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Clusters Clusters

Compute cluster overview

When you SSH into stampede2 ls6, your session is assigned to one of a small set of login nodes (also called head nodes). These are not separate from the cluster compute nodes that will run your jobs.

Think of a node as a computer, like your laptop, but probably with more cores and memory. Now multiply that computer a thousand or more, and you have a cluster.

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The small set of login nodes are a shared resource (type the users command to see everyone currently logged in) and are not meant for running interactive programs – for that you submit a description of what you want done to a batch system, which farms distributes the work out to one or more compute nodes.

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Here is a comparison of the configurations and ls6 and stampede2. As you can see, stampede2 is the larger cluster, launched in 2017, but ls6, launched this yearom 2022, has fewer but more powerful nodes.

Note the use of the term virtual core above on stampede2. Compute cores are standalone processors – mini CPUs, each of which can execute separate sets of instructions. However modern cores may also have hyper-threading enabled, where a single core can appear as more than one virtual processor to the operating system (see https://en.wikipedia.org/wiki/Hyper-threading for more on hyper-threading). For example, stampede2 nodes have 2 or 4 hyperthreads (HTs) per core. So KNL nodes with 4 HTs for each of the 68 physical cores, have a total of 272 virtual cores.

User guides for ls6 and stampede2 can be found at:

• https://portal.tacc.utexas.edu/user-guides/lonestar6
• https://portal.tacc.utexas.edu/user-guides/stampede2

User guides for ls6 and stampede2 can be found at:

• https://portal.tacc.utexas.edu/user-guides/lonestar6
• https://portal.tacc.utexas.edu/user-guides/stampede2

Unfortunately, the TACC user guides Unfortunately, the TACC user guides are aimed towards a different user community – the weather modelers and aerodynamic flow simulators who need very fast matrix manipulation and other high performance computing High Performance Computing (HPC) features. The usage patterns for bioinformatics – generally running 3rd party tools on many different datasets – datasets – is rather a special case for HPC. TACC calls our type of processing "parameter sweep jobs" and has a special process for running them, using their launcher modulehas a special process for running them, using their launcher module.

About cores and hyperthreads

Note the use of the term virtual core on stampede2. Compute cores are standalone processors – mini CPUs, each of which can execute separate sets of instructions. However modern cores may also have hyperthreading enabled, where a single core can appear as more than one virtual processor to the operating system (see https://en.wikipedia.org/wiki/Hyper-threading). For example, stampede2 nodes have 2 or 4 hyperthreads (HTs) per core. So KNL nodes with 4 HTs for each of the 68 physical cores, have a total of 272 virtual cores.

Threading is an operating system scheduling mechanism for allowing one CPU/core to execute multiple computations, seemingly in parallel.

The writer of a program that takes advantage of threading first identifies portions of code that can run in parallel because the computations are independent. The programmer assigns some number of threads to that work (usually based on a command-line option) using specific thread and synchronization programming language constructs. An example is the the samtools sort -@ N option to specify N threads can be used for sorting independent sets of the input alignments.

If there are multiple cores/CPUs available, the operating system can assign a program thread to each of them for actual parallelism. But only "seeming" (or virtual) parallelism occurs if there are fewer cores than the number of threads specified.

Suppose there's only one core/CPU. The OS assigns program thread A to the core to run until the program performs an I/O operation that causes it to be "suspended" for the I/O operation to complete. During this time, when normally the CPU would be doing nothing but waiting on the I/O to complete, the OS assigns program thread B to the CPU and lets it do some work. This threading allows more efficient use of existing cores as long as the multiple program threads being assigned do some amount of I/O or other operations that cause them to suspend. But trying to run multiple compute-only, no-I/O programs using multiple threads on one CPU just causes "thread thrashing" -- OS scheduler overhead when threads are suspended for time, not just I/O.

The analogy is a grocery store where there are 5 customers (threads). If there are 5 checkout lines (cores), each customer (thread) can be serviced in a separate checkout line (core). But if there's only one checkout line (core) open, the customers (threads) will have to wait in line. To be a more accurate analogy, any checkout clerk would be able to handle some part of checkout for each customer, then while waiting for the customer to find and enter credit card information, the clerk could handle a part of a different customer's checkout.

Hyperthreading is just a hardware implementation of OS scheduling. Each CPU offers some number of "virtual cores" (hyperthreads) that can "almost" act like separate cores using various hardware tricks. Still, if the work assigned to multiple hyperthreads on a single core does not pause from time to time, thread thrashing will occur.

