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development/README.md deleted 100755 → 0
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---
sort: 7
---
# Code Development
{% include list.liquid all=true %}
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# Compilers
The default development environment on Star is provided by Intel
Cluster Studio XE. In general users are advised to use the Intel
compilers and MKL performance libraries, since they usually give the
best performance.
## Fortran compilers
The recommended Fortran compiler is the Intel Fortran Compiler: ifort.
The gfortran compiler is also installed, but we do not recommend it for
general usage.
## Usage of the Intel ifort compiler
For plain Fortran codes (all Fortran standards) the general form for
usage of the Intel ifort compiler is as follows:
$ ifort [options] file1 [file2 ...]
where options represents zero or more compiler options, and fileN is a
Fortran source (.f .for .ftn .f90 .fpp .F .FOR .F90 .i .i90), assembly
(.s .S), object (.o), static library (.a), or an other linkable file.
Commonly used options may be placed in the ifort.cfg file.
The form above also applies for Fortran codes parallelized with OpenMP
([www.openmp.org](http://www.openmp.org/),
[Wikipedia](https://en.wikipedia.org/wiki/OpenMP)); you only have to
select the necessary compiler options for OpenMP.
For Fortran codes parallelized with MPI the general form is quite
similar:
$ mpif90 [options] file1 [file2 ...]
The wrapper mpif90 is using the Intel ifort compiler and invokes all the
necessary MPI machinery automatically for you. Therefore, everything
else is the same for compiling MPI codes as for compiling plain Fortran
codes.
## C and C++ compilers
The recommended C and C++ compilers are the Intel Compilers; icc (C) and
icpc (C++). The gcc and g++ compilers are also installed, but we do not
recommend them for general usage due to performance issues.
## Usage of the Intel C/C++ compilers
For plain C/C++ codes the general form for usage of the Intel icc/icpc
compilers are as follows:
$ icc [options] file1 [file2 ...] # for C
$ icpc [options] file1 [file2 ...] # for C++
where options represents zero or more compiler options, fileN is a C/C++
source (.C .c .cc .cpp .cxx .c++ .i .ii), assembly (.s .S), object (.o),
static library (.a), or other linkable file. Commonly used options may
be placed in the icc.cfg file (C) or the icpc.cfg (C++).
The form above also applies for C/C++ codes parallelized with OpenMP
([www.openmp.org](http://www.openmp.org/),
[Wikipedia](https://en.wikipedia.org/wiki/OpenMP)); you only have to
select the necessary compiler options for OpenMP.
For C/C++ codes parallelized with MPI the general form is quite similar:
$ mpicc [options] file1 [file2 ...] # for C when using OpenMPI
$ mpiCC [options] file1 [file2 ...] # For C++ when using OpenMPI
Both mpicc and mpiCC are using the Intel compilers, they are just
wrappers that invoke all the necessary MPI machinery automatically for
you. Therefore, everything else is the same for compiling MPI codes as
for compiling plain C/C++ codes.
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# Debugging
## Debugging with TotalView
TotalView is a graphical, source-level, multiprocess debugger. It is the
primary debugger on Star and has excellent capabilities for debugging
parallel programs. When using this debugger you need to turn on
X-forwarding, which is done when you login via ssh. This is done by
adding the -Y on newer ssh version, and -X on older:
$ ssh -Y username@star.uit.no
The program you want to debug has to be compiled with the debug option.
This is the "-g" option, on Intel and most compilers. The executable
from this compilation will in the following examples be called
"filename".
First, load the totalview module to get the correct environment
variables set:
$ module load TotalView
To start debugging run:
$ totalview MyProg
Which will start a graphical user interface.
Once inside the debugger, if you cannot see any source code, and keep
the source files in a separate directory, add the search path to this
directory via the main menu item File-\>Search path.
Source lines where it is possible to insert a breakpoint are marked with
a box in the left column. Click on a box to toggle a breakpoint.
Double clicking a function/subroutine name in a source file should open
the source file. You can go back to the previous view by clicking on the
left arrow on the top of the window.
The button "Go" runs the program from the beginning until the first
breakpoint. "Next" and "Step" takes you one line / statement forward.
"Out" will continue until the end of the current subroutine/function.
"Run to" will continue until the next breakpoint.
The value of variables can be inspected by right clicking on the name,
then choose "add to expression list". The variable will now be shown in
a pop up window. Scalar variables will be shown with their value, arrays
with their dimensions and type. To see all values in the array, right
click on the variable in the pop up window and choose "dive". You can
now scroll through the list of values. Another useful option is to
visualize the array: after choosing "dive", open the menu item
"Tools-\>Visualize" of the pop up window. If you did this with a 2D
array, use middle button and drag mouse to rotate the surface that
popped up, shift+middle button to pan, Ctrl+middle button to zoom
in/out.
Running totalview on an interactive node:
$ mkdir -p /global/work/$USER/test_dir
$ cp $HOME/test_dir/a.out /global/work/$USER/test_dir
$ cd /global/work/$USER/test_dir
$ module load TotalView
$ totalview a.out
Replace \[#procs\] with the core-count for the job.
A window with name "New Program" should pop up. Under "Program" write
the name of the executable. Under "Parallel" choose "Open MPI" and
"Tasks" is the number of cores you are using (\[#procs\]).
You can also start Totalview with:
$ mpirun -tv a.out
The users guide and the quick start quide for Totalview can be found on
the [RogueWave documentation
page](https://support.roguewave.com/documentation/tvdocs/en/current).
## Debugging with Valgrind
- A memory error detector
- A thread error detector
- A MPI error detector
- A cache and branch-prediction profiler
- A call-graph generating cache profiler
- A heap profiler
- Very easy to use
Valgrind is the best thing for developers since the invention of
pre-sliced bread!
Valgrind works by emulating one or more CPUs. Hence it can intercept and
inspect your unmodified, running program in ways which would not be
otherwise possible. It can for example check that all variables have
actually been assigned before use, that all memory references are within
their allowed space, even for static arrays and arrays on the stack.
What makes Valgrind so extremely powerful is that it will tell exactly
where in the program a problem, error or violation occurred. It will
also give you information on how many allocates/deallocates the program
performed, and whether there is any unreleased memory or memory leaks at
program exit. In fact, it goes even further and will tell you on what
line the unreleased/leaking memory was allocated. The cache profiler
will give you information about cache misses and where they occur.
The biggest downside to Valgrind is that it will make your program run
much slower. How much slower depends on what kind of, and how much,
information you request. Typically the program will run 10-100 times
slower under Valgrind.
Simply start Valgrind with the path to the binary program to be tested:
$ module load Valgrind
$ valgrind /path/to/prog
This runs Valgrind with the default "tool" which is called memcheck,
which checks memory consistency. When run without any extra flags,
Valgrind will produce a balanced, not overly detailed and informative
output. If you need a more detailed (but slower) report, run Valgrind
with:
$ valgrind --leak-check=full --track-origins=yes --show-reachable=yes /path/to/prog
Of course, if you want to get all possible information about where in
the program something was inconsistent you must compile the program with
debugging flags switched on.
If you have a multi-threaded program (e.g. OpenMP, pthreads), and you
are unsure if there might be possible deadlocks or data races lurking in
your program, the Valgrind thread checker is your best friend. The
thread checking tool is called helgrind:
$ export OMP_NUM_THREADS=2
$ valgrind --tool=helgrind /path/to/prog
For more information on using Valgrind please refer to the man pages and
the Valgrind manual which can be found on the Valgrind website:
<http://www.valgrind.org>
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# Environment modules
The user's environment, and which programs are available for immediate
use, is controlled by the `module` command. Many development libraries
are dependant on a particular compiler versions, and at times a specific
MPI library. When loading and/or unloading a compiler, `module`
automatically unloads incompatible modules and, if possible, reloads
compatible versions.
Currently, not all libraries in all combinations of all compilers and
MPI implementations are supported. By using the default compiler and MPI
library, these problems can be avoided. In the future, we aim to
automate the process so that all possible (valid) permutations are
allowed.
Read the `module_scheme` section for an introduction on how to use
modules.
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#!/usr/bin/env bash
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=20
#SBATCH --time=0-00:10:00
module load perf-reports/5.1
# create temporary scratch area for this job on the global file system
SCRATCH_DIRECTORY=/global/work/$USER/$SLURM_JOBID
mkdir -p $SCRATCH_DIRECTORY
# run the performance report
# all you need to do is to launch your "normal" execution
# with "perf-report"
cd $SCRATCH_DIRECTORY
perf-report mpiexec -n 20 $SLURM_SUBMIT_DIR/example.x
# perf-report generates summary files in html and txt format
# we copy result files to submit dir
cp *.html *.txt $SLURM_SUBMIT_DIR
# clean up the scratch directory
cd /tmp
rm -rf $SCRATCH_DIRECTORY
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# Profiling and optimization
In general, in order to reach performances close to the theoretical
peak, it is necessary to write your algorithms in a form that allows the
use of scientific library routines, such as BLACS/LAPACK.
## Arm Performance Reports
[Arm Performance
Reports](https://www.arm.com/products/development-tools/hpc-tools/cross-platform/performance-reports)
offers a nice and convenient way to get an overview profile for your run
very quickly. It will introduce a typically negligible runtime overhead
and all you need to do is to load the `perf-reports` module and to
launch your "normal" execution using the `perf-report` launcher.
Here is an example script:
<div class="literalinclude" language="bash" linenos="">
files/perf-reports.sh
</div>
What we do there is to profile an example binary located in
`$SLURM_SUBMIT_DIR/example.x`.
The profiler generates summary files in html and txt format and this is
how an example html summary can look (open it in your browser):
![image](files/perf-reports.jpg)
## Performance tuning by Compiler flags
### Quick and dirty
Use `ifort/icc -O3`. We usually recommend that you use the `ifort/icc`
compilers as they give superior performance on Star. Using `-O3` is a
quick way to get reasonable performance for most applications.
Unfortunately, sometimes the compiler break the code with `-O3` making
it crash or give incorrect results. Try a lower optimization, `-O2` or
`-O1`, if this doesn't help, let us know and we will try to solve this
or report a compiler bug to INTEL. If you need to use `-O2` or `-O1`
instead of `-O3` please remember to add the `-ftz` too, this will flush
small values to zero. Doing this can have a huge impact on the
performance of your application.
### Profile based optimization
The Intel compilers can do something called profile based optimization.
This uses information from the execution of the application to create
more effective code. It is important that you run the application with a
typical input set or else the compiler will tune the application for
another usage profile than you are interested in. With a typical input
set one means for instance a full spatial input set, but using just a
few iterations for the time stepping.
1. Compile with `-prof-gen`.
2. Run the app (might take a long time as optimization is turned off in
this stage).
3. Recompile with `-prof-use`. The simplest case is to
compile/run/recompile in the same catalog or else you need to use
the `-prof-dir` flag, see the manual for details.
## Vtune
Intel Vtune Amplifier is a versatile serial and parallel profiler, with
features such as stack sampling, thread profiling and hardware event
sampling.
## Totalview
[Totalview](https://support.roguewave.com/documentation/tvdocs/en/current)
is a source- and machine-level debugger for multi-process,
multi-threaded programs.
help/writing-support-requests.md
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......@@ -12,10 +12,10 @@ practices.
## Never send support requests to staff members directly
Always send them to <support@metacenter.no> and staff members will pick
them up there. On <support@metacenter.no> they get tracked and have
Always send them to <support@starhpc.hofstra.io> and staff members will pick
them up there. On <support@starhpc.hofstra.io> they get tracked and have
higher visibility. Some staff members work on the support line only part
time. Sending the request to <support@metacenter.no> makes sure that
time. Sending the request to <support@starhpc.hofstra.io> makes sure that
somebody will pick it up. Please note in the request that it is about
Star, as there are more clusters that are handled with this email
address.
......
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---
sort: 2
---
# Batch system
The Star cluster is a resource that is shared between many of users and
to ensure fair use everyone must do their computations by submitting
jobs through a batch system that will execute the applications on the
available resources.
The batch system on Star is [SLURM](https://slurm.schedmd.com/)
(Simple Linux Utility for Resource Management). Read more about SLURM <a href="./slurm_parameter.html">here</a>.
<!-- <a href="https://docs.starhpc.hofstra.io/en/latest/jobs/slurm_parameter.html">here</a>. -->
## Creating a job script
To run a job on the system you need to create a job script. A job script
is a regular shell script (bash) with some directives specifying the
number of CPUs, memory, etc., that will be interpreted by the batch
system upon submission.
You can find job script examples in `job_script_examples`.
After you wrote your job script as shown in the examples, you can start
it with:
sbatch jobscript.sh
## How to pass command-line parameters to the job script
It is sometimes convenient if you do not have to edit the job script
every time you want to change the input file. Or perhaps you want to
submit hundreds of jobs and loop over a range of input files. For this
it is handy to pass command-line parameters to the job script. For an
overview of the different possible parameters, see `slurm_parameter`.
In SLURM you can do this:
$ sbatch myscript.sh myinput myoutput
And then you can pick the parameters up inside the job script:
#!/bin/bash
#SBATCH ...
#SBATCH ...
...
# argument 1 is myinput
# argument 2 is myoutput
mybinary.x < ${1} > ${2}
For recommended sets of parameters see also `slurm_recommendations`.
## Walltime
We recommend you to be as precise as you can when specifying the
parameters as they will inflict on how fast your jobs will start to run.
We generally have these rules for prioritizing jobs:
1. Large jobs, that is jobs with high CPUcounts, are prioritized.
2. Short jobs take precedence over long jobs.
3. Use fairshare. This means that users with many jobs running will get
a decreased priority compared to other users.
To find out whether all users within one project share the same
priority, run:
$ sshare -a -A nnNNNNk
For a given account (project) consider the column "RawShares". If the
RawShares for the users is "parent", they all share the same fairshare
priority. If it is a number, they have individual priorities.
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---
sort: 1
---
# Dos and don'ts
- Never run calculations on the home disk.
- Always use the SLURM queueing system.
- The login node is only for editing files and submitting jobs.
- Do not run calculations interactively on the login nodes.
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# Job script examples
## Basic examples
### General blueprint for a jobscript
You can save the following example to a file (e.g. run.sh) on Star.
Comment the two `cp` commands that are just for illustratory purpose
(lines 46 and 55) and change the SBATCH directives where applicable. You
can then run the script by typing:
$ sbatch run.sh
Please note that all values that you define with SBATCH directives are
hard values. When you, for example, ask for 6000 MB of memory
(`--mem=6000MB`) and your job uses more than that, the job will be
automatically killed by the manager.
<div class="literalinclude" language="bash">
files/slurm-blueprint.sh
</div>
### Running many sequential jobs in parallel using job arrays
In this example we wish to run many similar sequential jobs in parallel
using job arrays. We take Python as an example but this does not matter
for the job arrays:
<div class="literalinclude" language="python">
files/test.py
</div>
Save this to a file called "test.py" and try it out:
$ python test.py
start at 15:23:48
sleep for 10 seconds ...
stop at 15:23:58
Good. Now we would like to run this script 16 times at the same time.
