palmetto
Various filesystems are available for users to store data. These differ in capacity, data-persistence, and efficiency, and it is important that users understand which filesystem to use under which circumstances.
Home and scratch directories
| Location | Available space | Notes |
|---|---|---|
/home/username |
100 GB per user | Backed-up nightly, permanent storage space accessible from all nodes |
/scratch1/username |
1.9 PB shared by all users | Not backed up, temporary work space accessible from all nodes, BeeGFS Parallel File System |
/scratch2/username |
189 TB shared by all users | Not backed up, temporary work space accessible from all nodes, ZFS |
/local_scratch |
Varies between nodes (99GB-3.6TB) | Per-node temporary work space, accessible only for the lifetime of job |
- The
/home,scratch1,/scratch2directories are shared by all nodes. - Each node has its own
/local_scratchdirectory. - The parallel file system,
/scratch1, is best suited for workflows issuing large read or write requests or creating a large number of files and directories. A large read or write is when data is accessed in large chunks, such as 1MB at a time. A large number of files and directories refers to workflows that generate thousands of files and directories during its processing./scratch1is also ideal for non-parallel jobs with heavy I/O needs. - The remaining scratch system, /scratch2, is best suited for workflows without heavy I/O needs. They are much like using your home directory but you are not limited to space, and you are allowed to run jobs against these file systems.
All data in the /home directory is permanent (not automatically deleted) and backed-up on a nightly basis.
If you lose data in the /home directory, it may be possible to recover it if it was previously backed up.
Data in the /scratch1 and /scratch2 directories is not backed up, and any data that is untouched for 30 days is automatically
removed from the /scratch directories. Data that cannot easily be reproduced should not be stored in the /scratch directories,
and any data that is not required should be removed as soon as possible.
See this guide on how to choose the appropriate filesystem for your work.
Temporary reservations
All users may apply for temporary reservation of the following resources:
- Up to 150 TB of long-term storage
- Up to 8.5 TB of fast SSD scratch space
All requests will be reviewed by Clemson University Computational Advisory Team (CU-CAT). Reservation requests can be made here.
Purchased storage
Users or groups may also purchase storage in 1 TB increments. For details about purchased storage, please contact the Palmetto support staff (ithelp@clemson.edu, and include the word "Palmetto" in the subject line).
Data transfer in and out of Palmetto
Data can be transferred in and out of Palmetto via the following recommended methods:
- Direct transfer using login001: Data size smaller than 10GB.
- Direct transfer using xfer01: Data size between 10-100GB.
- Transfer using Globus: Data size larger than 100GB.
Direct transfer
On Windows machines, using the MobaXterm SSH client, the built-in file browser can be used (SCP tab of the side window). Using the Upload (green arrow pointing up) and and Dowload (blue arrow pointing down) buttons at the top of the SCP tab, you can easily transfer small files between Palmetto and your local computer.
You can setup an SSH session to xfer01-ext.palmetto.clemson.edu similar to how you login to login001.palmetto.clemson.edu.
On Unix systems, you can use the scp (secure copy) command to
perform file transfers. The general form of the scp command is:
$ scp <path_to_source> username@xfer01-ext.palmetto.clemson.edu:<path_to_destination>
For example, here is the scp command to copy a file from the
current directory on your local machine to your
/home/username directory on Palmetto
(this command is entered into a terminal
when not logged-in to Palmetto):
$ scp myfile.txt username@xfer01-ext.palmetto.clemson.edu:/home/username
... and to do the same in reverse, i.e., copy from Palmetto to your local machine. (again, from a terminal running on your local machine, not on Palmetto):
$ scp username@xfer01-ext.palmetto.clemson.edu:/home/username/myfile.txt .
The . represents the working directory on the local machine.
For folders, include the -r switch:
$ scp -r myfolder username@xfer01-ext.palmetto.clemson.edu:/home/username
Transfer large files using Globus
For larger files, we recommend using the Globus file transfer application. Here, we demonstrate how to use Globus Online to transfer files between Palmetto and a local machine (laptop). However, Globus can be used for file transfers to/from other locations as well.
-
You will need to have a Globus account set up to begin. Visit https://www.globus.org/ and set up a Globus account.
-
To begin transfering files, navigate to the Globus Online transfer utility here: https://www.globus.org/app/transfer.