Software at TACC

Programs and your $PATH

When you type in the name of an arbitrary program (ls for example), how does the shell know where to find that program? The answer is your $PATH. $PATH is a pre-defined predefined environment variable whose value is a list of directories.The shell looks for program names in that list, in the order the directories appear.

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For example, the following module load command makes the fastqc FASTQ file quality checking program singularity container management system available to you:

# first type "matlabsingularity" to show that it is not present in your environment:
matlabsingularity
# it's not on your $PATH either:
which matlabsingularity

# now add biocontainers matlabtoto your environment and try again:
module load matlabbiocontainers
# and see how singularity it'sis now on your $PATH:
which matlabsingularity
# you can see the new directory at the front of $PATH
echo $PATH

# to remove it, use "unload"
module unload matlabbiocontainers
matlabsingularity
# gone from $PATH again...
which matlabsingularity

TACC BioContainers modules

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TACC obtains its containers from BioContainers (https://biocontainers.pro/ and https://github.com/BioContainers/containers), a large public repository of bioinformatics tool Singularity containers. This has allowed TACC to easily provision thousands of such tools!

These BioContainers are not visible in TACC's "standard" module system, but only after the master biocontainers module is loaded. Once it has been loaded, you can search for your favorite bioinformatics program using module spider.

# Verify that samtools is not available
samtools
# and cannot be found in the standard module system
module spider samtools

# Load the BiocontainersBioContainers master module (this takes a while)
module load biocontainers

# Now look for these programs
module spider samtools
module spider Rstats
module spider kallisto
module spider bowtie2
module spider minimap2
module spider multiqc
module spider GATKgatk
module spider velvet

Notice how the BioContainers module names have "ctr" in their names, version numbers, and other identifying information.

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For one thing, remember that your $HOME directory quota is fairly small (10 GB on ls6), and that can fill up quickly if you install many programs. We recommend creating an installation area in your $WORK directory and installing programs there. You can then make symbolic links to the binaries you need in your $HOME~/local/bin directory (which was added to your $PATH in your .bashrc).

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ls -l ~/local/bin

# this will tell you the real location of the launcher_creator.py

 script is
# /work/projects/BioITeam/common/bin/launcher_creator.py

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Job Execution

Job execution is controlled by the SLURM batch system on both stampede2 and ls6.

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The process of running the job involves these steps:

1. Create a commands file containing exactly one task per line.
2. Prepare a job control file for the commands file that describes how the job should be run.
3. You submit the job control file to the batch system. The job is then said to be queued to run.
4. The batch system prioritizes the job based on the number of compute nodes needed and the job run time requested.
5. When compute nodes become available, the job tasks (command lines in the <job_name>.cmds file) are assigned to one or more compute nodes and begin to run in parallel.
6. The job completes when either:
   1. you cancel the job manually
   2. all job tasks in the job complete (successfully or not!)
   3. the requested job run time has expired

SLURM at a glance

Here are the main components of the SLURM batch system.

Simple example

Let's go through a simple example. Execute the following commands to copy a pre-made simple.cmds commands file:

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What are the tasks we want to do? Each task corresponds to one line in the simple.cmds commands file, so let's take a look at it using the cat (concatenate) command that simply reads a file and writes each line of content to standard output (here, your Terminal):

cat simple.cmds

The tasks we want to perform look like this:

sleep 5; echo "Command 1 on `hostname` - `date`" > cmd1.log 2>&1
sleep 5; echo "Command 2 on `hostname` - `date`" > cmd2.log 2>&1
sleep 5; echo "Command 3 on `hostname` - `date`" > cmd3.log 2>&1
sleep 5; echo "Command 4 on `hostname` - `date`" > cmd4.log 2>&1
sleep 5; echo "Command 5 on `hostname` - `date`" > cmd5.log 2>&1
sleep 5; echo "Command 6 on `hostname` - `date`" > cmd6.log 2>&1
sleep 5; echo "Command 7 on `hostname` - `date`" > cmd7.log 2>&1
sleep 5; echo "Command 8 on `hostname` - `date`" > cmd8.log 2>&1

There are 8 tasks. Each is a simple echo command that just outputs task sleeps for 5 seconds, then uses the echo command to output a string containing the task number and date to a different log file after sleeping for 5 secondsnamed for the task number. Notice that we can put two commands on one line if they are separated by a semicolon ( ; ).