For this we use the following script:
<div class="literalinclude" language="bash">
files/slurm-job-array.sh
</div>
Submit the script and after a short while you should see 16 output files
in your submit directory:
$ ls -l output*.txt
-rw------- 1 user user 60 Oct 14 14:44 output_1.txt
-rw------- 1 user user 60 Oct 14 14:44 output_10.txt
-rw------- 1 user user 60 Oct 14 14:44 output_11.txt
-rw------- 1 user user 60 Oct 14 14:44 output_12.txt
-rw------- 1 user user 60 Oct 14 14:44 output_13.txt
-rw------- 1 user user 60 Oct 14 14:44 output_14.txt
-rw------- 1 user user 60 Oct 14 14:44 output_15.txt
-rw------- 1 user user 60 Oct 14 14:44 output_16.txt
-rw------- 1 user user 60 Oct 14 14:44 output_2.txt
-rw------- 1 user user 60 Oct 14 14:44 output_3.txt
-rw------- 1 user user 60 Oct 14 14:44 output_4.txt
-rw------- 1 user user 60 Oct 14 14:44 output_5.txt
-rw------- 1 user user 60 Oct 14 14:44 output_6.txt
-rw------- 1 user user 60 Oct 14 14:44 output_7.txt
-rw------- 1 user user 60 Oct 14 14:44 output_8.txt
-rw------- 1 user user 60 Oct 14 14:44 output_9.txt
### Packaging smaller parallel jobs into one large parallel job
There are several ways to package smaller parallel jobs into one large
parallel job. The preferred way is to use Job Arrays. Browse the web for
many examples on how to do it. Here we want to present a more pedestrian
alternative which can give a lot of flexibility.
In this example we imagine that we wish to run 5 MPI jobs at the same
time, each using 4 tasks, thus totalling to 20 tasks. Once they finish,
we wish to do a post-processing step and then resubmit another set of 5
jobs with 4 tasks each:
<div class="literalinclude" language="bash">
files/slurm-smaller-jobs.sh
</div>
The `wait` commands are important here - the run script will only
continue once all commands started with `&` have completed.
### Example on how to allocate entire memory on one node
<div class="literalinclude" language="bash">
files/slurm-big-memory.sh
</div>
### How to recover files before a job times out
Possibly you would like to clean up the work directory or recover files
for restart in case a job times out. In this example we ask Slurm to
send a signal to our script 120 seconds before it times out to give us a
chance to perform clean-up actions.
<div class="literalinclude" language="bash">
files/slurm-timeout-cleanup.sh
</div>
## OpenMP and MPI
You can download the examples given here to a file (e.g. run.sh) and
start it with:
``` bash
$ sbatch run.sh
```
### Example for an OpenMP job
<div class="literalinclude" language="bash">
files/slurm-OMP.sh
</div>
### Example for a MPI job
<div class="literalinclude" language="bash">
files/slurm-MPI.sh
</div>
### Example for a hybrid MPI/OpenMP job
<div class="literalinclude" language="bash">
files/slurm-MPI-OMP.sh
</div>
If you want to start more than one MPI rank per node you can use
`--ntasks-per-node` in combination with `--nodes`:
``` bash
#SBATCH --nodes=4 --ntasks-per-node=2 --cpus-per-task=8
```
This will start 2 MPI tasks each on 4 nodes, where each task can use up
to 8 threads.
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# Interactive jobs
## Starting an interactive job
You can run an interactive job like this:
$ srun --nodes=1 --ntasks-per-node=1 --time=01:00:00 --pty bash -i
Here we ask for a single core on one interactive node for one hour with
the default amount of memory. The command prompt will appear as soon as
the job starts.
This is how it looks once the interactive job starts:
srun: job 12345 queued and waiting for resources
srun: job 12345 has been allocated resources
Exit the bash shell to end the job. If you exceed the time or memory
limits the job will also abort.
Interactive jobs have the same policies as normal batch jobs, there are
no extra restrictions. You should be aware that you might be sharing the
node with other users, so play nice.
Some users have experienced problems with the command, then it has
helped to specify the cpu account:
$ srun --account=<NAME_OF_MY_ACCOUNT> --nodes=1 --ntasks-per-node=1 --time=01:00:00 --pty bash -i
## Keeping interactive jobs alive
Interactive jobs die when you disconnect from the login node either by
choice or by internet connection problems. To keep a job alive you can
use a terminal multiplexer like `tmux`.
tmux allows you to run processes as usual in your standard bash shell
You start tmux on the login node before you get a interactive slurm
session with `srun` and then do all the work in it. In case of a
disconnect you simply reconnect to the login node and attach to the tmux
session again by typing:
tmux attach
or in case you have multiple sessions running:
tmux list-session
tmux attach -t SESSION_NUMBER
As long as the tmux session is not closed or terminated (e.g. by a
server restart) your session should continue. One problem with our
systems is that the tmux session is bound to the particular login server
you get connected to. So if you start a tmux session on star-1 and
next time you get randomly connected to star-2 you first have to
connect to star-1 again by:
ssh login-1
To log out a tmux session without closing it you have to press CTRL-B
(that the Ctrl key and simultaneously "b", which is the standard tmux
prefix) and then "d" (without the quotation marks). To close a session
just close the bash session with either CTRL-D or type exit. You can get
a list of all tmux commands by CTRL-B and the ? (question mark). See
also [this
page](https://www.hamvocke.com/blog/a-quick-and-easy-guide-to-tmux/) for
a short tutorial of tmux. Otherwise working inside of a tmux session is
almost the same as a normal bash session.
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# Managing jobs
The lifecycle of a job can be managed with as little as three different
commands:
1. Submit the job with `sbatch <script_name>`.
2. Check the job status with `squeue`. (to limit the display to only
your jobs use `squeue -u <user_name>`.)
3. (optional) Delete/kill the job with `scancel <job_id>`.
You can also hold the start of a job:
scontrol hold \<job_id\>
Put a hold on the job. A job on hold will not start or block other jobs
from starting until you release the hold.
scontrol release \<job_id\>
Release the hold on a job.
## Job status descriptions in squeue
When you run `squeue` (probably limiting the output with
`squeue -u <user_name>`), you will get a list of all jobs currently
running or waiting to start. Most of the columns should be
self-explaining, but the *ST* and *NODELIST (REASON)* columns can be
confusing.
*ST* stands for *state*. The most important states are listed below. For
a more comprehensive list, check the [squeue help page section Job State
Codes](https://slurm.schedmd.com/squeue.html#lbAG).
R
The job is running
PD
The job is pending (i.e. waiting to run)
CG
The job is completing, meaning that it will be finished soon
The column *NODELIST (REASON)* will show you a list of computing nodes
the job is running on if the job is actually running. If the job is
pending, the column will give you a reason why it still pending. The
most important reasons are listed below. For a more comprehensive list,
check the [squeue help page section Job Reason
Codes](https://slurm.schedmd.com/squeue.html#lbAF).
Priority
There is another pending job with higher priority
Resources
The job has the highest priority, but is waiting for some running job to
finish.
QOS\*Limit
This should only happen if you run your job with `--qos=devel`. In
developer mode you may only have one single job in the queue.
launch failed requeued held
Job launch failed for some reason. This is normally due to a faulty
node. Please contact us via <support@metacenter.no> stating the problem,
your user name, and the jobid(s).
Dependency
Job cannot start before some other job is finished. This should only
happen if you started the job with `--dependency=...`
DependencyNeverSatisfied
Same as *Dependency*, but that other job failed. You must cancel the job
with `scancel JOBID`.
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# Monitoring your jobs
## SLURM commands
To monitor your jobs, you can use of of those commands. For details run
them with the <span class="title-ref">-</span>-help option:
`scontrol show jobid -dd <jobid>` lists detailed information for a job
(useful for troubleshooting).
`sacct -j <jobid> --format=JobID,JobName,MaxRSS,Elapsed` will give you
statistics on completed jobs by jobID. Once your job has completed, you
can get additional information that was not available during the run.
This includes run time, memory used, etc.
From our monitoring tool Ganglia, you can watch live status information
on Star:
- [Load situation](http://star-adm.uit.no/ganglia/)
- [Job
queue](http://star-login2.uit.no/slurmbrowser/html/squeue.html)
## CPU load and memory consumption of your job
Star has only limited resources and usually a high demand. Therefore
using the available resources as efficient as possible is paramount to
have short queueing times and getting most out of your quota.
### Accessing slurmbrowser
In order to find out the CPU load and memory consumption of your running
jobs or jobs which have finished less than 48 hours ago, please use the
[job browser](http://star-login2.uit.no/slurmbrowser/html/squeue.html)
(accessible from within the UNINETT network).
For users from outside of UNINETT, you have to setup a ssh tunnel to
star with:
ssh -L8080:localhost:80 star-login2.uit.no
After that you can access slurmbrowser at
<http://localhost:8080/slurmbrowser/html/squeue.html>
### Detecting inefficient jobs
You can filter for a slurm job ID, account name or user name with the
search bar in the upper left corner.
For single- or multinode jobs the `AvgNodeLoad` is an important
indicator if your jobs runs efficiently, at least with respect to CPU
usage. If you use the whole node, the average node load should be close
to number of CPU cores of that node (so 16 or 20 on Star). In some
cases it is totally acceptable to have a low load if you for instance
need a lot of memory but in general either CPU or memory load should be
high. Otherwise you are wasting your quota and experience probably
longer than necessary queuing times.
If you detect inefficient jobs you either look for ways to improve the
resource usage of your job or ask for less resources in your SLURM
script.
## Understanding your job status
When you look at the job queue through the [job
browser](http://star-login2.uit.no/slurmbrowser/html/squeue.html), or
you use the `squeue` command, you will see that the queue is divided in
3 parts: Active jobs, Idle jobs, and Blocked jobs.
Active jobs are the jobs that are running at the moment. Idle jobs are
next in line to start running, when the needed resources become
available. Each user can by default have only one job in the Idle Jobs
queue.
Blocked jobs are all other jobs. Their state can be *Idle*, *Hold*, or
*Deferred*. *Idle* means that they are waiting to get to the Idle queue.
They will eventually start when the resources become available. The jobs
with the *Hold* state have been put on hold either by the system, or by
the user. F.e. if you have one job in the Idle queue, that is not very
important to you, and it is blocking other, more urgent, jobs from
starting, you might want to put that one job on hold. Jobs on hold will
not start until the hold is released. *Deferred* jobs will not start. In
most cases, the job is deferred because it is asking for a combination
of resources that Star can not provide.
Please contact the support staff, if you don't understand why your job
has a hold or deferred state.
## Summary of used resources
Slurm will append a summary of used resources to the `slurm-xxx.out`
file. The fields are:
- Task and CPU usage stats
- `AllocCPUS`: Number of allocated CPUs
- `NTasks`: Total number of tasks in a job or step.
- `MinCPU`: Minimum CPU time of all tasks in job (system + user).
- `MinCPUTask`: The task ID where the mincpu occurred.
- `AveCPU`: Average CPU time of all tasks in job (system + user)
- `Elapsed`: The jobs elapsed time in format \[DD-\[HH:\]\]MM:SS.
- `ExitCode`: The exit code returned by the job script. Following
the colon is the signal that caused the process to terminate if
it was terminated by a signal.
- Memory usage stats
- `MaxRSS`: Maximum resident set size of all tasks in job.
- `MaxRSSTask`: The task ID where the maxrss occurred.
- `AveRSS`: Average resident set size of all tasks in job.
- `MaxPages`: Maximum number of page faults of all tasks in job.
- `MaxPagesTask`: The task ID where the maxpages occurred.
- `AvePages`: Average number of page faults of all tasks in job.
- Disk usage stats
- `MaxDiskRead`: Maximum number of bytes read by all tasks in job.
- `MaxDiskReadTask`: The task ID where the maxdiskread occurred.
- `AveDiskRead`: Average number of bytes read by all tasks in job.
- `MaxDiskWrite`: Maximum number of bytes written by all tasks in
job.
- `MaxDiskWriteTask`: The task ID where the maxdiskwrite occurred.
- `AveDiskWrite`: Average number of bytes written by all tasks in
job.
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# Running MPI jobs
There are two available MPI implementations on Star:
- OpenMPI provided by the `foss` module, e.g. `module load foss/2016b`
- Intel MPI provided by the `intel` module, e.g.
`module load intel/2016b`
There are several ways of launching an MPI application within a SLURM
allocation, e.g. `srun`, `mpirun`, `mpiexec` and `mpiexec.hydra`.
Unfortunately, the best way to launch your program depends on the MPI
implementation (and possibly your application), and choosing the wrong
command can severly affect the efficiency of your parallel run. Our
recommendation is the following:
## Intel MPI
With Intel MPI, we have found that `mpirun` can incorrectly distribute
the MPI ranks among the allocated nodes. We thus recommend using `srun`:
$ srun --mpi=pmi2 ./my_application
## OpenMPI
With OpenMPI, `mpirun` seems to be working correctly. Also, it seems
that `srun` fails to launch your application in parallel, so here we
recommend using `mpirun`:
$ mpirun ./my_application
NOTE: If you're running on the `multinode` partition you automatically
get the `--exclusive` flag, e.i. you get allocated (and charged for)
**full** nodes, even if you explicitly ask for less resources per node.
This is not the case for the `normal` partition.
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.. _new_sw:
New software and module scheme
==============================
**On the 9th-10th of May 2017, the default module setup changed. It is likely that you will have problems loading modules and jobs waiting to start might crash. Jobs already running should not be affected.**
What this means:
We have many new software packages installed, that were not visible by default,
though they were already accessible. On the afore mentioned days we made these packages visible by default. There are several big changes that you will experience, and sadly this might break your job scripts and cause you error messages when trying to load modules. Jobs that are waiting to start might crash at start because they can not load modules, but already running jobs should run without problems. We apologize for these problems and we believe that this way of switching is still a better alternative than shutting Stallo down.
If you have trouble or questions, please, check this manual and contact us.
Here are the main changes you will experience:
Change of software module names
-------------------------------
You will still be able to use the "old" software you used before, but many names of sw packages will change.
The name changes are in using mixed case instead of just small case, f.e. "python" is will become "Python", "openmpi" becomes "OpenMPI", and so on. The version of the modules has also changed. In most modules the version now contains some basic information on what it was compiled with - f.e. intel/2016a or foss/2016a (Free Open Source Software, commonly referred to as GNU) - and in some cases also information on some dependency - f.e. Python/2.7.12. In the new installation, the name "intel" has a somewhat different meaning from the old installations: loading intel/13.0 will give you the intel module, and you will have icc and ifort available to you; loading intel/2016a will load several packages including icc, ifort, imkl and impi. More information on this coming soon.
If you are looking for a package, try::
$ module avail <package_name>
This command is case insensitive, and lists you all modules, which you can load for this package
(both from the new and the old installations). If you get too many results because the name of your package is part of other names (f.e. "R", "intel", or "Python") add a "/" at the end of the name::
$ module avail <package_name>/
Behavior of potentially conflicting modules
-------------------------------------------
Another important change is behavior of potentially conflicting modules. In the "old setup" you are
not able to have two conflicting modules - f.e. openmpi and impi - loaded at the same time.
This is only partially true for the "new scheme" and you will need to pay more attention to this.
With the new packages, it is also more common that when you load one package it will automatically
load several other packages that it needs to function.
Check what you have currently loaded with::
$ module load
or ``ml`` for short, and unload conflicting modules, or do::
$ module purge
to remove all loaded packages and start over.
StdEnv and StdMod
-----------------
By default, you will have StdEnv and StdMod loaded. StdEnv sets up the paths and other variables for you to have a working module environment. It has a "sticky" tag and will not be unloaded with a ``module purge`` or a ``module unload StdEnv``. We strongly recommend keeping it loaded.
StdMod loads the default system modules. You are welcome to unload it, if you wish, and both ``module unload StdMod`` or ``module purge`` will unload it.
Lmod warning "rebuild your saved collection"
--------------------------------------------
Lmod allows a user to save a bundle of modules as a collection using ``module save <collection_name>`` and ``module restore <collection_name>``. This enables you to quickly get the same list of modules loaded if you tend to use the same modules over and over.
With a new module scheme came a different system MODULEPATH. For this reason, if you have some module collections saved, you will experience the following warning: "Lmod Warning: The system MODULEPATH has changed: please rebuild your saved collection."