-
The transfer utility allows you to transfer files between "endpoints". You will need to set your local machine as a Globus Connect Personal Endpoint for the file transfer. As a part of this step, you must install the Globus Connect Personal application (see here: https://www.globus.org/app/endpoints/create-gcp ). After installing, ensure that the application is running. You should then be able to set your local machine as one endpoint. In the figure below, the endpoint is named
My Personal Mac. -
As the second endpoint, choose
clemson#xfer01-ext.clemson.edu. -
Make sure that you check the options to
verify file integrity after transferandencrypt transferunder theTransfer & Sync Options. -
You can now transfer files between any locations on your local machine and the Palmetto cluster.
Viewing all Palmetto's hardware
The login node login001 (the node that users first log-in to), is not meant for running computationally intensive tasks.
Instead, users must reserve hardware from the compute nodes of the cluster. Currently, Palmetto cluster has over 2040 compute
nodes. The hardware configuration of the different nodes is available in the file /etc/hardware-table:
[lngo@login001 ~]$ cat /etc/hardware-table
PALMETTO HARDWARE TABLE Last updated: Jun 15 2020
PHASE COUNT MAKE MODEL CHIP(0) CORES RAM(1) /local_scratch Interconnect GPUs SSD
0 6 HP DL580 Intel Xeon 7542 24 505 GB(2) 99 GB 1ge 0 0
0 5 Dell R820 Intel Xeon E5-4640 32 750 GB(2) 740 GB(12) 10ge 0 0
0 1 HP DL560 Intel Xeon E5-4627v4 40 1.5 TB(2) 881 GB 10ge 0 0
0 1 Dell R830 Intel Xeon E5-4627v4 40 1.0 TB(2) 880 GB 10ge 0 0
0 1 HPE DL560 Intel Xeon 6138G 80 1.5 TB(2) 3.6 TB 10ge 0 0
0 1 HPE DL560 Intel Xeon 6148G 80 1.5 TB(2) 3.6 TB 10ge 0 0
0 1 HPE DL560 Intel Xeon 6148G 80 1.5 TB(2) 745 GB(12) 10ge 0 0
1a 116 Dell R610 Intel Xeon E5520 8 31 GB 220 GB 1g 0 0
1b 40 Dell R610 Intel Xeon E5645 12 92 GB 220 GB 1g 0 0
2a 30 Dell R620 Intel Xeon E5-2660 16 375 GB 2.7 TB 1g 0 0
2b 134 Dell PE1950 Intel Xeon E5410 8 15 GB 37 GB 1g 0 0
3 224 Sun X2200 AMD Opteron 2356 8 15 GB 193 GB 1g 0 0
4 323 IBM DX340 Intel Xeon E5410 8 15 GB 111 GB 1g 0 0
5a 310 Sun X6250 Intel Xeon L5420 8 31 GB 31 GB 1g 0 0
5b 9 Sun X4150 Intel Xeon E5410 8 31 GB 99 GB 1g 0 0
5c 19 Dell R510 Intel Xeon E5640 8 22 GB 7 TB 1g 0 0
5d 20 Dell R520 Intel Xeon E5-2450 12 46 GB 2.7 TB 1g 0 0
6 66 HP DL165 AMD Opteron 6176 24 46 GB 193 GB 1g 0 0
7a 42 HP SL230 Intel Xeon E5-2665 16 62 GB 240 GB 56g, fdr, 10ge 0 0
7b 12 HP SL250s Intel Xeon E5-2665 16 62 GB 240 GB 56g, fdr, 10ge 2(3) 0
8a 71 HP SL250s Intel Xeon E5-2665 16 62 GB 900 GB 56g, fdr, 10ge 2(4) 300 GB(7)
8b 57 HP SL250s Intel Xeon E5-2665 16 62 GB 420 GB 56g, fdr, 10ge 2(4) 0
8c 88 Dell PEC6220 Intel Xeon E5-2665 16 62 GB 350 GB 56g, fdr, 10ge 0 0
9 72 HP SL250s Intel Xeon E5-2665 16 126 GB 420 GB 56g, fdr, 10ge 2(4) 0
10 80 HP SL250s Intel Xeon E5-2670v2 20 126 GB 800 GB 56g, fdr, 10ge 2(4) 0
11a 40 HP SL250s Intel Xeon E5-2670v2 20 126 GB 800 GB 56g, fdr, 10ge 2(6) 0
11b 4 HP SL250s Intel Xeon E5-2670v2 20 126 GB 800 GB 56g, fdr, 10ge 0 0