Use the handy launcher_creator.py program to create the job submission scriptcontrol file.

launcher_creator.py -j simple.cmds -n simple -t 00:01:00 -a OTH21164 -q normaldevelopment

You should see output something like the following, and you should see a simple.slurm batch submission file in the current directory.

Project simple.
Using job file simple.cmds.
Using normaldevelopment queue.
For 00:01:00 time.
Using OTH21164 allocation.
Not sending start/stop email.
Launcher successfully created. Type "sbatch simple.slurm" to queue your job.

Submit your batch job like this, then check the batch queue to see the job's status.

sbatch simple.slurm --reservation CoreNGS_Day1 
showq -u

# Output looks something like this:
-------------------------------------------------------------
          Welcome to the Lonestar6 Supercomputer
-------------------------------------------------------------

No reservation for this job
--> Verifying valid submit host (login1)...OK
--> Verifying valid jobname...OK
--> Verifying valid ssh keys...OK
--> Verifying access to desired queue (normal)...OK
--> Checking available allocation (OTH21164)...OK
Submitted batch job 232542

The queue status will show your job as ACTIVE while its running, or WAITING if not.

SUMMARY OF JOBS FOR USER: <abattenh>

ACTIVE JOBS--------------------
JOBID     JOBNAME    USERNAME      STATE   NODES REMAINING STARTTIME
================================================================================
232542924965    simple     abattenh      Running 1      0:00:5442  ThuSat Jun  93 1121:3033:1831

WAITING JOBS------------------------
JOBID     JOBNAME    USERNAME      STATE   NODES WCLIMIT   QUEUETIME
================================================================================

Total Jobs: 1     Active Jobs: 1     Idle Jobs: 0     Blocked Jobs: 0

If you don't see your simple job in either the ACTIVE or WAITING sections of your queue, it probably already finished – it should only run for a few seconds!

Notice in my queue status, where the STATE is Running, there is only one node assigned. Why is this, since there were 8 tasks?

Every job, no matter how few tasks requested, will be assigned at least one node. Each tlonestar6 lonestar6 node has 128 physical cores, so each of the 8 tasks can be assigned to a different core.

Exercise: What files were created by your job?

cmd1.log  cmd3.log  cmd5.log  cmd7.log  simple.cmds      simple.o2916562o924965 
cmd2.log  cmd4.log  cmd6.log  cmd8.log  simple.e2916562e924965  simple simple.slurm

filename wildcarding

You can look at one of the output log files like this:

cat cmd1.log

But here's a cute trick for viewing the contents all your output files at once, using the cat command and filename wildcarding.

cat cmd*.log

The cat command actually takes can take a list of one or more files (if you're giving it files rather than standard input – more on this shortly) and outputs the concatenation of them to standard output. The asterisk ( * ) in cmd*.log is a multi-character wildcard that matches any filename starting with cmd then ending with .log. So it would match cmd_hello_worldwith cmd then ending with .log.

You can also specify single-character matches inside brackets ( [ ] ) in either of the ways below, this time using the ls command so you can better see what is matching:

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This technique is sometimes called filename globbing, and the pattern a glob. Don't ask me why – it's a Unix thing. Globbing – translating a glob pattern into a list of files – is one of the handy thing the bash shell does for you. (Read more about Wildcards and special filenames. Pathname wildcards)

Exercise: How would you list all files starting with "simple"?

Here's what my cat output looks like. Notice the times are all nearly the same because all the tasks ran in parallel. That's the power of cluster computing!

Command 1 on c305c304-005.ls6.tacc.utexas.edu - ThuSat Jun  93 1121:3033:3950 CDT 20222023
Command 2 on c305c304-005.ls6.tacc.utexas.edu - ThuSat Jun  93 1121:3033:3344 CDT 20222023
Command 3 on c305c304-005.ls6.tacc.utexas.edu - ThuSat Jun  93 1121:3033:3346 CDT 20222023
Command 4 on c305c304-005.ls6.tacc.utexas.edu - ThuSat Jun  93 1121:3033:3847 CDT 20222023
Command 5 on c305c304-005.ls6.tacc.utexas.edu - ThuSat Jun  93 1121:3033:4051 CDT 20222023
Command 6 on c305c304-005.ls6.tacc.utexas.edu - ThuSat Jun  93 1121:3033:3847 CDT 20222023
Command 7 on c305c304-005.ls6.tacc.utexas.edu - ThuSat Jun  93 11:3021:33:51 CDT 20222023
Command 8 on c305c304-005.ls6.tacc.utexas.edu - ThuSat Jun  93 1121:3033:3949 CDT 20222023

echo

Lets take a closer look at a typical task in the simple.cmds file.

sleep 5; echo "Command 3 `date`" > cmd3.log 2>&1

The echo command is like a print statement in the bash shell. Echo   echo takes its arguments and writes them to one line of standard output. While not always required, it is a good idea to put echo's output string in double quotes.

