The following table shows SLURM commands on the SOE cluster.

For more information, run man on the commands above. See some examples below.

1. Info about the partitions and nodes
List all the partitions available to you and the nodes therein:

sinfo

Nodes in state idle can accept new jobs.

Show a partition configuratuin, for example, SOE_main

scontrol show partition=SOE_main

Show current info about a specific node:

scontrol show node=<nodename>

You can also specify a group of nodes in the command above. For example, if your MPI job is running across soenode05,06,35,36, you can execute the command below to get the info on the nodes you are interested in:

scontrol show node=soenode[05-06,35-36]

An informative parameter in the output to look at would be CPULoad. It allows you to see how your application utilizes the CPUs on the running nodes.

2. Submit scripts
The header in a submit script specifies job name, partition (queue), time limit, memory allocation, number of nodes, number of cores, and files to collect standard output and error at run time, for example

#!/bin/bash

#SBATCH --job-name=OMP_run     # job name, "OMP_run"
#SBATCH --partition=SOE_main   # partition (queue)
#SBATCH -t 0-2:00              # time limit: (D-HH:MM) 
#SBATCH --mem=32000            # memory per node in MB 
#SBATCH --nodes=1              # number of nodes
#SBATCH --ntasks-per-node=16   # number of cores
#SBATCH --output=slurm.out     # file to collect standard output
#SBATCH --error=slurm.err      # file to collect standard errors

If the time limit is not specified in the submit script, SLURM will assign the default run time, 3 days. This means the job will be terminated by SLURM in 72 hrs. The maximum allowed run time is two weeks, 14-0:00.
If the memory limit is not requested, SLURM will assign the default 16 GB. The maximum allowed memory per node is 128 GB. To see how much RAM per node your job is using, you can run commands sacct or sstat to query MaxRSS for the job on the node - see examples below.
Depending on a type of application you need to run, the submit script may contain commands to create a temporary space on a computational node - see the discussion about using the file systems on the cluster.
Then it sets the environment specific to the application and starts the application on one or multiple nodes - see sbatch sample scripts in directory /usr/local/Samples on soemaster1.hpc.rutgers.edu.
You can submit your job to the cluster with sbatch command:

sbatch myscript.sh

3. Query job information
List all currently submitted jobs in running and pending states for a user:

squeue -u <username>

Command squeue can be run with format options to expose specific information, for example, when pending job #706 is scheduled to start running:

squeue -j 706 --format="%S"

START_TIME
2015-04-30T09:54:32

More info can be shown by placing additional format options, for example:

squeue -j 706 --format="%i %P %j %u %T %l %C %S"

JOBID PARTITION   NAME    USER STATE   TIMELIMIT  CPUS START_TIME
706   SOE_main  Par_job_3 mike PENDING 3-00:00:00 64   2015-04-30T09:54:32

To see when all the jobs, pending in the queue, are scheduled to start:

squeue --start 

List all running and completed jobs for a user

sqlog -u <username>

or

sqlog -j <JobID>

The following appreviations are used for the job states:

       CA   CANCELLED      Job was cancelled.

       CD   COMPLETED      Job completed normally.

       CG   COMPLETING     Job is in the process of completing.

       F    FAILED         Job termined abnormally.

       NF   NODE_FAIL      Job terminated due to node failure.

       PD   PENDING        Job is pending allocation.

       R    RUNNING        Job currently has an allocation.

       S    SUSPENDED      Job is suspended.

       TO   TIMEOUT        Job terminated upon reaching its time limit.

You can specify the fields you would like to see in the output of sqlog:

sqlog --format=list

The command below, for example, provides Job ID, user name, exit state, start date-time, and end date-time for job #2831:

sqlog -j 2831 --format=jid,user,state,start,end

List status info for a currently running job:

sstat -j <jobid>

A formatted output can be used to gain only a specific info, for example, the maximum resident RAM usage on a node:

sstat --format="JobID,MaxRSS" -j <jobid>

To get statistics on completed jobs by jobID:

sacct --format="JobID,JobName,MaxRSS,Elapsed" -j <jobid>

To view the same information for all jobs of a user:

sacct --format="JobID,JobName,MaxRSS,Elapsed" -u <username>

To print a list of fields that can be specified with the --format option:

sacct --helpformat

For example, to get Job ID, Job name, Exit state, start date-time, and end date-time for job #2831:

sacct -j 2831 --format="JobID,JobName,State,Start,End"