12 30 Lenovo NX360M5 Intel Xeon E5-2680v3 24 126 GB 800 GB 56g, fdr, 10ge 2(6) 0
13 24 Dell C4130 Intel Xeon E5-2680v3 24 126 GB 1.8 TB 56g, fdr, 10ge 2(6) 0
14 12 HPE XL1X0R Intel Xeon E5-2680v3 24 126 GB 880 GB 56g, fdr, 10ge 2(6) 0
15 32 Dell C4130 Intel Xeon E5-2680v3 24 126 GB 1.8 TB 56g, fdr, 10ge 2(6) 0
16 40 Dell C4130 Intel Xeon E5-2680v4 28 126 GB 1.8 TB 56g, fdr, 10ge 2(8) 0
17 20 Dell C4130 Intel Xeon E5-2680v4 28 126 GB 1.8 TB 56g, fdr, 10ge 2(8) 0
18a 2 Dell C4140 Intel Xeon 6148G 40 372 GB 1.9 TB(12) 100g, hdr, 10ge 4(9) 0
18b 65 Dell R740 Intel Xeon 6148G 40 372 GB 1.8 TB 100g, hdr, 25ge 2(10) 0
18c 10 Dell R740 Intel Xeon 6148G 40 748 GB 1.8 TB 100g, hdr, 25ge 2(10) 0
19a 28 Dell R740 Intel Xeon 6248G 40 372 GB 1.8 TB 100g, hdr, 25ge 2(10) 0
19b 4 HPE XL170 Intel Xeon 6252G 48 372 GB 1.8 TB 56g, fdr, 10ge 0 0
*** PBS resource requests are always lowercase ***
(0) CHIP has 3 resources: chip_manufacturer, chip_model, chip_type
(1) Leave 2 or 3GB for the operating system when requesting memory in PBS jobs
(2) Specify queue "bigmem" to access the large memory machines, only ncpus and mem are valid PBS resource requests
(3) 2 NVIDIA Tesla M2075 cards per node, use resource request "ngpus=[1|2]" and "gpu_model=m2075"
(4) 2 NVIDIA Tesla K20m cards per node, use resource request "ngpus=[1|2]" and "gpu_model=k20"
(5) 2 NVIDIA Tesla M2070-Q cards per node, use resource request "ngpus=[1|2]" and "gpu_model=m2070q"
(6) 2 NVIDIA Tesla K40m cards per node, use resource request "ngpus=[1|2]" and "gpu_model=k40"
(7) Use resource request "ssd=true" to request a chunk with SSD in location /ssd1, /ssd2, and /ssd3 (100GB max each)
(8) 2 NVIDIA Tesla P100 cards per node, use resource request "ngpus=[1|2]" and "gpu_model=p100"
(9) 4 NVIDIA Tesla V100 cards per node with NVLINK2, use resource request "ngpus=[1|2|3|4]" and "gpu_model=v100nv"
(10)2 NVIDIA Tesla V100 cards per node, use resource request "ngpus=[1|2]" and "gpu_model=v100"
(11)Phase18a nodes contain NVMe storage for /local_scratch.
(12)local_scratch is housed entirely on SSD
The compute nodes are divided into "phases" (currently phases 0-19). Each phase is composed of several nodes with identical configuration, e.g., each node in phase 5a has 8 cores, 32 GB ram, 31 GB local disk space, and 10 Gbps Myrinet interconnect.
A useful command on the login node is whatsfree, which gives information
about how many nodes from each phase are currently in use, free, or offlined
for maintenance.
[lngo@login001 ~]$ ./whatsfree
Mon Aug 03 2020 22:37:26
TOTAL NODES: 2102 NODES FREE: 2035 NODES OFFLINE: 14 NODES RESERVED: 0
PHASE 0 TOTAL = 16 FREE = 15 OFFLINE = 0 BIGMEM nodes: (6) 24cores/500GB, (5) 32cores/750GB, (3) 80cores/1.5TB, (1) 40cores/1.5TB, (1) 40cores/1TB, (1) 64cores/2TB
PHASE 1a TOTAL = 117 FREE = 94 OFFLINE = 0 TYPE = Dell R610 Intel Xeon E5520, 8 cores, 31GB, 1g
PHASE 1b TOTAL = 40 FREE = 40 OFFLINE = 0 TYPE = Dell R610 Intel Xeon E5645, 12 cores, 94GB, 1g
PHASE 2a TOTAL = 30 FREE = 30 OFFLINE = 0 TYPE = Dell R620 Intel Xeon E5-2660 16 cores, 382GB, 1g
PHASE 2b TOTAL = 162 FREE = 162 OFFLINE = 0 TYPE = Dell PE1950 Intel Xeon E5410, 8 cores, 15GB, 1g