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So what is this funny looking `date` bit doing? Well, date is just another Linux command (try just typing it in) that just displays the current date and time. Here we don't want the shell to put the string "date" in the output, we want it to execute the date command and put the result text into the output. The backquotes ( ` ` also called backticks) around the date command tell the shell we want that command executed and its standard output substituted into the string. (Read more about Quoting in the shell.)

# These are equivalent:
date
echo `date`

# But different from this:
echo date

output redirection

There's still more to learn from one of our simple tasks, something called output redirection:still more to learn from one of our simple tasks, something called output redirection.

Every command and Unix program has three "built-in" streams: standard input, standard output and standard error, each with a name, a number, and a redirection syntax.

Image Added

Normally echo writes its string to standard output, but it could encounter an error and write an error message to standard error. We want both standard output and standard error for each task stored in a log file named for the command number.

sleep 5; echo "Command 3 `date`" > cmd3.log 2>&1

Normally echo writes its string to standard output. If you invoke echo in an interactive shell like Terminal, standard output is displayed to the Terminal window.

Image Removed

log 2>&1

So in the above example the first '>' says to redirect the standard output of the echo command to the cmd3.log file. The '2>&1' part says to redirect standard error to the same place. Technically, it says to redirect standard error (built-in Linux stream 2) to the same place as standard output (built-in Linux stream 1); and since standard output is going to cmd3.log, any standard error will go there also. (Read more about Standard streams and redirection)

When the TACC batch system runs a job, all All outputs generated by tasks in your the batch job are directed to one output and error file per job. Here they have names like simple.e2916562e924965 and simple.o2916562; o924965. simple.o2916562o924965 contains all all standard output and simple.o2916562o924965 contains all standard error generated by your tasks that was not redirected elsewhere, as well as information relating to running your job and its tasks. For large jobs with complex tasks, it is not easy to troubleshoot execution problems using these files.

So we usually we want a best practice is to separate the outputs of all our tasks into individual log files, one per tasktask, as we do here. Why is this important? Suppose we run a job with 100 commands, each one a whole pipeline (alignment, for example). 88 finish fine but 12 do not. Just try figuring out which ones had the errors, and where the errors occurred, if all the standard output is in one intermingled file and all standard error in the other intermingled file!So in the above example the first '>' says to redirect the standard output of the echo command to the cmd3.log file. The '2>&1' part says to redirect standard error to the same place. Technically, it says to redirect standard error (built-in Linux stream 2) to the same place as standard output (built-in Linux stream 1); and since standard output is going to cmd3.log, any standard error will go there also. (Read more about Standard I/O streams.) in the other intermingled file!

Job parameters

Now that we've executed a really simple job, let's take a look at some important job submission parameters. These correspond to arguments to the launcher_creator.py script.

A bit of background. Historically, TACC was set up to cater to researchers writing their own C or Fortran codes highly optimized to exploit parallelism (the HPC crowd). Much of TACC's documentation is aimed at this audience, which makes it difficult to pick out the important parts for us.

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The launcher module knows how to interpret various job parameters in the <job_name>.slurm batch SLURM submission script and use them to create your job and assign its tasks to compute nodes. Our launcher_creator.py program is a simple Python script that lets you specify job parameters and writes out a valid <job_name>.slurm submission script.

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usage: launcher_creator.py [-h] -n NAME -t TIME_REQUEST [-j JOB_FILE]
                           [-b SHELL_COMMANDS] [-B SHELL_COMMANDS_FILE]
                           [-q QUEUE] [-a [ALLOCATION]] [-m MODULES]
                           [-M MODULES_FILE] [-w WAYNESS] [-N NUM_NODES]
                           [-e [EMAIL]] [-l LAUNCHER] [-s]

Create launchers for TACC clusters. Report problems to rt-
other@ccbb.utexas.edu

optional arguments:
  -h, --help            show this help message and exit

Required:
  -n NAME, --name NAME  The name of your job.
  -t TIME_REQUEST, --time TIME_REQUEST
                        The time you want to give to your job. Format:
                        hh:mm:ss

Commands:
  You must use at least one of these options to submit your commands for
  TACC.