Another useful command to gain information about a running job is scontrol:

scontrol show job=<jobid>

4. Cancel a job
To cancel one job:

scancel <jobid>

To cancel one job and delete the TMP directory created by the submit script on a node:

sdel <jobid>

To cancel all the jobs for a user:

scancel -u <username>

To cancel one or more jobs by name:

scancel --name <myJobName>

1. Learn and get updated

Some of the bad biological programmers only learn new technical or non-technical things when it’s absolutely necessary. The good biological programmers learn new technical skills proactively. But great biological programmers not only learn new technical skills on their own but also learn non-technical skills, and have an open mind to sources of knowledge that others may shut out.

In other concrete term, the bad biological programmer learn Perl's regular expression when they started a project on comparative genomics; the good biological programmer learned it a year before because it looked interesting; and the great biological programmer also read about the BioPerl packages, genomics, DNA string, genomic theories, or some similar course of study so that they could understand the results and explain it biologically.

2. Not a merely coder!!!

I often encountered with biological programmer who call themself a hard-core computer programmer and avoid biology. I can almost guarantee that if you are one of them then you are not doing research but merely writing "dry" codes.

According to my supervisor most of the computational biologist, don't know what they are doing biologically. Even they struggle to explain their own programs output and results. Therefore, It is highly advisable to learn basic of biology which can assist you to explain the result and understand your discovery. Always remember you are a researcher not a coder.

3. Be Social with biologist

The computational biologist spends most of the time in from of computers, writing codes. They always think their job is to produce working codes, not technical research perfections. But, they are completely wrong. You should not forget that apart from your computational skills you also need some biologist, other than your supervisor, to explain and make you understand the complex biological mechanism.

I highly recommend your to interact with biotech researchers and learn how do they explain their one graph (which they generally produce after one year of work) biologically. Remember, the origin of your research project is complex biological phenomenon, which is more complex than that of your limited programming rules.

4. Do not search, research for answers

Researching for answers means more than typing several keywords into a search engine or posting a question at Stack Overflow or the BioStars forums. I have entered problems into search engines that generate no results, and every question I posted on Stack Overflow or the BioStars forums never got anything resembling an answer, yet I solved the issues and moved on. I’m not a magician — I just know how to find answers or discover root causes.

Many problems are situational, and if you depend on search engines and forums, you can waste a lot of time going down a rabbit hole and possibly never getting a solution. Learn to perform root cause analysis, learn enough about the underlying system to look for other clues and solutions, and learn to take a long distance view of an issue before deep diving into it.

5. Love and defend your research

You cannot rise to the top in this research profession without loving your work. There are some very good “it’s just a job” biological programmers (I’ve been one at times), but if that is your outlook, you won’t be willing to do whatever it takes to succeed. This idea gets a lot of folks in a huff, because they feel it is a personal insult. “I’m a good programmer, but I have other priorities and can’t make work my life.” I understand completely; I have other priorities too. As much as I hate to say it, when I am passionate about my work, I am willing (though not eager) to abandon my other priorities to finish the job. It is not an insult to say that if you aren’t willing to pull out all the stops you can’t be the best, it is a fact.

You must be passionate about more than programming — you must also be excited about your research, the tools and technology you are using, and so on. I have seen very good and even great biological programmers operating at mediocre levels because something was not a good fit, such as they hated the project or were using a technology they disliked. Therefore, like your research project and get excited about your discoveries. You have not only to discover but also defend your finding with scientific words.

Thanks to all of you for reading.