To solve this you need to remove your old collections and create them again. We apologize for the inconvenience.
If you are already using "mod_setup.sh" or "/home/easybuild/"
-------------------------------------------------------------
If you have already started using the new modules by loading the "notur" module and sourcing "mod_setup.sh", simply remove these two steps and continue working as normal. The same goes for any references to /home/easybuild/. We kept these options working for now to reduce the number of potentially crashing jobs, but we plan to remove them in the near future, so we recommend removing those lines from any new jobs you launch. You do not need the module "varset" any more, StdEnv has taken over its functionality. If the only reason you were using the "notur" module was to access the newer installations, you do not need it either. If you were using the "notur" module to run ADF or other software, then you still need to continue using it.
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.. figure:: tag.jpg
:scale: 57 %
Found in Via Notari, Pisa; (c) Roberto Di Remigio.
.. _news:
News and notifications
======================
News about planned/unplanned downtime, changes in hardware, and important
changes in software will be published on the HPC UiT twitter account
`<https://twitter.com/hpc_uit>`_ and on the login screen of stallo.
For more information on the different situations see below.
System status and activity
--------------------------
You can get a quick overview of the system load on Stallo on the
`Sigma2 hardware page <https://www.sigma2.no/hardware/status>`_.
More information on the system load, queued jobs, and node states can
be found on the `jobbrowser page <http://stallo-login2.uit.no/slurmbrowser/html/squeue.html>`_
(only visible from within the UiT network).
Planned/unplanned downtime
--------------------------
In the case of a planned downtime, a reservation will be made in the
queuing system, so new jobs, that would not finish until the downtime,
won't start. If the system goes down, running jobs will be restarted,
if possible. We apologize for any inconveniences this may cause.
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# General comments about licenses
This is an attempt to describe the different licenses we have on Star
and for the entire NOTUR system when it comes to chemistry and material
science codes.
Here, I will only address the codes with a certain access limitation.
For free, open source codes without any limitations, I do not really see
the point in commenting. But, the reader should be aware that "free" not
allways means "unrestricted access".
Also, due to a choice of policy, I am only adressing the issues of
academic access. For commercial licenses, the user or user groups needs
to investigate consequences and prices themselves. For any given code,
the license model and related issues is more in depth described as a
part of the standard documentation.
There is in general 6 different categories of access limitations for
software on Star:
1. Licenses paid entirely by the users.
2. Licenses paid partially by the users and partly by national bodies.
3. Licenses paid partly or entirely by institutions.
4. Licenses paid entirely by national bodies.
5. Licenses available only on a machine or a service (machine-license).
6. Free licenses that requires an agreement with the vendor before
using the code.
Category 1 is what is the "bring your own license" group. Either the
vendor does not provide computer centre license, or pricing is somewhat
outside the boundaries of acceptance for NOTUR. VASP is in this
category, where NOTUR does administer a certain level of access based on
feedback from the VASP vendor. Other examples is Petrel, Molpro
(previously).
Category 2 is a joint funding model, where users have to add to the
total pool of finance in order to provide a certain level of license
tokens available. For instance, the Schrodinger small molecule drug
discovery suite license is split into a "national" and a "local" part,
with one common license server and unlimited tokens available for
everyone. NOTUR provides support and manages the license tokens on
behalf of the community, up until now as a part of DS Chem (predecessor
to the Application Managment project). The license is monitored, in
order to ensure a minimum level of fairness. If a user crosses a certain
level of usage without representing one of the groups contributing to
funding - access will be denied until finance formalities has been
settled.
Category 3 is the group of software where your respective host
institution has implication for access or not. Codes like COMSOL,
MATLAB, STATA, ANSYS, STAR-CCM+, IDL, Mathematica are all in this
category.
Category 4 is a lot more complex than you might anticipate. Codes like
ADF and Turbomole are in this category. ADF is currently entirely funded
by Sigma2, but how long this will continue is unknown. For ADF, we have
a national license - meaning that anyone can request a download and
install the code suite as long as they are members on a Norwegian Grant
degreeing institution. Turbomole has currently the form of a national
machine type license, as a part of prevoiously mentioned DS Chem. As
long as on person has the responsibility of installing Turbomole on all
the national academic high performance compute clusters, we only have to
by one common license for all these machines. Currently, the national
body has paid for a two group Crystal license, in order to map interest
and need. We have (or at least had when the license was bought) an
agreement that if the interest came to the level where we needed to
grant access to more people, we could transform the license into a
machine type license by only paying the difference in license fee.
Gaussian has the appearance of a national license, and is paid by
NOTUR - but it really is 4 site licenses making a total of "access
license for those who comes from one of the four original norwegian
academic hpc host sites - NTNU, UiB, UiO, Hofstra". It also means that
others that do hold a valid Gaussian license will be allowed access.
This licensing is limited to main version (G09, G16 etc) and may explain
limitations on the more recent versions. The intel tools and compiler
suites for use on the national academic hpc cluster is also in this
category.
Category 5 is the group of software that is only available on a single
installation/machine. On Star we have NBO6 and the nbo6 plug in for
ADF. On Abel they have an installation of Molpro which is both machine
type and site type licensed.
Category 6 In this group, we have Molcas, ORCA. (and maybe Dalton and
Dirac). Some vendors, even if they offer their software free for
academic usage in general, or for limited areas (Molcas is free for
academics from Nordic institutions), they still want to have a certain
control over the software distribution.
and, of course, category 7 is the entire group of software where this is
not really a problem;-).
You will find more info in the application guides or in the respective
module files. The latter info you get access to by typing:
``` bash
module help <application> # Like for instance ADF
```
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# Which software is installed on Star
To find out what software packages are available, type:
module avail
## Changes in application software
News about planned/unplanned downtime, changes in hardware, and
important changes in software will be published on the HPC Hofstra twitter
account <https://twitter.com/hpc_uit> and on the login screen of star.
For more information on the different situations see <span
class="title-ref">news</span>.
The easiest way to check which software and versions available is to use
the `module` command. List all software available:
module avail
List all version of a specific software:
module avail software-name
# Missing or new software
If there is any software missing on this list that you would like to
have installed on Star, or you need help to install your own software,
please feel free to contact the support personal about this:
<support@metacenter.no>.
If there is software missing on Star that you would like to have,
there are different options to install it: \* Use our installation
framework EasyBuild \* Use a package manager like Conda or especially
for bioinformatic software Bioconda \* Compile it yourself and install
into your home directory \* Ask us to install for you
In this document we will show some ways of installing a new software. If
you run into problems, need help or have question, please feel free to
contact the support personal about this: <support@metacenter.no>.
## Installing new software using EasyBuild
Our building and installation framework,
[EasyBuild](https://easybuild.readthedocs.io/en/latest/Using_the_EasyBuild_command_line.html)
can be used by you to install software into your own home directory.
This is especially interesting for you if you need a software but it is
probably not used by other users of Star, so a systemwide installation
doesn't make much sense.
To see if your software is available via the EasyBuild system have a
look into the [easyconfig github
repository](https://github.com/easybuilders/easybuild-easyconfigs). if
you can find a version of your software there. Alternatively use the
search function of EasyBuild:
eb -S REGEX
Let's assume you want to install the genome aligner Kraken2. We find a
easyconfig file with the name
[Kraken2-2.0.7-beta-foss-2018b-Perl-5.28.0.eb](https://github.com/easybuilders/easybuild-easyconfigs/blob/master/easybuild/easyconfigs/k/Kraken2/Kraken2-2.0.7-beta-foss-2018b-Perl-5.28.0.eb)
in the easyconfig repository.
Before we start the installation process we first have to load the
necessary modules:
ml purge
ml EasyBuild
Before we actually build we can check the building process by adding
<span class="title-ref">--dry-run-short</span> or <span
class="title-ref">-D</span> to the build command:
eb Kraken2-2.0.7-beta-foss-2018b-Perl-5.28.0.eb --dry-run-short
As you probably see, a long list of packages is printed with some
packages being marked as missing. To automatically install all necessary
dependencies we can add <span class="title-ref">--robot</span> and then
run the installation:
eb Kraken2-2.0.7-beta-foss-2018b-Perl-5.28.0.eb --robot
Before we can load your newly installed module, we have to add the path
to the Lmod search path. You might have to do this from time to time, or
even every time you use your software, as your local cache is regularly
updated. :
ml use ~/.local/easybuild/modules/all
ml Kraken2/2.0.7
## Conda/ Bioconda
Many software packages, especially if they are python based, can be
easily installed using the Conda package manager. For many
bioinformatics software, Bioconda has become a nice solution.
A small tutorial can be found in the [Python]({% link software/python_r_perl.md %}#python) section of this
documentation.
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# Application guides
For a general explanation on how to make an application available for
use, the module system, and information about changes in application
software see module\_scheme.
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.. _ADF:
===============================================
The Amsterdam Density Functional modeling suite
===============================================
This page contains general information about the ADF modeling suite installed on Stallo:
Related information
-------------------
.. toctree:: :maxdepth: 1
:titlesonly:
firsttime_adf
ADFprog
Band
firsttime_band
advanced
General Information
===================
Description
-----------
The DFT code ADF is used in many areas of chemistry and materials science, and consists of the following parts:
* The pure DFT code based on Slater type orbitals; ADF.
* The periodic DFT code BAND shares a lot of functionality with ADF.d for treating large periodic molecular systems.
* DFTB and MOPAC are fast approximate methods to study large molecules and big periodic systems, employing DFT-based and semi-empirical data, respectively.
* Reaction dynamics in large complex systems can be studied with bond order based ReaxFF.
* COSMO-RS uses quantum mechanical data from ADF to predict thermodynamic properties of solutions and mixtures (LogP, VLE, pKa, …)
Online info from vendor
-----------------------
* Homepage: https://www.scm.com
* Documentation: https://www.scm.com/doc
The support people in NOTUR, do not provide trouble shooting guides anymore, due to a national agreement that it is better for the community as a whole to add to the community info/knowledge pool where such is made available. For ADF/BAND we advise to search in general documentation, sending emails to support (either metacenter or scm) or trying the ADF mailing list (see https://www.scm.com/Support for more info).
License and access policy
-------------------------
The license of ADF/Band is commercial.
NOTUR holds a national license of the ADF program system, making usage of ADF/BAND available for all academic scient\
ists in Norway.
We have a national license for the following packages:
- ADF & ADFGUI
- BAND &BANDGUI
- CRS
- DFTB & DFTBGUI
- GUI
- REAXFF & REAXFFGUI
- NBO6 (on Stallo only, but machine license available for all users of Stallo).
`Please note that this is an academic type license; meaning that research institutes not being part of Norwegian Universities must provide their own l\
icense to be able to use and publish results obtained with this code on NOTUR installlations.`
Citation
--------
When publishing results obtained with the referred software referred, please do check the developers web page in order to find the correct citat\
ion(s).
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.. _ADFprog:
====================================
Amsterdam Density Functional program
====================================
Information regarding the quantum chemistry code ADF on Stallo
General Information
===================
Description
-----------
According to the vendor, ADF (Amsterdam Density Functional) is a DFT program particularly strong in understanding and predicting structure, reactivity, and spectra of molecules. It is a Fortran program for calculations on atoms and molecules (in gas phase or solution). It can be used for the study of such diverse fields as molecular spectroscopy, organic and inorganic chemistry, crystallography and pharmacochemistry.
The underlying theory is the Kohn-Sham approach to Density-Functional Theory (DFT). The software is a DFT-only first-principles electronic structure calculations program system, and consists of a rich variety of packages.
Online documentation from vendor
--------------------------------
* Documentation: https://www.scm.com/doc/ADF
Usage
=====
The ADF/BAND suite of software is currently installed as precompiled binaries on Stallo. We install the intel-mpi version, since it has proven to collaborate the better with our mpi setup. We generally advise to run ADF on more than one node, unless you do know that your particular problem does not make the code scale well.
Use
.. code-block:: bash
$ module avail ADF
to see which versions of ADF which are available. Use
.. code-block:: bash
$ module load ADF/<version> # i.e adf2017.108
to get access to any given version of ADF.
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.. _Band:
==========================
The periodic DFT code Band
==========================
Information regarding the periodic DFT code Band on Stallo. You may also be interested in this: :doc:`firsttime_band`.
General Information
===================
Description
-----------
BAND is an atomic-orbital based DFT program for periodic systems (crystals, slabs, chains and molecules).
The Amsterdam Density Functional Band-structure program - BAND - can be used for calculations on periodic systems, i.e. polymers, slabs and crystals, and is supplemental to the molecular ADF program for non-periodic systems. It employs density functional theory in the Kohn-Sham approach. BAND is very similar to ADF in the chosen algorithms, although important differences remain.
BAND makes use of atomic orbitals, it can handle elements throughout the periodic table, and has several analysis options available. BAND can use numerical atomic orbitals, so that the core is described very accurately. Because of the numerical orbitals BAND can calculate accurate total energies. Furthermore it can handle basis functions for arbitrary l-values.
Online info from vendor
-----------------------
* Documentation: https://www.scm.com/doc/BAND
Usage
=====
The ADF/BAND suite of software is currently installed as precompiled binaries on Stallo. We install the intel-mpi version, since it has proven to collaborate the better with our mpi setup. We generally advise to run Band on more than one node, unless you do know that your particular problem does not make the code scale well.
Use
.. code-block:: bash
$ module avail ADF
to see which versions of Band which are available. Use
.. code-block:: bash
$ module load ADF/<version> # i.e adf2017.108
to get access to any given version of Band.
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.. _adf_advanced:
==============================
Information for advanced users
==============================
Scaling behaviour
-----------------
Since ADF is a very complex code, able to solve a vast range of chemistry problems - giving a unified advice regarding scaling is difficult. We will try to inspect scaling behaviour related to most used areas of application. For a standard geometry optimization, it seems to scale well in the region of 4-6 full nodes (60-100 cores) at least. For linear transit we would currently stay at no more than 4 full nodes or less currently.Unless having tests indicating otherwise, users who want to run large jobs should allocate no more than the prescribed numbers of processors. More information will come.
Memory allocation
-----------------
On Stallo there are 32 GB and 128GB nodes. Pay close attention to memory usage of job on the nodes where you run, and if necessary redistribute the job so that it uses less than all cores on the node until the limit of 32 GB/core. More than that, you will need to ask for access to the highmem-queue. As long as you do not ask for more than 2 GB/core, using the pmem-flag for torque does in principle give no meaning.
How to restart an ADF job
-------------------------
#. In the directory where you started your job, rename or copy the job-output t21 file into $SCM_TMPDIR/TAPE21.
#. In job.inp file, put RESTART TAPE21 just under the comment line.
#. Submit job.inp file as usual.
This might also be automized, we are working on a solution for restart after unexpected downtime.
How to run ADF using fragments
------------------------------
This is a brief introduction to how to create fragments necessary for among other things, BSSE calculations and proper broken symmetry calculations.
**Running with fragments:**
* Download and modify script for fragment create run, e.g. this template: Create.TZP.sh (modify ACCOUNT, add desired atoms and change to desired basis and desired functional)
* Run the create job in the same folder as the one where you want to run your main job(s) (sbatch Create.TZP.sh).
* Put the line cp $init/t21.* . in your ADF run script (in your $HOME/bin directory)
* In job.inp, specify correct file name in FRAGMENT section, e.g. “H t21.H_tzp”. * Submit job.inp as usual.
Running with fragments is only necessary when you want to run a BSSE calculations or manipulate charge and/or atomic mass for a given atom (for instance modeling heavy isotope labelled samples for frequency calculations).
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.. _first_time_adf:
==============================
First time you run an ADF job?