PHASE 3 TOTAL = 224 FREE = 224 OFFLINE = 0 TYPE = Sun X2200 AMD Opteron 2356, 8 cores, 15GB, 1g
PHASE 4 TOTAL = 319 FREE = 319 OFFLINE = 0 TYPE = IBM DX340 Intel Xeon E5410, 8 cores, 15GB, 1g
PHASE 5a TOTAL = 308 FREE = 308 OFFLINE = 0 TYPE = Sun X6250 Intel Xeon L5420, 8 cores, 30GB, 1g
PHASE 5b TOTAL = 9 FREE = 9 OFFLINE = 0 TYPE = Sun X4150 Intel Xeon E5410, 8 cores, 15GB, 1g
PHASE 5c TOTAL = 34 FREE = 34 OFFLINE = 0 TYPE = Dell R510 Intel Xeon E5460, 8 cores, 23GB, 1g
PHASE 5d TOTAL = 23 FREE = 23 OFFLINE = 0 TYPE = Dell R520 Intel Xeon E5-2450 12 cores, 46GB, 1g
PHASE 6 TOTAL = 65 FREE = 64 OFFLINE = 0 TYPE = HP DL165 AMD Opteron 6176, 24 cores, 46GB, 1g
PHASE 7a TOTAL = 42 FREE = 38 OFFLINE = 0 TYPE = HP SL230 Intel Xeon E5-2665, 16 cores, 62GB, FDR
PHASE 7b TOTAL = 12 FREE = 0 OFFLINE = 0 TYPE = HP SL250s Intel Xeon E5-2665, 16 cores, 62GB, FDR, M2075
PHASE 8a TOTAL = 71 FREE = 61 OFFLINE = 0 TYPE = HP SL250s Intel Xeon E5-2665, 16 cores, 62GB, FDR, K20, SSD
PHASE 8b TOTAL = 57 FREE = 57 OFFLINE = 0 TYPE = HP SL250s Intel Xeon E5-2665, 16 cores, 62GB, FDR, K20
PHASE 8c TOTAL = 88 FREE = 78 OFFLINE = 10 TYPE = Dell PEC6220 Intel Xeon E5-2665, 16 cores, 62GB, 10ge
PHASE 9 TOTAL = 72 FREE = 69 OFFLINE = 0 TYPE = HP SL250s Intel Xeon E5-2665, 16 cores, 125GB, FDR, K20, 10ge
PHASE 10 TOTAL = 80 FREE = 76 OFFLINE = 0 TYPE = HP SL250s Intel Xeon E5-2670v2, 20 cores, 125GB, FDR, K20, 10ge
PHASE 11a TOTAL = 40 FREE = 39 OFFLINE = 0 TYPE = HP SL250s Intel Xeon E5-2670v2, 20 cores, 125GB, FDR, K40, 10ge
PHASE 11b TOTAL = 4 FREE = 4 OFFLINE = 0 TYPE = HP SL250s Intel Xeon E5-2670v2, 20 cores, 125GB, FDR, Phi, 10ge
PHASE 12 TOTAL = 30 FREE = 30 OFFLINE = 0 TYPE = Lenovo MX360M5 Intel Xeon E5-2680v3, 24 cores, 125GB, FDR, K40, 10ge
PHASE 13 TOTAL = 24 FREE = 24 OFFLINE = 0 TYPE = Dell C4130 Intel Xeon E5-2680v3, 24 cores, 125GB, FDR, K40, 10ge
PHASE 14 TOTAL = 12 FREE = 12 OFFLINE = 0 TYPE = HP XL190r Intel Xeon E5-2680v3, 24 cores, 125GB, FDR, K40, 10ge
PHASE 15 TOTAL = 32 FREE = 32 OFFLINE = 0 TYPE = Dell C4130 Intel Xeon E5-2680v3, 24 cores, 125GB, FDR, K40, 10ge
PHASE 16 TOTAL = 40 FREE = 37 OFFLINE = 0 TYPE = Dell C4130 Intel Xeon E5-2680v4, 28 cores, 125GB, FDR, P100, 10ge
PHASE 17 TOTAL = 20 FREE = 20 OFFLINE = 0 TYPE = Dell C4130 Intel Xeon E5-2680v4, 28 cores, 125GB, FDR, P100, 10ge
PHASE 18a TOTAL = 2 FREE = 0 OFFLINE = 0 TYPE = Dell C4140 Intel Xeon 6148G, 40 cores, 372GB, HDR, V100nv, 10ge
PHASE 18b TOTAL = 65 FREE = 55 OFFLINE = 0 TYPE = Dell R740 Intel Xeon 6148G, 40 cores, 372GB, HDR, V100, 25ge
PHASE 18c TOTAL = 10 FREE = 10 OFFLINE = 0 TYPE = Dell R740 Intel Xeon 6148G, 40 cores, 748GB, HDR, V100, 25ge
PHASE 19a TOTAL = 28 FREE = 24 OFFLINE = 4 TYPE = Dell R740 Intel Xeon 6248G, 40 cores, 372GB, HDR, V100, 25ge
PHASE 19b TOTAL = 4 FREE = 3 OFFLINE = 0 TYPE = HPE XL170 Intel Xeon 6252G, 48 cores, 372GB, 10ge
PHASE 20 TOTAL = 22 FREE = 22 OFFLINE = 0 TYPE = Dell R740 Intel Xeon 6238R, 56 cores, 372GB, HDR, V100S, 25ge
NOTE: Your job will land on the oldest phase that satisfies your PBS resource requests.