#!/usr/bin/perl

APPEAL:

listen (please, please);

open yourself, wide;
join (you, me),
connect (us,together),

tell me.

do something if distressed;

@dawn, dance;
@evening, sing;
read (books,$poems,stories) until peaceful;
study if able;

write me if-you-please;

sort your feelings, reset goals, seek (friends, family, anyone);

do*not*die (like this)
if sin abounds;

keys (hidden), open (locks, doors), tell secrets;
do not, I-beg-you, close them, yet.

accept (yourself, changes),
bind (grief, despair);

require truth, goodness if-you-will, each moment;

select (always), length(of-days)

# listen (a perl poem)
# Sharon Hopkins
# rev. June 19, 1995

For example ... contig-stats.pl is a Perl script that will automatically describe features of a sequence assembly.

http://milkweedgenome.org/?q=scripts

Address of the bookmark: http://milkweedgenome.org/?q=scripts

#Find the reverse complement of a DNA string.
#Given: A DNA string Pattern.
#Return: Pattern, the reverse complement of Pattern.

use strict;
use warnings;

my $string="AAAACCCGGT";
my $finalString="";
my %hash = (
    "C" => "G",
    "A" => "T",
    "T" => "A",
    "G" => "C",
);

for (my $aa=0; $aa<=(length($string)-1); $aa++) {
    my $char=substr $string, $aa, 1;
    #print $hash{$char};
    $finalString="$hash{$char}"."$finalString";
}

print $finalString;
print "\n";

Script are moved to http://bioinformaticsonline.com/snippets/view/34633/clump-finding-problem-solved-with-perl

CORE MODULES

Three modules make up the core engine for MCE.

MCE::Core

Provides the Core API for Many-Core Engine. The various MCE options are described here.

MCE::Signal

Temporary directory creation, cleanup, and signal handling.

MCE::Util

Utility functions for Many-Core Engine.

MCE EXTRAS

There are 4 add-on modules for use with MCE.

MCE::Candy

Provides a collection of sugar methods and output iterators for preserving output order.

MCE::Mutex

Provides a simple semaphore implementation supporting threads and processes.

MCE::Queue

Provides a hybrid queuing implementation for MCE supporting normal queues and priority queues from a single module. MCE::Queue exchanges data via the core engine to enable queuing to work for both children (spawned from fork) and threads.

MCE::Relay

Enables workers to receive and pass on information orderly with zero involvement by the manager process while running.

MCE MODELS

The models take Many-Core Engine to a new level for ease of use. Two options (chunk_size and max_workers) are configured automatically as well as spawning and shutdown.

MCE::Loop

Provides a parallel loop utilizing MCE for building creative loops.

MCE::Flow

A parallel flow model for building creative applications. This makes use of user_tasks in MCE. The author has full control when utilizing this model. MCE::Flow is similar to MCE::Loop, but allows for multiple code blocks to run in parallel with a slight change to syntax.

MCE::Grep

Provides a parallel grep implementation similar to the native grep function.

MCE::Map

Provides a parallel map model similar to the native map function.

MCE::Step

Provides a parallel step implementation utilizing MCE::Queue between user tasks. MCE::Step is a spin off from MCE::Flow with a touch of MCE::Stream. This model, introduced in 1.506, allows one to pass data from one sub-task into the next transparently.

MCE::Stream

Provides an efficient parallel implementation for chaining multiple maps and greps together through user_tasks and MCE::Queue. Like with MCE::Flow, MCE::Stream can run multiple code blocks in parallel with a slight change to syntax from MCE::Map and MCE::Grep.

MISCELLANEOUS

Miscellaneous additions included with the distribution.

MCE::Examples

Describes various demonstrations for MCE including a Monte Carlo simulation.

MCE::Subs

Exports functions mapped directly to MCE methods; e.g. mce_wid. The module allows 3 options; :manager, :worker, and :getter.

REQUIREMENTS

Perl 5.8.0 or later. PDL::IO::Storable is required in scripts running PDL.

SOURCE AND FURTHER READING

The source, cookbook, and examples are hosted at GitHub.

• https://github.com/marioroy/mce-perl

• https://github.com/marioroy/mce-cookbook

• https://github.com/marioroy/mce-examples

SEE ALSO

MCE::Shared provides data sharing capabilities for MCE. It includes MCE::Hobo for running code asynchronously.

• MCE::Shared

• MCE::Hobo

Address of the bookmark: https://github.com/marioroy/mce-examples

http://search.cpan.org/~lds/Bio-Graphics-2.37/

Address of the bookmark: http://search.cpan.org/~lds/Bio-Graphics-2.37/

Address of the bookmark: https://saliweb.readthedocs.io/en/latest/frontend.html