==============================
This page contains info aimed at first time
users of ADF on Stallo, but may also be usefull to
more experienced users. Please look carefully through the
provided examples. Also note that the job-script example is rather richly
commented to provide additional and relevant info.
If you want to run this testjob, download the copies of the scripts and put them
into your test job folder (which I assume you have created in advance).
ADF input example
-----------------
.. include:: ../files/caffeine.adf
:literal:
This file can also be downloaded here: :download:`Caffeine input for ADF <../files/caffeine.adf>`.
Place this file in a job folder of choice, say ADFFIRSTJOB in your home directory on Stallo.
then, copy the job-script as seen here:
.. include:: ../files/job_adf.sh
:literal:
(Can also be downloaded from here: :download:`job_adf.sh <../files/job_adf.sh>`)
Place this script in the same folder and type:
.. code-block:: bash
sbatch job_adf.sh
You may now have submitted your first adf job.
These files are also available on Stallo
----------------------------------------
.. code-block:: bash
module load ADF/adf2017.108
cd <to whatever you call your test folder> # for instance ADFFIRSTJOB
cp -R /global/hds/software/notur/apprunex/ADF/* .
To verify that the jobs has worked fine, check the outputfile. If it says EXIT and print Bond Energies at the end of the file, you are likely on the safe side.
Check the Bond Energy in adf_caffeine.out. The value should ideally be close to -156.75317227 eV. When this is the case, you may alter variables in the shell script as much as you like to adapt to your own jobs.
**NB: ADF is installed as precompiled binaries, they come with their own mpi (intel MPI). So if you are not using the provided runscript example and/or loading the newer modules - please make sure that you load an intelmpi module and also preferably a rather recent intel compiler.**
Also note that, on Stallo, we hold the nbo6 plug in license that allows users of adf to produce files that can be read with the nb06 software.
Good luck with your chemistry!
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.. _first_time_band:
==============================
First time you run a Band job?
==============================
This page contains info aimed at first time
users of Band on Stallo, but may also be usefull to
more experienced users. Please look carefully through the
provided examples. Also note that the job-script example is rather richly
commented to provide additional and relevant info.
If you want to run this testjob, download the copies of the scripts and put them
into your test job folder (which I assume you have created in advance).
Band input example
------------------
.. include:: ../files/silicone.adf
:literal:
This file can also be downloaded here: :download:`Silicone input for Band <../files/silicone.adf>`.
Place this file in a job folder of choice, say BANDFIRSTJOB in your home directory on Stallo.
then, copy the job-script as seen here:
.. include:: ../files/job_band.sh
:literal:
(Can also be downloaded from here: :download:`job_band.sh <../files/job_band.sh>`)
Place this script in the same folder and type:
.. code-block:: bash
sbatch job_band.sh
You may now have submitted your first adf job.
These files are also available on Stallo
----------------------------------------
.. code-block:: bash
module load ADF/adf2017.108
cd <to whatever you call your test folder> # for instance BANDFIRSTJOB
cp -R /global/hds/software/notur/apprunex/ADF/* .
To verify that the jobs has worked fine, check the outputfile. If it says EXIT and print Energy of Formation at the end of the file, you are likely on the safe side.
#Check the ENERGY OF FORMATION in band_silicone.out. The value should ideally be close to -4.2467 eV. When this is the case, you may alter variables in the shell script as much as you like to adapt to your own jobs.
**NB: The ADF modeling suite is installed as precompiled binaries, they come with their own mpi (intel MPI). So if you are not using the provided runscript example and/or loading the newer modules - please make sure that you load an intelmpi module and also preferably a rather recent intel compiler.**
Good luck with your chemistry!
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.. _Dalton:
======
Dalton
======
Related information
===================
.. toctree::
:maxdepth: 1
firsttime_dalton.rst
General information
===================
Description
-----------
Dalton is an ab initio quantum chemistry computer program. It is capable of calculating various molecular properties using the Hartree-Fock, MP2, MCSCF and coupled cluster theories. Dalton also supports density functional theory calculations.
Online information from vendor
------------------------------
* Homepage: http://daltonprogram.org/
* Documentation: http://daltonprogram.org/documentation/
* For download: http://daltonprogram.org/download/
License and access policy
-------------------------
Dalton is licensed under the GPL 2. That basically means that everyone may freely use the program.
Citation
--------
When publishing results obtained with the referred software referred, check the following page to find the correct citation(s): http://daltonprogram.org/citation/
Usage
=====
You load the application by typing:
.. code-block:: bash
module load Dalton/2016-130ffaa0-intel-2017a
For more information on available versions of Dalton, type:
.. code-block:: bash
module avail Dalton
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.. _first_time_dalton:
================================
First time you run a Dalton job?
================================
This page contains info aimed at first time
users of Dalton on Stallo. Please look carefully through the
provided examples. Also note that the job-script example is rather richly
commented to provide additional and relevant info.
If you want to run this testjob, copy the input example and the job script example shown below
into your test job folder (which I assume you have created in advance).
Dalton needs one molecule file and one input file. For simplicity, we have tarred them together and you
may download them here: :download:`Dalton files<../files/dalton_example_files.tar.gz>`
Molcas runscrip example
-----------------------
.. include:: ../files/job_dalton.sh
:literal:
You can also download the runscript file here: :download:`Dalton run script<../files/job_dalton.sh>`
The runscript example and the input are also on Stallo
------------------------------------------------------
Type:
.. code-block:: bash
cd <whatever_test_folder_you_have> # For instance testdalton
cp -R /global/hds/software/notur/apprunex/dalton/* .
When you have all the necessary files in the correct folders, submit the job by typing:
.. code-block:: bash
sbatch job_dalton.sh
Good luck.
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=========
GaussView
=========
Gaussview is a visualization program that can be used to open Gaussian output
files and checkpoint files (.chk) to display structures, molecular orbitals,
normal modes, etc. You can also set up jobs and submit them directly.
Official documentation: https://gaussian.com/gaussview6/
GaussView on Stallo
-------------------
To load and run GaussView on Stallo, load the relevant Gaussian module, and
then call GaussView::
$ module avail Gaussian
$ module load Gaussian/g16_B.01
$ gview
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# Gaussian
{% include list.liquid all=true %}
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%chk=example
%mem=500MB
#p b3lyp/cc-pVDZ opt
benzene molecule example
0 1
C 0.00000 1.40272 0.00000
C 0.00000 -1.40272 0.00000
C 1.21479 0.70136 0.00000
C 1.21479 -0.70136 0.00000
C -1.21479 0.70136 0.00000
C -1.21479 -0.70136 0.00000
H 0.00000 2.49029 0.00000
H 0.00000 -2.49029 0.00000
H 2.15666 1.24515 0.00000
H 2.15666 -1.24515 0.00000
H -2.15666 1.24515 0.00000
H -2.15666 -1.24515 0.00000
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#!/bin/bash -l
#SBATCH --account=nn....k
#SBATCH --job-name=example
#SBATCH --output=example.log
# Stallo nodes have either 16 or 20 cores: 80 is a multiple of both
#SBATCH --ntasks=80
# make sure no other job runs on the same node
#SBATCH --exclusive
# syntax is DD-HH:MM:SS
#SBATCH --time=00-00:30:00
# allocating 30 GB on a node and leaving a bit over 2 GB for the system
#SBATCH --mem-per-cpu=1500MB
################################################################################
# no bash commands above this line
# all sbatch directives need to be placed before the first bash command
# name of the input file without the .com extention
input=example
# flush the environment for unwanted settings
module --quiet purge
# load default program system settings
module load Gaussian/g16_B.01
# set the heap size for the job to 20 GB
export GAUSS_LFLAGS2="--LindaOptions -s 20000000"
# split large temporary files into smaller parts
lfs setstripe –c 8 .
# create scratch space for the job
export GAUSS_SCRDIR=/global/work/${USER}/${SLURM_JOB_ID}
tempdir=${GAUSS_SCRDIR}
mkdir -p ${tempdir}
# copy input and checkpoint file to scratch directory
cp ${SLURM_SUBMIT_DIR}/${input}.com ${tempdir}
if [ -f "${SLURM_SUBMIT_DIR}/${input}.chk" ]; then
cp ${SLURM_SUBMIT_DIR}/${input}.chk ${tempdir}
fi
# run the code
cd ${tempdir}
time g16.ib ${input}.com > ${input}.out
# copy output and checkpoint file back
cp ${input}.out ${SLURM_SUBMIT_DIR}
cp ${input}.chk ${SLURM_SUBMIT_DIR}
# remove the scratch directory after the job is done
# cd ${SLURM_SUBMIT_DIR}
# rm -rf /global/work/${USER}/${SLURM_JOB_ID}
exit 0
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.. _gaussian_on_stallo:
==========================
Memory and number of cores
==========================
This page contains info about special features related to
the Gaussian install made on Stallo, but also general issues
related to Gaussian only vaguely documented elsewhere.
Choosing the right version
--------------------------
To see which versions of Gaussien are available, use::
$ module avail Gaussian/
To load a specific version of Gaussian, use for instance::
$ module load Gaussian/g16_B.01
Gaussian over Infiniband
------------------------
First note that the Gaussian installation on Stallo is the Linda parallel
version, so it scales somewhat initially. On top of this, Gaussian is installed
with a little trick, where the loading of the executable is intercepted before
launched, and an alternative socket library is loaded. This enables the
running Gaussian natively on the Infiniband network giving us
two advantages:
* The parallel fraction of the code scales to more cores.
* The shared memory performance is significantly enhanced (small scale performance).
But since we do this trick, we are greatly depending on altering the specific
node address into the input file: To run gaussian in parallel requires the
additional keywords ``%LindaWorkers`` and ``%NProcshared`` in the ``Link 0`` part of the
input file. This is taken care of by a wrapper script around the
original binary in each individual version folder. This
is also commented in the job script example. Please use
our example when when submitting jobs.
We have also taken care of the rsh/ssh setup in our installation procedure, to
avoid .tsnet.config dependency for users.
Parallel scaling
----------------
Gaussian is a rather large program system with a range of different binaries,
and users need to verify whether the functionality they use is parallelized and
how it scales.
Due to the preload Infiniband trick, we have a somewhat more generous policy
when it comes to allocating cores/nodes to Gaussian jobs but before computing a
table of different functionals and molecules, we strongly advice users to first
study the scaling of the code for a representative system.
Please do not reuse scripts inherited from others without studying the
performance and scaling of your job. If you need assistance with this, please
contact the user support.
We do advice people to use up to 256 cores (``--tasks``). We have observed
acceptable scaling of the current Gaussian install beyond 16 nodes for the jobs
that do scale outside of one node (i.e. the binaries in the $gXXroot/linda-exe
folder). Linda networking overhead seems to hit hard around this amount of
cores, causing us to be somewhat reluctant to advice going beyond 256 cores.
Since we have two different architectures with two different core counts on
Stallo, the ``--exclusive`` flag is important to ensure that the distribution
of jobs across the whole system are done in a rather flexible and painless way.
Memory allocation
-----------------
Gaussian takes care of memory allocation internally.
That means that if the submitted job needs more memory per core than what is in
average available on the node, it will automatically scale down the number of
cores to mirror the need. This also means that you always should ask for full
nodes when submitting Gaussian jobs on Stallo! This is taken care of by the
``--exclusive`` flag and commented in the job script example.
The ``%mem`` allocation of memory in the Gaussian input file means two things:
* In general it means memory/node – for share between ``nprocshared``, and
additional to the memory allocated per process. This is also documented by
Gaussian.
* For the main process/node it also represents the network
buffer allocated by Linda since the main Gaussian process takes a part
and Linda communication process takes a part equally sized – thus you should
never ask for more than half of the physical memory on the nodes, unless they
have swap space available - which you never should assume.
The general ``%mem`` limit will always be half of the physical memory
pool given in MB instead of GB - 16000MB instead of 16GB since this leaves a
small part for the system. That is why we would actually advise to use 15GB as
maximum ``%mem`` size.
Large temporary outputs on Stallo
---------------------------------
As commented here :doc:`/storage/storage` there is an issue related to very
large temporary files on Stallo. Please read up on it at act accordingly. This
issue is also commented in the job script example.
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====================
Gaussian job example
====================
To run this example create a directory, step into it, create the input file and submit the script with::
$ sbatch gaussian-runscript.sh
Input example
-------------
Direct download link: :download:`input example <example.com>`
.. literalinclude:: example.com
:language: none
Runscript example
-----------------
Direct download link: :download:`runscript example <gaussian-runscript.sh>`
.. literalinclude:: gaussian-runscript.sh
:language: bash
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======================
Gaussian and GaussView
======================
.. toctree::
:maxdepth: 1
job-example.rst
gaussian_on_stallo.rst
GaussView.rst
`Gaussian <http://gaussian.com>`_ is a popular computational chemistry program.
Official documentation: http://gaussian.com/man
License and access
------------------
The license is commercial/proprietary and constitutes of 4 site licenses for the 4 current host
institutions of NOTUR installations; NTNU, UiB, UiO, UiT. Only
persons from one of these institutions have access to the Gaussian Software
system installed on Stallo. Note that users that do not come from one of the
above mentioned institutions still may be granted access, but they need to
document access to a valid license for the version in question first.
* To get access to the code, you need to be in the ``gaussian`` group of users.
* To be in the ``gaussian`` group of users, you need be qualified for it - see above.
Citation
--------
For the recommended citation, please consult http://gaussian.com/citation/.
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.. _Molcas:
======
Molcas
======
Related information
===================
.. toctree::
:maxdepth: 1
firsttime_molcas.rst
General information
===================
Description
-----------
Molcas is an ab initio quantum chemistry software package developed by scientists to be used by scientists. The basic philosophy is is to be able to treat general electronic structures for molecules consisting of atoms from most of the periodic table. As such, the primary focus of the package is on multiconfigurational methods with applications typically connected to the treatment of highly degenerate states.
Key aspects of Molcas:
* SCF/DFT, CASSCF/RASSCF, CASPT2/RASPT2
* Fast, accurate, and robust code
Online information from vendor
------------------------------
* Homepage: http://molcas.org/
* Documentation: http://molcas.org/documentation/manual/
* For download: http://molcas.org/download.html
License and access policy
-------------------------
Molcas comes with a non-free computer center license, though it is free for academic employees from the Nordic region.
The HPC group @ UiT have an agreement with Molcas as a part of the old application management project, that we do make a central
install of the code - but users who wants access needs to document that they have made an agreement with Molcas. This proof of agreement should
then be mailed (e or non-e) to support@metacenter.no to be granted membership of the molcas group on Stallo.
For the future, we are considering establishing the same policy as for :ref:`ORCA`, especially since the vendor has done a substantial job at decomplicating
the install procedure using CMake.
Citation
--------
When publishing results obtained with the referred software referred, please do check the developers web page in order to find the correct citation(s).
Usage
=====
You load the application by typing:
.. code-block:: bash
module load Molcas/molcas82-intel-2015a
For more information on available versions of Molcas, type:
.. code-block:: bash
module avail Molcas
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.. _first_time_molcas:
================================
First time you run a Molcas job?
================================
This page contains info aimed at first time
users of Molcas on Stallo. Please look carefully through the
provided examples. Also note that the job-script example is rather richly
commented to provide additional and relevant info.
If you want to run this testjob, copy the input example and the job script example shown below
into your test job folder (which I assume you have created in advance).
Molcas comes with one input file and one geometry file. For simplicity, we have tarred them together and you
may download them here: :download:`Ethane-input<../files/C2H6.tar.gz>`
Molcas runscrip example
-----------------------
.. include:: ../files/job_molcas.sh
:literal:
**NB: Note that some of Molcas´s various modules are not mpi scalable. Consult the vendor-provided manual for scaling issues. Our example is based on a single-node all cores job.**
You can also download the runscript file here: :download:`Molcas run script<../files/job_molcas.sh>`
The runscript example and the input are also on Stallo
------------------------------------------------------
Type:
.. code-block:: bash
module load Molcas/molcas82-intel-2015a
cd <whereevertestfolderyouhave> # For instance testmolcas
cp -R /global/hds/software/notur/apprunex/Molcas/* .