Later sections of this guide will describe how to submit jobs to the cluster, i.e., reserve compute nodes for running computational tasks.
Checking and using available software
The Palmetto cluster provides a limited number of packages (including site-licensed packages),
that can be used by all Palmetto users. These packages are available as modules,
and must be activated/deactivated using the module command:
| Command | Purpose |
|---|---|
module avail |
List all packages available (on current system) |
module add package/version |
Add a package to your current shell environment |
module list |
List packages you have loaded |
module rm package/version |
Remove a currently loaded package |
module purge |
Remove all currently loaded packages |
For example, to load the GCC (v9.3.0), CUDA Toolkit (v10.2.89) and OpenMPI (v3.1.6) modules, you can use the command:
[lngo@login001 ~]$ module add gcc/9.3.0-gcc/8.3.1 cuda/10.2.89-gcc/8.3.1 openmpi/3.1.6-gcc/8.3.1-cuda10_2-ucx
Then, check the loaded modules and version of gcc:
[lngo@login001 ~]$ module list
Currently Loaded Modules:
1) gmp/6.1.2-gcc/8.3.1 2) gcc/9.3.0-gcc/8.3.1 3) cuda/10.2.89-gcc/8.3.1 4) openmpi/3.1.6-gcc/8.3.1-cuda10_2-ucx
[lngo@login001 ~]$ gcc --version
gcc (Spack GCC) 9.3.0
Copyright (C) 2019 Free Software Foundation, Inc.
This is free software; see the source for copying conditions. There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
[lngo@login001 ~]$
Some modules when loaded, implicitly load other modules as well. If you use some modules to compile/install some software, then you will probably have to load them when running that software as well, otherwise you may see errors about missing libraries/headers. Modules do not remain loaded when you log out and log back in, i.e., they are active only for the current session - so you will need to load them for every session.
As an exercise, examine the environment variables PATH, LIBRARY_PATH, etc., before and after loading some module:
$ echo $PATH
$ module add anaconda3/2019.10-gcc/8.3.1
$ echo $PATH
Job submission and control on Palmetto
The Palmetto cluster uses the Portable Batch Scheduling system (PBS) to manage jobs. Here are some basic PBS commands for submitting, querying and deleting jobs:
| Command | Action |
|---|---|
qsub -I |
Submit an interactive job (reserves 1 core, 1gb RAM, 30 minutes walltime) |
qsub xyz.pbs |
Submit the job script xyz.pbs |
qstat <job id> |
Check the status of the job with given job ID |
qstat -u <username> |
Check the status of all jobs submitted by given username |
qstat -xf <job id> |
Check detailed information for job with given job ID |
qsub -q <queuename> xyz.pbs |
Submit to queue queuename |
qdel <job id> |
Delete the job (queued or running) with given job ID |
qpeek <job id> |
"Peek" at the standard output from a running job |
qdel -Wforce <job id> |
Use when job not responding to just qdel |
For more details and more advanced commands for submitting and controlling jobs, please refer to the PBS Professional User's Guide.
Starting an interactive job
An interactive job can be started using the qsub command.
Here is an example of an interactive job:
[username@login001 ~]$ qsub -I -l select=1:ncpus=2:mem=4gb,walltime=4:00:00
qsub (Warning): Interactive jobs will be treated as not rerunnable
qsub: waiting for job 8730.pbs02 to start
qsub: job 8730.pbs02 ready
[username@node0021 ~]$ module add anaconda3/2019.10-gcc/8.3.1
[username@node0021 ~]$ python runsim.py
.
.
.
[username@node0021 ~]$ exit
[username@login001 ~]$
Above, we request an interactive job using 1 "chunk" of hardware (select=1),
2 CPU cores per "chunk", and 4gb of RAM per "chunk", for a wall time of 4 hours.
Once these resources are available, we receive a Job ID (8730.pbs02),
and a command-line session running on node0021.
Submit a batch job
Interactive jobs require you to be logged-in while your tasks are running. In contrast, you may logout after submitting a batch job, and examine the results at a later time. This is useful when you need to run several computational tasks to the cluster, and/or when your computational tasks are expected to run for a long time.
To submit a batch job, you must prepare a batch script
(you can do this using an editor like vim or nano).
Following is an example of a batch script (call it example.pbs).
In the batch job below, we really don't do anything useful
(just sleep or "do nothing" for 60 seconds),
#PBS -N example
#PBS -l select=1:ncpus=1:mem=2gb,walltime=00:10:00
module add gcc/9.3.0-gcc/8.3.1
cd /home/username
echo Hello World from `hostname`
sleep 60
After saving the above file, you can submit the batch job using qsub:
[username@login001 ~]$ qsub example.pbs
8738.pbs02
The returned job ID can be used to query the status of the job (using qstat)
(or delete it using qdel):
[username@login001 ~]$ qstat 8738.pbs02
Job id Name User Time Use S Queue
---------------- ---------------- ---------------- -------- - -----
8738.pbs02 example username 00:00:00 R c1_solo
Once the job is completed, you will see the files example.o8738 (containing output if any)
and example.e8738 (containing errors if any) from your job.
[username@login001 ~]$ cat example.o8738
Hello World from node0230.palmetto.clemson.edu
PBS job options
The following switches can be used either
with qsub on the command line,
or with a #PBS directive in a batch script.
| Parameter | Purpose | Example |
|---|---|---|
-N |
Job name (7 characters) | -N maxrun1 |
-l |
Job limits (lowercase L), hardware & other requirements for job. | -l select=1:ncpus=8:mem=1gb |
-q |
Queue to direct this job to (workq is the default, supabad is an example of specific research group's job queue) |
-q supabad |
-o |
Path to stdout file for this job (environment variables are not accepted here) | -o stdout.txt |
-e |
Path to stderr file for this job (environment variables are not accepted here) | -e stderr.txt |
-m |
mail event: Email from the PBS server with flag abort\ begin\ end \ or no mail for job's notification. | -m abe |
-M |
Specify list of user to whom mail about the job is sent. The user list argument is of the form: [user[@host],user[@host],...]. If -M is not used and -m is specified, PBS will send email to userid@clemson.edu | -M user1@domain1.com,user2@domain2.com |
-j oe |
Join the output and error streams and write to a single file | -j oe |
-r n |
Ask PBS not to restart the job if it's failed | -r n |
For example, in a batch script:
#PBS -N hydrogen
#PBS -l select=1:ncpus=24:mem=200gb,walltime=4:00:00
#PBS -q bigmem
#PBS -m abe
#PBS -M userid@domain.com
#PBS -j oe
And in an interactive job request on the command line:
$ qsub -I -N hydrogen -q bigmem -j oe -l select=1:ncpus=24:mem=200gb,walltime=4:00:00
For more detailed information, please take a look at:
$man qsub
Resource limits specification
The -l switch provided to qsub or along with the #PBS directive
can be used to specify the amount and kind of compute hardware (cores, memory, GPUs, interconnect, etc.,),
its location, i.e., the node(s) and phase from which to request hardware,
| Option | Purpose |
|---|---|
select |
Number of chunks and resources per chunk. Two or more "chunks" can be placed on a single node, but a single "chunk" cannot span more than one node. |
walltime |
Expected wall time of job (job is terminated after this time) |
place |
Controls the placement of the different chunks |
Here are some examples of resource limits specification:
-l select=1:ncpus=8:chip_model=opteron:interconnect=10g
-l select=1:ncpus=16:chip_type=e5-2665:interconnect=56g:mem=62gb,walltime=16:00:00
-l select=1:ncpus=8:chip_type=2356:interconnect=10g:mem=15gb
-l select=1:ncpus=1:node_manufacturer=ibm:mem=15gb,walltime=00:20:00
-l select=1:ncpus=4:mem=15gb:ngpus=2,walltime=00:20:00
-l select=1:ncpus=4:mem=15gb:ngpus=1:gpu_model=k40,walltime=00:20:00
-l select=1:ncpus=2:mem=15gb:host=node1479,walltime=00:20:00