When you have all the necessary files in the correct folders, submit the job by typing:
.. code-block:: bash
sbatch job_molcas.sh
To verify that nothing has gone wrong, check the status file. If it concludes "happy landing" you are on the safe side, most probably. Good luck.
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.. _ORCA:
====
ORCA
====
Some users have requested an installation of the ORCA quantum chemistry program
on Stallo. Unfortunately, ORCA does not come with a computer center license,
which basically means that we cannot easily install it globally on Stallo.
However, the code is free of charge and available for single users or at
research group level, provided compliance with the *ORCA End User License
Agreement*. This means that interested users can download the precompiled
executables from the ORCA web page themselves, and run the code on Stallo.
* Homepage: https://orcaforum.kofo.mpg.de
Installation
============
The following has been tested for version 4.2.1 on Stallo:
1) Go to the ORCA forum page and register as user
2) Wait for activation e-mail (beware of spam filters...)
3) Log in and click on **Downloads** in the top menu
4) Download the Linux x86-64 complete archive and place it somewhere in your home folder
5) Extract the tarball: ``$ tar xf orca_4_2_1_linux_x86-64_openmpi314.tar.xz``
6) The ``orca`` executable is located in the extracted archive and should work out of the box
Usage
=====
In order to run ORCA in parallel the OpenMPI module must be loaded, and it is
important to use the **full path** to the executable
.. code-block:: bash
$ module load OpenMPI/3.1.3-GCC-8.2.0-2.31.1
$ /full/path/to/orca orca.inp >orca.out
Note that the calculation should not be launched by ``mpirun``, but you should use
the input keyword ``nprocs``. A very simple example input file is provided below.
Please refer to the ORCA manual (can be downloaded from the same download page
on the ORCA forum) for details on how to run the code for your application.
ORCA input example
------------------
.. include:: ../files/orca.inp
:literal:
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# ORCA
{% include list.liquid all=true %}
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# Chemistry
{% include list.liquid all=true %}
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.. _Schrodinger:
==========================
Schrödinger Product Suites
==========================
A description of the Schrödinger product suites that are available for norwegian academic users
Related information
-------------------
.. toctree:: :maxdepth: 1
:titlesonly:
run_schrod
install_schrod
General Information
===================
NOTUR is, together with the user community, funding a national license of Schrödinger´s Small Molecule Drug Discovery Suite and PyMol. This implies that users are allowed to download and install their own copies of the software, and using the license server for providing license tokens. More about that in another document: :ref:`install_schrodinger`.
Online info from vendor
-----------------------
* Homepage: https://www.schrodinger.com/
About the suites:
* Small Molecule Drug Discovery Suite: https://www.schrodinger.com/suites/small-molecule-drug-discovery-suite
* PyMol: https://www.schrodinger.com/pymol
For documentation, you need to create an account (free) and log in. Documentation is also available in the software home folder:
.. code-block:: bash
$ module load Schrodinger/{version}
$ cd $SCRODINGER/docs
Remeber to point a pdf-reader or html reader to the documentation if you plan to read it on Stallo.
Support people in NOTUR do not provide trouble shooting guides anymore. For Schrödinger suites, the vendor company is giving good support \
on a system level. Problems related to running schrodinger on a NOTUR facility should be adressed to us.
Citation
--------
When publishing results obtained with the referred software, please do check the developers web page in order to find the correct citation(s).
License and access policy
-------------------------
The licenses of Schrödinger Product Suites are commercial.
NOTUR is, together with the user community, funding a national license of Schrödinger´s Small Molecule Drug Discovery Suite and PyMol. The licenses are administered by a license server based on flexlm. The adress for this license setup is available upon request to support@metacenter.no.
The outtake of license tokens is monitored on regular basis, and we try to catch those who seems to use the suite for regular scientific production and ask them to contribute financially to the overall deal. So far, this policy have worked fairly well.
All members of the co-funding research groups have unlimited access to the Schrödinger´s Small Molecule Drug Discovery Suite and PyMol license tokens.
`Please note that this is an academic type license; meaning that research institutes not being part of Norwegian Universities must provide their own license to be able to use and publish results obtained with this code on NOTUR installlations.`
Usage
=====
The most commot usage of schrodinger on Stallo is through the Maestro gui. Log in with x11-tunneling enabled, or through the web-interface http://stallo-gui.uit.no/.
Load the module of choice:
Use
.. code-block:: bash
$ module avail Schrodinger
to see which versions of Schrodinger are available.
Use
.. code-block:: bash
$ module load Schrodinger/<version> # i.e 2017-2-intel-2016a
to get access to Schrodinger. The batch resource allocation system is integrated with the gui through a schrodinger.hosts file which is centrally administered.
Please do not hold a local copy!
For examples on how to submit Schrodinger jobs on Stallo, look here :ref:`run_schrodinger`.
Finding available licenses
==========================
This should in principle be obsolete for users, since we are promised unlimited licenses in the national system. But still, for the curious souls:
If you want to know about avaible licenses; do the following
(after loading the schrodinger module)
.. code-block:: bash
$ licadmin STAT
This command will give you information about license status for the national Schrodinger suite licenses.
Access to PyMOL
===============
For users that wants/needs access to PyMOL, please fill out the following form: https://skjema.uio.no/pymol-access.
**Please not that this strategy replaces old habits of sending personal emails in this regard.**
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.. _install_schrodinger:
Installing Schrodinger on your local client machine
===================================================
This page contains info about how to download and install the Schrodinger suite on your local client machine.
Getting an account on www.schrodinger.com
-----------------------------------------
You need to create an account (free) and log in with the password you create. Then you download the relevant software for your machine and follow install procedures that comes with the software.
Getting access to the national license
--------------------------------------
Since we have a joint funding of license tokens between user community and national bodies, members of the funding research groups are entiteled to get access to the license from their client machines. The easiest way to do this is by downloading the following license file:
.. include:: ../files/License_Schrodinger.txt
:literal:
(Can also be downloaded from here: :download:`License_Schrodinger.txt <../files/License_Schrodinger.txt>`)
This should be sufficient to make your personal copy of Schrodinger Small Molecule Drug Discovery suite work on your machine.
**Note: It is a known bug that the configure setup of Schrodinger reports no license found even if the program suite works well. Please ignore.**
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.. _run_schrodinger:
Running Schrodinger jobs on Stallo
==================================
This page contains info about how to submit Schrodinger jobs on Stallo, based
on two sets of examples - one for docking and one for molecular dynamics -
provided to us by users.
Pay enhanced attention to the `ssh -Y c1-2` in examples below; this represents the adviced behaviour on how to run jobs on Stallo for your benefit solely!
A more thorough explanation to this, is that maestro starts a distribution and surveilance process that creates the jobs that enters the shared resources allocation (aka batch) system. If this process dies, the underlying jobs dies disregarding their computational status. This could have been solved by just running this on the login node, but imagine how it would have been with 1000 simultanious users sharing two login nodes and 50 of those ran 20-40 simultaneous perl and python processes each on the login nodes. So, please do as told.
Submitting jobs through the maestro interface
---------------------------------------------
Log in to Stallo with either x11-tunnelling enabled or through stallo-gui.uit.no.
To get download the jobscript example(s), type the following:Direct log in via ssh:
.. code-block:: bash
$ module load Schrodinger/2017-2-intel-2016a
$ cp -r /global/hds/software/notur/apprunex/Schrodinger/* ~
# Be sure this do not overwrite any folder or info you may want to keep in your home.
Note: This suite is quite extensive in features, and we generally advice you to either join tutorial courses, talk to experts in your proximity or read the vendor-provided documentation if you have absolutely no knowledge about how to use this suite of software. Here we only provide a couple of rough startup examples to get you up running.
Then;
**If you want to test the molecular dynamics package**
Do the following:
.. code-block:: bash
$ ssh -Y c1-2
$ module load Schrodinger/2017-2-intel-2016a
$ cd example_md
$ maestro -SGL
and start a Molecular Dynamics task from the Tasks menu bar. Load model system from file, choose desmond_md_example.cms
Set all settings to your satisfaction.
Open the job settings; you should only be presented the following options
#. localhost (only for job-setups)
#. batch-normal (2 days (=48hrs) of walltime/1000 cpus)
#. batch-long (21 days (504 hrs) of walltime/1000cpus)
Make sure that only one option is highlighted (only one of the batch-XXX´s). Press the run button and supervise running.
(This test case is provided us by Vladimir Pomogaev.)
**If you want to test the docking package**
Do the following:
.. code-block:: bash
$ ssh -Y c1-2
$ module load Schrodinger/2017-2-intel-2016a
$ cd example_docking
$ maestro -SGL
and start a Docking task from the Tasks menu bar.
Choose abl1-kinase-t316i_glide-grid.zip as grid
Use ligands from file; if you want to run a very short test - choose abl1-kinase-t316i_minimized.mae, for a longer test browse for SD and choose Asinex-50000-3D-minimized.sdf set of ligands. Set all settings to your satisfaction. For job settings, see example above.
(This test case is provided us by Bjorn Dalhus.)
If you want to run vsw routines from the command line
-----------------------------------------------------
The Schrodinger suite is shipped with scripts that connects the software installation with the system batch resource allocation setup, making it possible to submit glide jobs from the linux command line.
Examples of valid command line submissions using the vsw-tool on Stallo:
.. code-block:: bash
vsw *.inp -DRIVERHOST batch-normal -host_glide batch-normal:100 -NJOBS 1 –adjust
If you are worried that you will not refind your files for emergency startup:
.. code-block::bash
vsw *.inp -DRIVERHOST batch-normal -host_glide batch-normal:100 -NJOBS 1 -LOCAL
vsw *.inp -DRIVERHOST batch-normal -host_glide batch-normal:100 -NJOBS 1 -SAVE
**Note the following details:**
#. When the Schrodinger module is loaded on Stallo, the Schrodinger software folder is set in path, making $SCHRODINGER unecessary.
#. The Schrodinger setup on Stallo writes to the scratch file system by default, potentially making both the -LOCAL and the -SAVE flags uneccesary.
#. We do not recommend the -REMOTEDRIVER flag due to the risk of loosing jobs related to the admin process running out allocated time.
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.. _TURBOMOLE:
============================
The TURBOMOLE program system
============================
Information regarding the quantum chemistry program system TURBOMOLE.
Related information
===================
.. toctree::
:maxdepth: 1
run_turbo.rst
General Information
===================
Description
-----------
TURBOMOLE is a quantum chemical program package, initially developed in the
group of Prof. Dr. Reinhart Ahlrichs at the University of Karlsruhe and at the
Forschungszentrum Karlsruhe. In 2007, the TURBOMOLE GmbH (Ltd) was founded by
the main developers of the program, and the company took over the responsibility
for the coordination of the scientific development of the program, to which it
holds all copy and intellectual property rights.
Online info from vendor
-----------------------
* Homepage: http://www.turbomole-gmbh.com
* Documentation: http://www.turbomole-gmbh.com/turbomole-manuals.html
License information
-------------------
Sigma2 holds a national TURBOMOLE license covering all computing centers in Norway.
Citation
--------
When publishing results obtained with this software, please check with the
developers web page in order to find the correct citation(s).
TURBOMOLE on Stallo
===================
Usage
-----
To see which versions of TURBOMOLE are available
.. code-block:: bash
$ module avail turbomole
Note that the latest (as of May 2018) version 7.2 comes in three different
parallelization variants
* TURBOMOLE/7.2
* TURBOMOLE/7.2.mpi
* TURBOMOLE/7.2.smp
where the latter two correspond to the old separation between distributed memory
(mpi) and shared memory (smp) implementations that some users may know from
previous versions of the program. We recommend, however, to use the new hybrid
parallelization scheme (new in v7.2) provided by the ``TURBOMOLE/7.2`` module.
In order to run efficiently in hybrid parallel the SLURM setup must be correct
by specifying CPUs rather than tasks per node
.. code-block:: bash
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=20
i.e. do NOT specify ``--ntasks=40``. Also, some environment variables must be
set in the run script
.. code-block:: bash
export TURBOTMPDIR=/global/work/${USER}/${SLURM_JOBID}
export PARNODES=${SLURM_NTASKS}
export OMP_NUM_THREADS=${SLURM_CPUS_PER_TASK}
See the TURBOMOLE example for more details.
NOTE: The new hybrid program (v7.2) will launch a separate server process in
addition to the requested parallel processes, so if you run e.g. on 20 cores
each on two nodes you will find one process consuming up to 2000% CPU on each
node plus a single process on the first node consuming very little CPU.
NOTE: We have found that the hybrid program (v7.2) runs efficiently only if
*full* nodes are used, e.i. you should always ask for ``--cpus-per-node=20``,
and scale the number of nodes by the size of the calculation.
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.. _turbomole_example:
==========================
TURBOMOL runscript example
==========================
.. include:: ../files/job_turbo.sh
:literal:
TURBOMOL example on Stallo
--------------------------
The above runscript can be found on Stallo along with the necessary input files
to perform a simple DFT calculation. To get access to the examples directory,
load the ``notur`` module
.. code-block:: bash
$ module load notur
$ cp -r /global/hds/software/notur/apprunex/TURBOMOLE .
From this directory you can submit the example script (you need to specify a
valid account in the script first)
.. code-block:: bash
$ sbatch job_turbo.sh
The calculation should take less than 2 min, but may spend much more than this
in the queue. Verify that the output file ``dscf.out`` ends with
.. code-block:: bash
**** dscf : all done ****
Please refer to the TURBOMOLE manual for how to setup different types of
calculations.
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# VASP
{% include list.liquid all=true %}
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.. _VASP:
==========================================
VASP (Vienna Ab initio Simulation Package)
==========================================
Information regarding the Vienna Ab initio Simulation Package (VASP) installed on Stallo.
Related information
===================
.. toctree::
:maxdepth: 1
firsttime_vasp.rst
vasp_on_stallo.rst
General information
===================
Description
-----------
VASP is a complex package for performing ab-initio quantum-mechanical molecular dynamics (MD) simulations using pseudopotentials or the projector-augmented wave method and a plane wave basis set. The approach implemented in VASP is based on the (finite-temperature) local-density approximation with the free energy as variational quantity and an exact evaluation of the instantaneous electronic ground state at each MD time step.
VASP uses efficient matrix diagonalisation schemes and an efficient Pulay/Broyden charge density mixing. These techniques avoid all problems possibly occurring in the original Car-Parrinello method, which is based on the simultaneous integration of electronic and ionic equations of motion.
The interaction between ions and electrons is described by ultra-soft Vanderbilt pseudopotentials (US-PP) or by the projector-augmented wave (PAW) method. US-PP (and the PAW method) allow for a considerable reduction of the number of plane-waves per atom for transition metals and first row elements. Forces and the full stress tensor can be calculated with VASP and used to relax atoms into their instantaneous ground-state.
There are various type plug ins and added functionality to VASP. On Stallo, we have added support for the Texas transition state tools (`vTST <http://theory.cm.utexas.edu/vtsttools/>`_), the `VASPsol <http://vaspsol.mse.ufl.edu>`_ implicit solvation model made by Hennig and Mathew from University of Florida and the `Bayesian Error Estimation Functionals <https://suncat.stanford.edu/about/facilities/software>`_ from the SUNCAT center, Standford.