-l select=2:ncpus=2:mem=15gb,walltime=00:20:00,place=scatter # force each chunk to be on a different node
-l select=2:ncpus=2:mem=15gb,walltime=00:20:00,place=pack # force each chunk to be on the same node
and examples of options you can use in the job limit specification:
chip_manufacturer=amd
chip_manufacturer=intel
chip_model=opteron
chip_model=xeon
chip_type=e5345
chip_type=e5410
chip_type=l5420
chip_type=x7542
chip_type=2356
chip_type=6172
chip_type=e5-2665
node_manufacturer=dell
node_manufacturer=hp
node_manufacturer=ibm
node_manufacturer=sun
gpu_model=k20
gpu_model=k40
interconnect=1g (1 Gbps Ethernet)
interconnect=10ge (10 Gbps Ethernet)
interconnect=56g (56 Gbps FDR InfiniBand, same as fdr)
interconnect=fdr (56 Gbps FDR InfiniBand, same as 56g)
ssd=true (Use a node with an SSD hard drive)
Querying job information and deleting jobs
The qstat command can be used
to query the status of a particular job:
$ qstat 7600424.pbs02
Job id Name User Time Use S Queue
---------------- ---------------- ---------------- -------- - -----
7600424.pbs02 pi-mpi username 00:00:00 R c1_single
To list the job IDs and status of all your jobs,
you can use the -u switch:
$ qstat -u username
pbs02:
Req'd Req'd Elap
Job ID Username Queue Jobname SessID NDS TSK Memory Time S Time
--------------- -------- -------- ---------- ------ --- --- ------ ----- - -----
7600567.pbs02 username c1_singl pi-mpi-1 1382 4 8 4gb 00:05 R 00:00
7600569.pbs02 username c1_singl pi-mpi-2 20258 4 8 4gb 00:05 R 00:00
7600570.pbs02 username c1_singl pi-mpi-3 2457 4 8 4gb 00:05 R 00:00
Once a job has finished running,
qstat -xf can be used to obtain detailed job information:
$ qstat -xf 7600424.pbs02
Job Id: 7600424.pbs02
Job_Name = pi-mpi
Job_Owner = username@login001.palmetto.clemson.edu
resources_used.cpupercent = 103
resources_used.cput = 00:00:04
resources_used.mem = 45460kb
resources_used.ncpus = 8
resources_used.vmem = 785708kb
resources_used.walltime = 00:02:08
job_state = F
queue = c1_single
server = pbs02
Checkpoint = u
ctime = Tue Dec 13 14:09:32 2016
Error_Path = login001.palmetto.clemson.edu:/home/username/MPI/pi-mpi.e7600424
exec_host = node0088/1*2+node0094/1*2+node0094/2*2+node0085/0*2
exec_vnode = (node0088:ncpus=2:mem=1048576kb:ngpus=0:nphis=0)+(node0094:ncp
us=2:mem=1048576kb:ngpus=0:nphis=0)+(node0094:ncpus=2:mem=1048576kb:ngp
us=0:nphis=0)+(node0085:ncpus=2:mem=1048576kb:ngpus=0:nphis=0)
Hold_Types = n
Join_Path = oe
Keep_Files = n
Mail_Points = a
Mail_Users = username@clemson.edu
mtime = Tue Dec 13 14:11:42 2016
Output_Path = login001.palmetto.clemson.edu:/home/username/MPI/pi-mpi.o760042
4
Priority = 0
qtime = Tue Dec 13 14:09:32 2016
Rerunable = True
Resource_List.mem = 4gb
Resource_List.mpiprocs = 8
Resource_List.ncpus = 8
Resource_List.ngpus = 0
Resource_List.nodect = 4
Resource_List.nphis = 0
Resource_List.place = free:shared
Resource_List.qcat = c1_workq_qcat
Resource_List.select = 4:ncpus=2:mem=1gb:interconnect=1g:mpiprocs=2
Resource_List.walltime = 00:05:00
stime = Tue Dec 13 14:09:33 2016
session_id = 2708
jobdir = /home/username
substate = 92
Variable_List = PBS_O_SYSTEM=Linux,PBS_O_SHELL=/bin/bash,
PBS_O_HOME=/home/username,PBS_O_LOGNAME=username,
PBS_O_WORKDIR=/home/username/MPI,PBS_O_LANG=en_US.UTF-8,
PBS_O_PATH=/software/examples/:/home/username/local/bin:/usr/lib64/qt-3
.3/bin:/opt/pbs/default/bin:/opt/gold/bin:/usr/local/bin:/bin:/usr/bin:
/usr/local/sbin:/usr/sbin:/sbin:/opt/mx/bin:/home/username/bin,
PBS_O_MAIL=/var/spool/mail/username,PBS_O_QUEUE=c1_workq,
PBS_O_HOST=login001.palmetto.clemson.edu
comment = Job run at Tue Dec 13 at 14:09 on (node0088:ncpus=2:mem=1048576kb
:ngpus=0:nphis=0)+(node0094:ncpus=2:mem=1048576kb:ngpus=0:nphis=0)+(nod
e0094:ncpus=2:mem=1048576kb:ngpus=0:nphis=0)+(node0085:ncpus=2:mem=1048
576kb:ngpus=0:nphis=0) and finished
etime = Tue Dec 13 14:09:32 2016
run_count = 1
Stageout_status = 1
Exit_status = 0
Submit_arguments = job.sh
history_timestamp = 1481656302
project = _pbs_project_default
Similarly, to get detailed information about
a running job, you can use qstat -f.
To delete a job (whether in queued, running or error status),
you can use the qdel command.
$ qdel 7600424.pbs02
Job limits on Palmetto
Walltime
Jobs running in phases 1-6 of the cluster (nodes with interconnect 1g)
can run for a maximum walltime of 168 hours (7 days).
Job running in phases 7 and higher of the cluster can run for a maximum walltime of 72 hours (3 days).
Number of jobs
When you submit a job, it is forwarded to a specific execution queue based on job critera (how many cores, RAM, etc.). There are three classes of execution queues:
-
MX queues (
c1_queues): jobs submitted to run on the older hardware (phases 1-6) will be forwarded to theses queues. -
IB queues (
c2_queues): jobs submitted to run the newer hardware (phases 7 and up) will be forwarded to these queues. -
GPU queues (
gpu_queues): jobs that request GPUs will be forwarded to these queues. -
bigmem queue: jobs submitted to the large-memory machines (phase 0).
Each execution queue has its own limits for
how many jobs can be running at one time,
and how many jobs can
be waiting in that execution queue.
The maximum number of running jobs per user in
execution queues may vary
throughout the day depending on cluster load.
Users can see what the current limits
are using the checkqueuecfg command:
[lngo@login001 ~]$ checkqueuecfg
1G QUEUES min_cores_per_job max_cores_per_job max_mem_per_queue max_jobs_per_queue max_walltime
c1_solo 1 1 800gb 100 168:00:00
c1_single 2 24 1200gb 10 168:00:00
c1_tiny 25 128 5120gb 5 168:00:00
c1_small 129 512 4096gb 1 168:00:00
c1_medium 513 2048 0gb 0 168:00:00
c1_large 2049 4096 0gb 0 168:00:00
IB QUEUES min_cores_per_job max_cores_per_job max_mem_per_queue max_jobs_per_queue max_walltime
c2_single 1 40 4000gb 10 72:00:00
c2_tiny 41 200 6400gb 2 72:00:00
c2_small 201 512 6144gb 1 72:00:00
c2_medium 513 2048 16384gb 1 72:00:00
c2_large 2049 4096 0gb 0 72:00:00
GPU QUEUES min_gpus_per_job max_gpus_per_job min_cores_per_job max_cores_per_job max_mem_per_queue max_jobs_per_queue max_walltime
gpu_small 1 4 1 96 1440gb 5 72:00:00
gpu_medium 5 16 1 256 3072gb 2 72:00:00
gpu_large 17 256 1 2048 6144gb 1 72:00:00
VGPU QUEUES min_vgpus_per_job max_vgpus_per_job min_cores_per_job max_cores_per_job max_mem_per_queue max_jobs_per_queue max_walltime
vgpu_small 1 1 1 4 0gb 0 72:00:00
SMP QUEUE min_cores max_cores max_jobs max_walltime
bigmem 1 80 1 168:00:00
'max_mem' is the maximum amount of memory all your jobs in this queue can
consume at any one time. For example, if the max_mem for the solo queue
is 4000gb, and your solo jobs each need 10gb, then you can run a
maximum number of 4000/10 = 400 jobs in the solo queue, even though the
current max_jobs setting for the solo queue may be set higher than 400.
[lngo@login001 ~]$
The qstat command tells you which of the execution queues
your job is forwarded to. For example, here is an interactive
job requesting 8 CPU cores, a K40 GPU, and 32gb RAM:
$ qsub -I -l select=1:ncpus=8:ngpus=1:gpu_model=k40:mem=32gb,walltime=2:00:00
qsub (Warning): Interactive jobs will be treated as not rerunnable
qsub: waiting for job 9567792.pbs02 to start
We see from qstat that the job request is forward to
the c2_single queue:
[username@login001 ~]$ qstat 9567792.pbs02
Job id Name User Time Use S Queue
---------------- ---------------- ---------------- -------- - -----
9567792.pbs02 STDIN username 0 Q c2_single
From the output of checkqueuecfg above,
we see that each user can have a maximum of 5 running jobs in this queue.
Example PBS scripts
A list of example PBS scripts for submitting jobs to the Palmetto cluster can be found here.