Online information from vendor
------------------------------
* Homepage: https://www.vasp.at
* Documentation: https://cms.mpi.univie.ac.at/wiki/index.php/The_VASP_Manual
License and access policy
-------------------------
The VASP license is proprietary and commercial. It is based on group license policy, and for NOTUR systems VASP packages falls in the "bring your own license" category. See :ref:`About_licenses`.
The Vasp program is not distributed via site licences. However, HPC-staff in NOTUR have access to the VASP code to be able to support any research groups that have a valid VASP license.
VASP is a commercial software package that requires a license for all who wants use it. To run VASP:
#. Your group must have a valid licence. To acquire a licence, please consult this link: https://www.vasp.at/index.php/faqs/71-how-can-i-purchase-a-vasp-license.
#. We need to get a confirmation from a VASP representative to confirm that you have access to the license. Your group representative needs to contact Dr. Doris Vogtenhuber (doris.vogtenhuber@univie.ac.at) from the VASP team and ask her to send a confirmation email to us (support@metacenter.no) to confirm that you have a valid licence. Once we get a confirmation email we will grant you access to run VASP.
The support people in NOTUR, do not provide trouble shooting guides anymore, due to a national agreement that it is better for the community as a whole to add to the community info/knowledge pool where such is made available. Also, HPC staff from UiT does not provide any support to VASP 4 anymore, basically due to age of the code.
Citation
---------
When publishing results obtained with the referred software referred, please do check the developers web page in order to find the correct citation(s).
Usage
=====
You load the application by typing:
.. code-block:: bash
$ module load VASP
This command will give you the default version.
For more information on available versions, type:
.. code-block:: bash
$ module avail VASP
Experiencewise, VASP is a rather memory intensive code. Users are advised to read up on the general job script example(s) for SLURM and Stallo, and also how to specify memory in `SLURM <https://slurm.schedmd.com>`_.
About the VASP version(s) installed on Stallo
---------------------------------------------
Since the installation we made on Stallo is rather taylor made, we have gathered information about this here: :doc:`vasp_on_stallo`.
Here we also adress compilation relevant information for those who would like to do it themselves.
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.. _first_time_vasp:
==============================
First time you run a VASP job?
==============================
This page contains info aimed at first time
users of VASP on Stallo, but may also be usefull to
more experienced users. Please look carefully through the
provided examples. Also note that the job-script example is rather richly
commented to provide additional and relevant info.
If you want to run this testjob, download the copies of the scripts and put them
into your test job folder (which I assume you have created in advance).
VASP input example
------------------
Download the tarred job :download:`CeO2job-files <../files/CeO2.tar.gz>`.
move this file to your test job folder on Stallo and type
.. code-block:: bash
tar -zxf CeO2.tar.gz
Then; download the job-script as seen here:
.. include:: ../files/job_vasp.sh
:literal:
Download it here :download:`job_vasp.sh <../files/job_vasp.sh>`.
These files are also available on Stallo
----------------------------------------
.. code-block:: bash
module load VASP/5.4.1.plain-intel-2016a
cd <to whatever you call your test folder> # for instance testvasp
cp -R /global/hds/software/notur/apprunex/VASP/* .
sbatch job_vasp.sh
and you are up running. Happy hunting.
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.. _vasp_on_stallo:
================================
About the VASP install on Stallo
================================
This page contains information related to the installation of VASP on Stallo. Some of this is relevant also for self-compilation of the code, for those who want to give this a try.
VASP on Stallo
--------------
Note that the VASP installation on Stallo mainly follows the standard syntax introduced by the vASP team with their new installation scheme. Based on their system, we have added two binaries - as commented under.
If you do
.. code-block:: bash
module avail VASP
on Stallo, you will notice that for version 5.4.1 and onwards, there is a dramatic increase in the available binaries. And this might appear confusing.
First; All versions of VASP is compiled with the support for maximally-localised Wannier functions and the Wannier90 program and also the MPI flag in FPP (-DMPI)
Second; Each release of VASP is compiled in two different versions; "tooled" and "plain".
* VASP/x.y.z.tooled is a version where all necessary support for Texas transition state tools (vTST) and the explicit solvation model (VSPsol) and BEEF is added.
* VASP/x.y.z.plain is the version without this support/ additions. (Relatively unmodified source).
The reason for this is that we are uncertain of effects on the tooles on calculated numbers, due to reproducibility, we have chosen to hold the different versions separate.
Then it starts getting interesting: For each version, there are 15 different binaries - consisting of 3 groups of 5.
* unmodified group; binaries without any additional name to them; vasp_std
* noshear: vasp_std_noshear
* abfix: vasp_std_abfix
The unmodified group is compiled on basis on the unmodified set of fortran files that comes with the code.
The noshear version is compiled with a modified version of the file constr_cell_relax.F file where shear forces are not calculated.
The abfix version is compiled with a modified version of the constr_cell_relax.F file where two lattice vectors are fixed.
There are 5 different binaries in each group, all compiled with the same FPP settings (mentioned below):
* vasp_std
* vasp_gam
* vasp_ncl
These are familiar from the standard build system that is provided with the code. In addtion to these, we have
* vasp_tbdyn
* vasp_gam_tbdyn
FPP settings for each binary
----------------------------
#. vasp_std is compiled with the following additional FPP flag(s): -DNGZhalf
#. vasp_gam is compiled with the following additional FPP flag(s): -DNGZhalf -DwNGZhalf
#. vasp_ncl is compiled with the following additional FPP flag(s): no modifcations in FPP settings
#. vasp_tbdyn is compiled with the following additional FPP flag(s): -DNGZhalf -Dtbdyn
#. vasp_gam_tbdyn is compiled with the following additional FPP flag(s): -DNGZhalf -DwNGZhalf -Dtbdyn
We would be happy to provide a copy of our build scripts (patches) upon request.
About memory allocation for VASP
--------------------------------
VASP is known to be potentially memory demanding. Quite often, you might experience to use less than the full number of cores on the node, but still all of the memory.
For core-count, node-count and amounts of memory on Stallo, see :ref:`about_stallo`.
There are two important considerations to make:
First: Make sure that you are using the SBATCH --exclusive flag in your run script.
Second: How to allocate all the memory:
.. literalinclude:: ../../../jobs/files/slurm-big-memory.sh
:language: bash
:linenos:
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*-------------------------------------------------------------------------------
&GATEWAY
coord
C2H6.xyz
basis
ANO-S-VDZ
group
y xz
*-------------------------------------------------------------------------------
&SEWARD
Title
Ethane DFT test job
*-------------------------------------------------------------------------------
>>foreach DFT in (BLYP, B3LYP )
&SCF ; KSDFT = $DFT
>>enddo
*-------------------------------------------------------------------------------
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8
$Revision: 7.8 $ bohr
C -0.2003259123 0.0000000000 -1.4269296645
C 0.2003259123 0.0000000000 1.4269296645
H -2.2104549009 0.0000000000 -1.9008373239
H 2.2104549009 0.0000000000 1.9008373239
H 0.6485856097 -1.6668156152 -2.3021358629
H 0.6485856097 1.6668156152 -2.3021358629
H -0.6485856097 -1.6668156152 2.3021358629
H -0.6485856097 1.6668156152 2.3021358629
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SERVER Lisens-Schrodinger.uit.no 0050569f5819 27008
VENDOR SCHROD PORT=53000
USE_SERVER
software/applications/chemistry/files/caffeine.adf deleted 100755 → 0
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title Caffeine for scale testing
integration 6.0
units
length angstrom
end
symmetry Nosym
atoms cartesian
C 1.179579 0.000000 -0.825950
C 2.359623 0.000000 0.016662
C 2.346242 0.000000 1.466600
N 1.092573 0.000000 2.175061
C 0.000000 0.000000 1.440000
N 0.000000 0.000000 0.000000
N 3.391185 0.000000 1.965657
C 4.217536 0.000000 1.154419
N 3.831536 0.000000 0.062646
C 4.765176 -0.384157 -0.964164
C 1.058378 -0.322767 3.578004
O -1.345306 0.000000 1.827493
C -1.260613 -0.337780 -0.608570
O 1.192499 0.000000 -2.225890
H -1.997518 -0.535233 0.168543
H -1.598963 0.492090 -1.227242
H -1.138698 -1.225644 -1.227242
H 0.031688 -0.271264 3.937417
H 1.445570 -1.329432 3.728432
H 1.672014 0.388303 4.129141
H 4.218933 -0.700744 -1.851470
H 5.400826 0.464419 -1.212737
H 5.381834 -1.206664 -0.604809
H 5.288201 0.000000 1.353412
end
BASIS
Type TZ2P
End
geometry
end
scf
end
unrestricted
charge 0 0
xc
gga PW91
end
noprint frag sfo
endinput
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#!/bin/bash -l
################### ADF Job Batch Script Example ###################
# Section for defining queue-system variables:
#-------------------------------------
# This script asks for a given set of cores nodes and cores. Stallo has got 16 or 20 cores/node,
# asking for something that adds up to both is our general recommodation (80, 160 etc), you would
# then need to use --ntasks instead of --nodes and --ntasks-per-node. (replace both).
# Runtime for this job is 59 minutes; syntax is hh:mm:ss.
# Memory is set to the maximum advised for a full node, 1500MB/core - giving a total
# of 30000MB/node and leaving some for the system to use. Memory
# can be specified pr core, virtual or total pr job (be carefull).
#-------------------------------------
# SLURM-section
#SBATCH --job-name=adf_runex
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=20
#SBATCH --time=00:59:00
#SBATCH --mem-per-cpu=1500MB # Be ware of memory needs, might be a lot higher if you are running Zora basis, for example.
#SBATCH --output=adf_runex.log
#SBATCH --mail-type=ALL
#SBATCH --exclusive
#SBATCH --partition=multinode
######################################
# Section for defining job variables and settings:
input=caffeine # Name of input without extention
ext=adf # We use the same naming scheme as the software default, also for extention
cores=$SLURM_NTASKS # Number of cores potentially used by mpi engine in submit procedure
# We load all the default program system settings with module load:
module --quiet purge
module load ADF/adf2017.108
# Now we create working directory and temporary scratch for the job(s):
# Necessary variables are defined in the notur and the software modules.
export SCM_TMPDIR=/global/work/$USER/$SLURM_JOBID
mkdir -p $SCM_TMPDIR
# Preparing and moving inputfiles to tmp:
submitdir=$SLURM_SUBMIT_DIR
cp ${input}.${ext} $SCM_TMPDIR
cd $SCM_TMPDIR
# In case necessary, set SCM_IOBUFFERSIZE
#export SCM_IOBUFFERSIZE=1024 # Or higher if necessary.
######################################
# Section for running the program and cleaning up:
# Running the program:
time adf -n $cores < ${input}.${ext} > adf_$input.out
# Cleaning up and moving files back to home/submitdir:
# Make sure to move all essential files specific for the given job/software.
cp adf_$input.out $submitdir/adf_$input.out
cp TAPE21 $submitdir/$input.t21
# To zip some of the output might be a good idea!
#gzip $input.t21
#mv $input.t21.gz $submitdir/
# Investigate potentially other files to keep:
echo $(pwd)
echo $(ls -ltr)
# ALWAYS clean up after yourself. Please do uncomment the following line
#cd $submitdir
#rm -r $tempdir/*
echo "Job finished at"
date
################### Job Ended ###################
exit 0
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#!/bin/bash -l
################### ADF Job Batch Script Example ###################
# Section for defining queue-system variables:
#-------------------------------------
# This script asks for a given set of cores nodes and cores. Stallo has got 16 or 20 cores/node,
# asking for something that adds up to both is our general recommodation (80, 160 etc), you would
# then need to use --ntasks instead of --nodes and --ntasks-per-node. (replace both).
# Runtime for this job is 59 minutes; syntax is hh:mm:ss.
# Memory is set to the maximum advised for a full node,
# of 30000MB/node and leaving some for the system to use. Memory
# can be specified pr core, virtual or total pr job (be carefull).
#-------------------------------------
# SLURM-section
#SBATCH --job-name=band_runex
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=20
#SBATCH --time=00:59:00
#SBATCH --mem-per-cpu=1500MB # Be ware of memory needs!
#SBATCH --output=band_runex.log
#SBATCH --mail-type=ALL
#SBATCH --exclusive
#SBATCH --partition=multinode
######################################
# Section for defining job variables and settings:
input=silicone # Name of input without extention
ext=adf # We use the same naming scheme as the software default, also for extention
cores=40 # Number of cores potentially used by mpi engine in submit procedure
# We load all the default program system settings with module load:
module --quiet purge
module load ADF/adf2017.108
# Now we create working directory and temporary scratch for the job(s):
# Necessary variables are defined in the notur and the software modules.
export SCM_TMPDIR=/global/work/$USER/$SLURM_JOBID
mkdir -p $SCM_TMPDIR
# Preparing and moving inputfiles to tmp:
submitdir=$SLURM_SUBMIT_DIR
tempdir=$SCM_TMPDIR
cd $submitdir
cp ${input}.${ext} $tempdir
cd $tempdir
# In case necessary, set SCM_IOBUFFERSIZE
#export SCM_IOBUFFERSIZE=1024 # Or higher if necessary.
######################################
# Section for running the program and cleaning up:
# Running the program:
time band -n $cores < ${input}.${ext} > band_$input.out
# Cleaning up and moving files back to home/submitdir:
# Make sure to move all essential files specific for the given job/software.
cp band_$input.out $submitdir/band_$input.out
cp TAPE21 $submitdir/$input.t21
# To zip some of the output might be a good idea!
#gzip $input.t21
#mv $input.t21.gz $submitdir/
# Investigate potentially other files to keep:
echo $(pwd)
echo $(ls -ltr)
# ALWAYS clean up after yourself. Please do uncomment the following line
#cd $submitdir
#rm $tempdir/*
#rmdir $tempdir
echo "Job finished at"
date
################### Job Ended ###################
exit 0
software/applications/chemistry/files/job_dalton.sh deleted 100755 → 0
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#!/bin/bash -l
# Write the account to be charged here
# (find your account number with `cost`)
#SBATCH --account=nnXXXXk
#SBATCH --job-name=daltonexample
# we ask for 20 cores
#SBATCH --ntasks=20
# run for five minutes
# d-hh:mm:ss
#SBATCH --time=0-00:05:00
# 500MB memory per core
# this is a hard limit
#SBATCH --mem-per-cpu=500MB
# Remove percentage signs to turn on all mail notification
#%%%SBATCH --mail-type=ALL
# You may not place bash commands before the last SBATCH directive!
# Load Dalton
# You can find all installed Dalton installations with:
# module avail dalton
module load Dalton/2013.2
# Define the input files
molecule_file=caffeine.mol
input_file=b3lyp_energy.dal
# Define and create a unique scratch directory
SCRATCH_DIRECTORY=/global/work/${USER}/daltonexample/${SLURM_JOBID}
mkdir -p ${SCRATCH_DIRECTORY}
cd ${SCRATCH_DIRECTORY}
# Define and create a temp directory
TEMP_DIR=${SCRATCH_DIRECTORY}/temp
mkdir -p ${TEMP_DIR}
# copy input files to scratch directory
cp ${SLURM_SUBMIT_DIR}/${input_file} ${SLURM_SUBMIT_DIR}/${molecule_file} ${SCRATCH_DIRECTORY}
# run the code
dalton -N ${SLURM_NTASKS} -t ${TEMP_DIR} ${input_file} ${molecule_file}
# copy output and tar file to submit dir
cp *.out *.tar.gz ${SLURM_SUBMIT_DIR}
# we step out of the scratch directory and remove it
cd ${SLURM_SUBMIT_DIR}
rm -rf ${SCRATCH_DIRECTORY}
# happy end
exit 0
software/applications/chemistry/files/job_g09.sh deleted 100755 → 0
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#!/bin/bash -l
################### Gaussian Job Batch Script Example ###################
# Section for defining queue-system variables:
#-------------------------------------
# This script asks for a given set of nodes and cores. Stallo has got 16 or 20 cores/node,
# so you need to know what you want.
# Runtime for this job is 59 minutes; syntax is hh:mm:ss.
# Memory is set to the maximum advised for a full node, 1500MB/core - giving a total
# of 30000MB/node and leaving some for the system to use. Memory
# can be specified pr core, virtual or total pr job (be carefull).
#-------------------------------------
# SLURM-section
#SBATCH --job-name=g09_runex
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=20
#SBATCH --time=00:59:00
#SBATCH --mem-per-cpu=1500MB
#SBATCH --output=g09_runex.log
#SBATCH --mail-type=ALL
#SBATCH --exclusive
#SBATCH --partition=gaussian # This is a particular gasussian partition due to some technicalities with SLURM.
######################################
# Section for defining job variables and settings:
input=caffeine # Name of input without extention
ext=com # We use the same naming scheme as the software default extention
# We load all the default program system settings with module load:
module --quiet purge
module load Gaussian/09.d01
# Now we create working directory and temporary scratch for the job(s):
# Necessary variables are defined in the notur and the software modules.
export GAUSS_SCRDIR=/global/work/$USER/$SLURM_JOB_ID
mkdir -p $GAUSS_SCRDIR
# Preparing and moving inputfiles to tmp:
submitdir=$SLURM_SUBMIT_DIR
tempdir=$GAUSS_SCRDIR
cp $submitdir/${input}.${ext} $tempdir
cd $tempdir
# Preparation of inputfile is done by G09.prep in folder $g09tooldir
# If you want to inspect it, cd $g09tooldir after loading the gaussian module
Gaussian.prep $input
######################################
# Section for running the program and cleaning up:
# Running the program:
time g09 < ${input}.${ext} > gaussian_$input.out
# Cleaning up and moving files back to home/submitdir:
# Make sure to move all essential files specific for the given job/software.
cp gaussian_$input.out $submitdir
cp $input.chk $submitdir
# To zip some of the output might be a good idea!
#gzip $resultszip
#mv $resultzip.gz $SUBMITDIR/
# Investigate potentially other files to keep:
echo $(pwd)
echo $(ls -ltr)
# ALWAYS clean up after yourself. Please do uncomment the following line
#cd $submitdir
#rm $tempdir/*
#rmdir $tempdir
echo "Job finished at"
date
################### Job Ended ###################
exit 0
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#!/bin/bash -l
################### VASP Job Batch Script Example ###################
# Section for defining queue-system variables:
#-------------------------------------
# This script asks for a given set of cores nodes and cores. Stallo has got 16 or 20 cores/node,
# asking for something that adds up to both is our general recommodation (80, 160 etc), you would
# then need to use --ntasks instead of --nodes and --ntasks-per-node. (replace both).
# Runtime for this job is 59 minutes; syntax is hh:mm:ss.
# Memory is set to the maximum advised for a full node, 1500MB/core - giving a total
# of 30000MB/node and leaving some for the system to use. Memory
# can be specified pr core, virtual or total pr job (be carefull).
#-------------------------------------
# SLURM-section
#SBATCH --job-name=molcas_runex
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=20
##SBATCH --ntasks=20
#SBATCH --time=00:59:00
#SBATCH --mem-per-cpu=1500MB
#SBATCH --output=molcas_runex.log
#SBATCH --mail-type=ALL
#SBATCH --exclusive
######################################
# Section for defining job variables and settings:
proj=ehtane # Name of project/folder
input=C2H6 # Name of job input
# We load all the default program system settings with module load:
module --quiet purge
module load Molcas/molcas82-intel-2015a
# You may check other available versions with "module avail Molcas"
# Now we create working directory and temporary scratch for the job(s):
# Necessary variables are defined in the notur and the software modules.
export MOLCAS_WORKDIR=/global/work/$USER/$SLURM_JOBID
mkdir -p $MOLCAS_WORKDIR
# Preparing and moving inputfiles to tmp:
submitdir=$SLURM_SUBMIT_DIR
tempdir=$MOLCAS_WORKDIR
cd $submitdir
cp ${input}.* $tempdir
cd $tempdir
######################################
# Section for running the program and cleaning up:
# Running the program:
time molcas Project=${proj} -f ${input}*.input
# Cleaning up and moving files back to home/submitdir:
# Make sure to move all essential files specific for the given job/software.
cp * $submitdir
# To zip some of the output might be a good idea!
#gzip $resultszip
#mv $resultzip.gz $SUBMITDIR/
# Investigate potentially other files to keep:
echo $(pwd)
echo $(ls -ltr)
# ALWAYS clean up after yourself. Please do uncomment the following line
#cd $submitdir
#rm $tempdir/*
#rmdir $tempdir
echo "Job finished at"
date
################### Job Ended ###################
exit 0
software/applications/chemistry/files/job_turbo.sh deleted 100755 → 0
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#!/bin/bash -l
#SBATCH --job-name=turbomole
#SBATCH --account=nnXXXXk
#SBATCH --time=1:00:00
#SBATCH --mem-per-cpu=1000MB
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=20
#SBATCH --mail-type=ALL
#SBATCH --partition=multinode
# For TURBOMOLE v7.2:
# --nodes : scale by the size of the calculation
# --ntasks-per-node : always a singel task per node
# --cpus-per-task : always full nodes
# load modules
module --quiet purge
module load TURBOMOLE/7.2
# TURBOMOLE needs this variable
export TURBOTMPDIR=/global/work/${USER}/${SLURM_JOBID}
workdir=${TURBOTMPDIR}
submitdir=${SLURM_SUBMIT_DIR}
# copy files to work
mkdir -p $workdir
cd $workdir
cp $submitdir/* $workdir
# set parallel environment
export PARNODES=${SLURM_NTASKS}
export OMP_NUM_THREADS=${SLURM_CPUS_PER_TASK}
# run jobex calculation
dscf > dscf.out
# copy files back
mv * $submitdir
# clean up
rm -r $workdir
exit 0
software/applications/chemistry/files/job_vasp.sh deleted 100755 → 0
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#!/bin/bash -l
################### VASP Job Batch Script Example ###################
# Section for defining queue-system variables:
#-------------------------------------
# This script asks for a given set of nodes and cores. cores. Stallo has got
# 16 or 20 cores/node, so you need to know what you want.
# Runtime for this job is 59 minutes; syntax is hh:mm:ss.
# Memory si set to the maximum amound adviced when asking for a full node, it
# adds up to 30 000MB, leaving a small part for the system to use. Memory
# can be specified pr core, virtual or total pr job (be carefull).
#-------------------------------------
# SLURM-section
#SBATCH --job-name=vasp_runex
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=20
#SBATCH --time=02:00:00
##SBATCH --mem-per-cpu=1500MB
#SBATCH --output=vasp_runex.log
#SBATCH --mail-type=ALL
#SBATCH --exclusive
#SBATCH --partition=multinode
######################################
# Section for defining job variables and settings:
# When you use this test script, make sure, your folder structure is as follows:
# ./job_vasp.sh
# ./CeO2job/INCAR
# ./CeO2job/KPOINTS
# ./CeO2job/POTCAR
# ./CeO2job/POSCAR
proj=CeO2job # Name of job folder
input=$(ls ${proj}/{INCAR,KPOINTS,POTCAR,POSCAR}) # Input files from job folder
# We load all the default program system settings with module load:
module --quiet purge
module load VASP/5.4.1.plain-intel-2016a
# You may check other available versions with "module avail VASP"
# Now we create working directory and temporary scratch for the job(s):
# Necessary variables are defined in the notur and the software modules.
export VASP_WORKDIR=/global/work/$USER/$SLURM_JOB_ID
mkdir -p $VASP_WORKDIR
# Preparing and moving inputfiles to tmp:
submitdir=$SLURM_SUBMIT_DIR
cp $input $VASP_WORKDIR
cd $VASP_WORKDIR
######################################
# Section for running the program and cleaning up:
# Running the program:
time mpirun vasp_std
# Cleaning up and moving files back to home/submitdir:
# Make sure to move all essential files specific for the given job/software.
cp OUTCAR $submitdir/${proj}.OUTCAR
# To zip some of the output might be a good idea!
#gzip results.gz OUTCAR
#mv $results.gz $submitdir/
# Investigate potentially other files to keep:
echo $(pwd)
echo $(ls -ltr)
# ALWAYS clean up after yourself. Please do uncomment the following line
# If we have to, we get really grumpy!
#cd $submitdir
#rm -r $VASP_WORKDIR/*
echo "Job finished at"
date
################### Job Ended ###################
exit 0
software/applications/chemistry/files/orca.inp deleted 100755 → 0
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! HF def2-TZVP
%pal
nprocs 4 # parallel execution
end
* xyz 0 1
C 0.0 0.0 0.0
O 0.0 0.0 1.13
*
software/applications/chemistry/files/silicone.adf deleted 100755 → 0
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TITLE Untitled
NumericalQuality Normal
UNITS
length Angstrom
angle Degree
END
ATOMS
Si -0.67875 -0.67875 -0.67875
Si 0.67875 0.67875 0.67875
END
Lattice
0.0 10.860 10.860
10.860 0.0 10.860
10.860 10.860 0.0
End
DOS
Enabled True
End
BasisDefaults
BasisType DZ
Core Large
End
XC
LDA SCF VWN
END
GeoOpt
UseVariableTrustRadius false
End
BZSTRUCT
Enabled True
END
end input
software/applications/firstime_run.rst.template deleted 100755 → 0
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.. _first_time_prog:
===================================
First time you run a VASP job?
===================================
This page contains info aimed at first time
users of VASP on Stallo, but may also be usefull to
more experienced users. Please look carefully through the
provided examples. Also note that the job-script example is rather richly
commented to provide additional and relevant info.
If you want to run this testjob, download the copies of the scripts and put them
into your test job folder (which I assume you have created in advance).
VASP input example:
--------------------
Download the tarred job :download:`CeO2job-files <../files/CeO2.tar.gz>`.
move this file to your test job folder on Stallo and type
.. code-block:: bash
tar -zxf CeO2.tar.gz
Then; download the job-script as seen here:
.. include:: ../files/job_vasp.sh
:literal:
Download it here :download:`job_vasp.sh <../files/job_vasp.sh>`.
These files are also available on Stallo:
------------------------------------------
.. code-block:: bash
module load VASP/5.4.1.plain-intel-2016a
cd <to whatever you call your test folder> # for instance testvasp
cp -R $RUNEX/* .
sbatch job_vasp.sh
and you are up running. Happy hunting.
software/applications/math-stat/README.md deleted 100755 → 0
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# Math and Statistics
{% include list.liquid all=true %}
software/applications/physics/COMSOL/COMSOL.rst deleted 100755 → 0
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.. _COMSOL:
======================================
The COMSOL Multiphysics software suite
======================================
Information regarding the computational physics program system COMSOL
Related information
-------------------
.. toctree::
:maxdepth: 1
:titlesonly:
firsttime_comsol.rst
General Information
===================
Description
-----------
COMSOL Multiphysics® is a general-purpose software platform, based on advanced numerical methods, for modeling and simulating physics-based problems. It is highly visual, based on graphical interfaces - so users are adviced to use either the client installation on their client machine or the remote graphics solution on Stallo.
Online info from vendor
-----------------------
* Homepage: https://www.comsol.com
* Documentation/support: https://www.comsol.com/support
License information
-------------------
The license of COMSOL is commercial/proprietary.
The installation of COMSOL on Stallo is for UiT users only. If you need/want access to COMSOL, you need to contact UiT support.
Citation
--------
When publishing results obtained with the referred software referred, please do check the developers web page in order to find the correct citat\
ion(s).
Additional online info about COMSOL on Stallo:
==============================================
Usage
-----
COMSOL is a rather memory intensive code, so we would generally advice users to either run their jobs on the highmem-nodes (with 8GB/core of memory) or less than full nodes on the slim nodes.
Use
.. code-block:: bash
$ module avail COMSOL
to see which versions of COMSOL are available. Use
.. code-block:: bash
$ module load COMSOL/<version> # i.e 5.3-intel-2016a
to get access to any given version of COMSOL.
The license of comsol is installed as a floating network license type, meaning that there is a server checking out license tokens upon request.
If you want to know whether there are available license tokens or not, load the module as above. Then type
.. code-block:: bash
$ lmstat -c $LMCOMSOL_LICENSE_FILE -a
Happy calculations!
software/applications/physics/COMSOL/README.md deleted 100755 → 0
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# The COMSOL Multiphysics software suite
{% include list.liquid all=true %}
software/applications/physics/COMSOL/firsttime_comsol.rst deleted 100755 → 0
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.. _first_time_comsol:
=================================
First time you run an COMSOL job?
=================================
This page contains info aimed at first time
users of COMSOL on Stallo, but may also be usefull to
more experienced users. Please look carefully through the
provided examples. Also note that the job-script example is rather richly
commented to provide additional and relevant info.
If you want to run this testjob, download the copies of the scripts and put them
into your test job folder (which I assume you have created in advance).
COMSOL input example
--------------------
The file used in this example can also downloaded here: :download:`Small test for COMSOL <../files/comsol_smalltest.mph>`.
Place this file in a job folder of choice, say COMSOLFIRSTJOB in your home directory on Stallo.
then, copy the job-script as seen here:
.. include:: ../files/run_comsol.sh
:literal:
(Can also be downloaded from here: :download:`run_comsol.sh <../files/run_comsol.sh>`)
Before you can submit a job, you need to know what "type" of study you want to perform (please read more about that on the vendor support page). For example purpose, I have chosed study 4 - named std04; and this is the second variable argument to the run script (see comments in script).
Place this script in the same folder and type:
.. code-block:: bash
sbatch run_comsol.sh comsol_smalltest std04
You may now have submitted your first COMSOL job.
Good luck with your (MULTI)physics!
software/applications/physics/README.md deleted 100755 → 0
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# Physics
{% include list.liquid all=true %}
software/applications/physics/files/job_StarCCM+.sh deleted 100755 → 0
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#!/bin/bash -l
###################################################
#
# Star CCM+ job example script
#
###################################################
#SBATCH --job-name=StarCCM+ex
# Running on default account, thus commented out the account giving comment:
##SBATCH --account=
# Stallo has 16 or 20 cores/node and therefore we take
# a number that is divisible by both:
#SBATCH --ntasks=80
# Run for 2 Hrs (format is day-hrs:mins:secs)
#SBATCH --time=0-02:00:00
# In slurm, memory is hardlimit. Be carefull!
#SBATCH --mem-per-cpu=1500MB
# Normal partition should do it
#SBATCH --partition=normal
##############
# Setting software environment for STAR CCM+
module purge --quiet
module load STAR-CCM+/STAR-CCM+12.02.010
##############
# Setting up real time license on demand.
# Users must specify their <licensekey>
#export LM_PROJECT=<licensekey>
export LM_PROJECT=KZBaPT+lhIWMrpIo2vgCyA
export CDLMD_LICENSE_FILE=1999@flex.cd-adapco.com
##############
# Setting up work environment and preparing for job to run:
# Specifying project name:
case=$SLURM_JOB_NAME
# Setting home for mpi:
export INTEL_MPI_HOME=$EBROOTIIMPI
# Moving to submit folder:
export submitdir=$SLURM_SUBMIT_DIR
cd $submitdir
# Definining, and creating if necessary, a temporary work directory:
export tempdir=/global/work/${USER}/starccm/${SLURM_JOBID}
mkdir -p $tempdir
# Copy inputfile and move to working directory
cp $case.sim $tempdir
cd $tempdir
# Defining number of mpi tasks to feed the mpi engine:
procs=$(echo $SLURM_NPROCS)
##############
# Starting the program:
starccm+ -bs SLURM -batch -power -mpi intel -pio -machinefile $SLURM_JOB_NODELIST -np $procs $case.sim
# Preferable options: -mesa.
# This should work:
#srun starccm+ -power -pio $case.sim
# Or this:
#starccm+ -bs SLURM -power -pio $case.sim
# Worst case scenario:
#starccm+ -bs SLURM -batch -power -mpi intel -pio -machinefile $SLURM_JOB_NODELIST -np $procs $case.sim
# Investigate potentially other files to keep:
echo $(pwd)
echo $(ls -ltr)
# ALWAYS clean up after yourself. Please do uncomment the following line
#cd $submitdir
#rm $tempdir/*
#rmdir $tempdir
echo "Job finished at"
date
################### Job Ended ###################
exit 0
software/applications/physics/files/run_comsol.sh deleted 100755 → 0
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#!/bin/bash -l
# Note that this is a setup for the highmem 128GB nodes. To run on the regular
# nodes, change to the normal partition and reduce the memory requirement.
#####################################
#SBATCH --job-name=comsol_runex
#SBATCH --nodes=2
#SBATCH --cpus-per-task=16 # Highmem nodes have 16 cores
#SBATCH --mem-per-cpu=7500MB # Leave some memory for the system to work
#SBATCH --time=0-01:00:00 # Syntax is DD-HH:MM:SS
#SBATCH --partition=highmem # For regular nodes, change to "normal"
#SBATCH --mail-type=ALL # Notify me at start and finish
#SBATCH --exclusive # Not necessary, implied by the specs above
##SBATCH --account=nnXXXXk # Change to your account and uncomment
#####################################
# define input
inp=$1 # First input argument: Name of input without extention
std=$2 # Second input argument: Type of study
ext=mph # We use the same naming scheme as the software default extention
# define directories
submitdir=${SLURM_SUBMIT_DIR}
workdir=/global/work/${USER}/${SLURM_JOBID}
# create work directory
mkdir -p ${workdir}
# move input to workdir
cp ${inp}.${ext} ${workdir}
# load necessary modules
module purge --quiet
module load COMSOL/5.3-intel-2016a
# run calculation in workdir
cd ${workdir}
time comsol -nn ${SLURM_NNODES}\
-np ${SLURM_CPUS_PER_TASK}\
-mpirsh /usr/bin/ssh\
-mpiroot $I_MPI_ROOT\
-mpi intel batch\
-inputfile ${inp}.${ext}\
-outputfile ${inp}_out.${ext}\
-study ${std}\
-mlroot /global/apps/matlab/R2014a
# move output back
mv *out* $submitdir
# cleanup
cd $submitdir
rm -r ${workdir}
exit 0
software/linux.md deleted 100755 → 0
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# Linux command line
## New on Linux systems?
This page contains some tips on how to get started using the Star
cluster if you are not too familiar with Linux/Unix. The
information is intended for both users that are new to Star and for
users that are new to Linux/UNIX-like operating systems. Please consult
the rest of the user guide for information that is not covered in this
chapter.
For details about the hardware and the operating system of Star, and
basic explanation of Linux clusters please see the `about_star` part
of this documentation.
To find out more about the storage and file systems on the machines,
your disk quota, how to manage data, and how to transfer file to/from
Star, please read the `file_transfer` section.
### ssh
The only way to connect to Star is by using ssh. Check the `login` in
this documentation to learn more about ssh.
### scp
The machines are stand-alone systems. The machines do not (NFS-)mount
remote disks. Therefore you must explicitly transfer any files you wish
to use to the machine by scp. For more info, see `file_transfer`.
### Running jobs on the machine
You must execute your jobs by submitting them to the batch system. There
is a dedicated section `/jobs/batch` in our user guide that explain how
to use the batch system. The pages explains how to write job scripts,
and how to submit, manage, and monitor jobs. It is not allowed to run
long or large memory jobs interactively (i.e., directly from the command
line).
### Common commands
pwd
Provides the full pathname of the directory you are currently in.
cd
Change the current working directory.
ls
List the files and directories which are located in the directory you
are currently in.
find
Searches through one or more directory trees of a file system, locates
files based on some user-specified criteria and applies a user-specified
action on each matched file. e.g.:
find . -name 'my*' -type f
The above command searches in the current directory (.) and below it,
for files and directories with names starting with my. "-type f" limits
the results of the above search to only regular files, therefore
excluding directories, special files, pipes, symbolic links, etc. my\*
is enclosed in single quotes (apostrophes) as otherwise the shell would
replace it with the list of files in the current directory starting with
"my".
grep
Find a certain expression in one or more files, e.g:
grep apple fruitlist.txt
mkdir
Create new directory.
rm
Remove a file. Use with caution.
rmdir
Remove a directory. Use with caution.
mv
Move or rename a file or directory.
vi/vim or emacs
Edit text files, see below.
less/ more
View (but do not change) the contents of a text file one screen at a
time, or, when combined with other commands (see below) view the result
of the command one screen at a time. Useful if a command prints several
screens of information on your screen so quickly, that you don't manage
to read the first lines before they are gone.
\|
Called "pipe" or "vertical bar" in English. Group 2 or more commands
together, e.g.:
ls -l | grep key | less
This will list files in the current directory (ls), retain only the
lines of *ls* output containing the string "key" (grep), and view the
result in a scrolling page (less).
### More info on manual pages
If you know the UNIX-command that you would like to use but not the
exact syntax, consult the manual pages on the system to get a brief
overview. Use 'man \[command\]' for this. For example, to get the right
options to display the contents of a directory, use 'man ls'. To choose
the desired options for showing the current status of processes, use
'man ps'.
### Text editing
Popular tools for editing files on Linux/UNIX-based systems are 'vi' and
'emacs'. Unfortunately the commands within both editors are quite
cryptic for beginners. It is probably wise to spend some time
understanding the basic editing commands before starting to program the
machine.
vi/vim
Full-screen editor. Use 'man vi' for quick help.
emacs
Comes by default with its own window. Type 'emacs -nw' to invoke emacs
in the active window. Type 'Control-h i' or follow the menu
'Help-\>manuals-\>browse-manuals-with-info' for help. 'Control-h t'
gives a tutorial for beginners.
### Environment variables
The following variables are automatically available after you log in:
$USER your account name
$HOME your home directory
$PWD your current directory
You can use these variables on the command line or in shell scripts by
typing $USER, $HOME, etc. For instance: 'echo $USER'. A complete listing
of the defined variables and their meanings can be obtained by typing
'printenv '.
You can define (and redefine) your own variables by typing:
export VARIABLE=VALUE
### Aliases
If you frequently use a command that is long and has for example many
options to it, you can put an alias (abbreviation) for it in your
`~/.bashrc` file. For example, if you normally prefer a long listing of
the contents of a directory with the command 'ls -laF \| more', you can
put following line in your `~/.bashrc` file:
alias ll='ls -laF | more'
You must run 'source \~/.bashrc' to update your environment and to make
the alias effective, or log out and in :-). From then on, the command
'll' is equivalent to 'ls -laF \| more'. Make sure that the chosen
abbreviation is not already an existing command, otherwise you may get
unexpected (and unwanted) behavior. You can check the existence and
location of a program, script, or alias by typing:
which [command]
whereis [command]
### \~/bin
If you frequently use a self-made or self-installed program or script
that you use in many different directories, you can create a directory
\~/bin in which you put this program/script. If that directory does not
already exist, you can do the following. Suppose your favorite little
program is called 'myscript' and is in your home ($HOME) directory:
mkdir -p $HOME/bin
cp myscript $HOME/bin
export PATH=$PATH:$HOME/bin
PATH is a colon-separated list of directories that are searched in the
order in which they are specified whenever you type a command. The first
occurrence of a file (executable) in a directory in this PATH variable
that has the same name as the command will be executed (if possible). In
the example above, the 'export' command adds the \~/bin directory to the
PATH variable and any executable program/script you put in the \~/bin
directory will be recognized as a command. To add the \~/bin directory
permanently to your PATH variable, add the above 'export' command to
your \~/.bashrc file and update your environment with 'source
\~/.bashrc'.
Make sure that the names of the programs/scripts are not already
existing commands, otherwise you may get unexpected (and unwanted)
behaviour. You can check the contents of the PATH variable by typing:
printenv PATH
echo $PATH
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# Python, R, Matlab and Perl
Scripting languages often support modules or libraries for additional
functionality or convenience functions. We encourage users to install
modules locally for only the current user.
## Python
You can install many python and non-python packages yourself using
[conda](https://docs.conda.io/en/latest/) or especially for
bioinformatics software [bioconda](https://bioconda.github.io/).
Conda enables you to easily install complex packages and software.
Creating multiple enviroments enables you to have installations of the
same software in different versions or incompatible software collections
at once. You can easily share a list of the installed packages with
collaborators or colleagues, so they can setup the same eniviroment in a
matter of minutes.
### Setup
First you load the miniconda module which is like a python and r package
manager. Conda makes it easy to have multiple environments for example
one python2 and one python3 based parallel to each other without
interfering.
Start by removing all preloaded modules which can complicate things. We
then display all installed version and load the newest one (4.6.14):
ml purge
ml avail Miniconda
ml Miniconda3/4.6.14
To install packages we first have to add the package repository to conda
(we only have to do this once) and set up a new conda environment which
we will call e.g. "python3" where we also specify which python version
we want and which packages should be installed (matplotlib numpy):
conda config --add channels defaults
conda config --add channels conda-forge
conda create --name python3 python=3 matplotlib numpy
If you want install bioinformatics packages you should also add the
bioconda channel:
conda config --add channels bioconda
In case you want to install the conda environment in another directory
than the home, you can add <span class="title-ref">--prefix PATH</span>.
This enables multiple users of a project to share the conda environment
by installing it into their project folder instead of the users home.
To suppress the warning that a newer version of conda exists which is
usually not important for most users and will be fixed by us by
installing a new module:
conda config --set notify_outdated_conda false
### Daily usage
To load this environment you have to use the following commands either
on the command line or in your job script:
ml purge
ml Miniconda3/4.6.14
conda activate python3
Then you can use all software as usual.
To deactivate the current environment:
conda deactivate
If you need to install additional software or packages, we can search
for it with:
conda search SOMESOFTWARE
and install it with:
conda install -n python3 SOMESOFTWARE
If the python package you are looking for is not available in conda you
can use [\*\*pip\*\*](https://pip.pypa.io/en/stable/) like usually from
within a conda environment to install additional python packages:
pip install SOMEPACKAGE
To update the a single package with conda:
conda update -n python3 SOMESOFTWARE
or to update all packages:
conda update -n python3 --all
### Share your environment
To export a list of all packages/programs installed with conda in a
certain environment (in this case "python3"):
conda list --explicit --name python3 > package-list.txt
To setup a new environment (let's call it "newpython") from ab exported
package list:
conda create --name newpython --file package-list.txt
### Additional Conda information
#### Cheatsheet and built-in help
See this
[cheatsheet](https://docs.conda.io/projects/conda/en/4.6.0/_downloads/52a95608c49671267e40c689e0bc00ca/conda-cheatsheet.pdf)
for an overview over the most important conda commands.
In case you get confused by the conda commands and command line options
you can get help by adding <span class="title-ref">--help</span> to any
conda command or have a look at the [conda
documentation](https://conda.io/projects/conda/en/latest/user-guide/getting-started.html).
#### Miniconda vs. Anaconda
Both Miniconda and Anaconda are distributions of the conda repository
managment system. But while Miniconda brings just the managment system
(the conda command), Anaconda comes with a lot of built-in packages.
Both are installed on Star but we advise the use of Miniconda. By
explicitly installing packages into your own enviroment the chance for
unwanted effects and errors due to wrong or incomaptible versions is
reduced. Also you can be sure that everything that happens with your
setup is controlled by yourself.
### Virtual Environments (deprecated)
We recommend using Conda , but there is another way to install modules
locally is using [virtual
environments](https://docs.python.org/3/tutorial/venv.html)
As an example we install the Biopython package (and here we use the
`Python/3.6.4-intel-2018a` module as an example):
$ module load Python/3.6.4-intel-2018a
$ virtualenv venv
$ source venv/bin/activate
$ pip install biopython
Next time you log into the machine you have to activate the virtual
environment:
$ source venv/bin/activate
If you want to leave the virtual environment again, type:
$ deactivate
And you do not have to call it "venv". It is no problem to have many
virtual environments in your home directory. Each will start as a clean
Python setup which you then can modify. This is also a great system to
have different versions of the same module installed side by side.
If you want to inherit system site packages into your virtual
environment, do this instead:
$ virtualenv --system-site-packages venv
$ source venv/bin/activate
$ pip install biopython
## R
### Load R
Using R on Star is quite straightforward. First check which versions
are available:
ml avail -r '^R/'
To load a version:
ml R/3.5.0-iomkl-2018a-X11-20180131
Now you can use R from the command line just as you would on your local
computer.
### Install Packages
To install R packages use `install.packages()`. First open the R command
line and then install a package e.g. "tidyverse":
R
install.packages("tidyverse")
Note: The first time you install new packages, R will ask you whether it
should install these packages into your home folder. Confirm both
questions with `y` and then choose a close download mirror
## MATLAB
### Load MATLAB
To use MATLAB simply load the module at the start of your jobscript or
type them on the command line:
ml purge
ml avail matlab # To display all installed versions
ml MATLAB/R2018a-foss-2017a # or any other version you want
### Interactice Shell
On the login nodes you can start a normal MATLAB session with an
graphical user interface (GUI). You can use this to visualize and look
at data. Just type `matlab`.
But remember NOT to run calculations on the login nodes as this might
slow down the system for all star users. If this happens we will kill
the process without prior warning.
You can also start an interactive matlab shell on the command line
without graphical user interface (headless) with:
matlab -nodesktop -nodisplay -nosplash
See `matlab -h` for all command line options. If you are on a compute
node `matlab` always starts a headless matlab shell.
### Running MATLAB Scripts
You can run a matlab script by:
matlab -r -nodisplay -nosplash -r 'run("SCRIPT.m")'
In some instances it might be necessary to use an absolute file path to
the script.
### Tips
- You can reduce the memory usage by starting matlab without java
support, just add `-nojvm`.
- To get a graphical interface when starting `matlab` on a login node,
you need to activate X11 forwarding for your ssh connection to
star. If you connect to star from a linux machine use `ssh -X`
to tunnel graphical output to your computer.
## Perl
We will use Perl 5.28 and use the standard paths. This follows the
general instruction given here: <https://metacpan.org/pod/local>::lib.
$ module load Perl/5.28.0-GCCcore-7.3.0
$ mkdir my_perl_installs # or however you want to call this temporary folder
$ cd my_perl_installs
# Check the newest version on metacpan.org and search for local::lib
$ wget https://cpan.metacpan.org/authors/id/H/HA/HAARG/local-lib-2.000024.tar.gz
$ tar xzf local-lib-2.000024.tar.gz
$ cd local-lib-2.000024
$ perl Makefile.PL --bootstrap
$ make test
$ make install
$ echo 'eval "$(perl -I$HOME/perl5/lib/perl5 -Mlocal::lib)"' >> ~/.bashrc
$ source ~/.bashrc
Now, the module local::lib is installed and the `~/.bashrc` changed such
that Perl should now recognize your local folder as module folder. All
future modules will be installed to `~/perl5`.
If you want to install, for example, the module Math::Vector::Real, just
call cpan:
$ cpan Math::Vector::Real
Remember to load the right Perl version first (`module load ...`). The
first time you call cpan, it will ask you to do some configurations.
Just press enter (let it do its configurations).
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