  -j JOB_FILE, --jobs JOB_FILE
                        The name of the job file containing your commands.
  -b SHELL_COMMANDS, --bash SHELL_COMMANDS
                        A string of shell (Bash, zsh, etc) commands that are
                        executed before any parametric jobs are launched.
  -B SHELL_COMMANDS_FILE, --bash_file SHELL_COMMANDS_FILE
                        A file containing shell (Bash, zsh, etc) commands that
                        are executed before any parametric jobs are launched.

Optional:
  -q QUEUE, --queue QUEUE
                        The TACC allocation for job submission.
                        Default="development"
  -a [ALLOCATION], -A [ALLOCATION], --allocation [ALLOCATION]
                        The TACC allocation for job submission. You can set a
                        default ALLOCATION environment variable.
  -m MODULES, --modules MODULES
                        A list of module commands. The "launcher" module is
                        always automatically included. Example: -m "module
                        swap intel gcc; module load bedtools"
  -M MODULES_FILE, --modules_file MODULES_FILE
                        A file containing module commands.
  -w WAYNESS, --wayness WAYNESS
                        Wayness: the number of commands you want to give each
                        node. The default is the number of cores per node.
  -N NUM_NODES, --num_nodes NUM_NODES
                        Number of nodes to request. You probably don't need
                        this option. Use wayness instead. You ONLY need it if
                        you want to run a job list that isn't defined at the
                        time you submit the launcher.
  -e [EMAIL], --email [EMAIL]
                        Your email address if you want to receive an email
                        from Lonestar when your job starts and ends. Without
                        an argument, it will use a default EMAIL_ADDRESS
                        environment variable.
  -l LAUNCHER, --launcher_name LAUNCHER
                        The name of the launcher script that will be created.
                        Default="<name>.slurm"
  -s                    Echoes the launcher filename to stdout.

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launcher_creator.py -j simple.cmds -n simple -t 00:01:00 -a OTH21164 -q normaldevelopment

• The name of your commands file is given with the -j simple.cmds argument option.
• Your desired job name is given with the -n <job_name> argument simple option.
  • The <job_name> (here simple) is the job name you will see in your queue.
  • By default a corresponding <job_name>.slurm batch file is created for you.
    • It contains the name of the commands file that the batch system will execute.

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• In launcher_creator.py, the queue is specified by the -q argument.
  
  • The default queue is development. Specify -q normal for normal queue jobs.
• The maximum runtime you are requesting for your job is specified by the -t argument.
  • Format is hh:mm:ss
  • Note that your job will be terminated without warning at the end of its time limit!

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You may be a member of a number of different projects, hence have a choice which resource allocation to run your job under.

• You specify that allocation name with the -a argument of launcher_creator.py.
• If you have set an $ALLOCATION environment variable to an allocation name, it that allocation will be used if you are a member of only one TACC project.

# This sets the default project allocation for launcher_creator.py
export ALLOCATION=OTH21164

• When you run a batch job, your project allocation gets "charged" for the time your job runs, in the currency of SUs (System Units).
• SUs are related in some way to node hours, usually 1 SU /= 1 node hour.
  
  

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One of the most confusing things in job submission is the parameter called wayness, which controls how many tasks are run on each computer compute node.

• Recall that there are 68 128 physical cores and 96 256 GB of memory on each compute node
  
  • so technically theoretically you can could run up to 68 128 commands on a node, each with ~1.4 ~2 GB available memory
  • you usually run fewer tasks on a node, and when you do, each task gets more resources

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# If $CORENGS is not defined:
export CORENGS=/work/projects/BioITeam/projects/courses/Core_NGS_Tools

cds
mkdir -p core_ngs/slurm/wayness
cd core_ngs/slurm/wayness
cp ~/CoreNGS$CORENGS/tacc/wayness.cmds .

Exercise: How many tasks are specified in the wayness.cmds file?

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wc -l wayness.cmds

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launcher_creator.py -j wayness.cmds -n wayness -w 4 -t 00:02:00 -a OTH21164 -q normaldevelopment
sbatch --reservation=BIO_DATA_week_1 wayness.slurm
showq -u

Exercise: With 17 16 tasks requested and wayness of 4, how many nodes will this job require? How much memory will be available for each task?

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 Exercise: If you specified a wayness of 2, how many nodes would this job require? How much memory could each task use?

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cat cmd*log

# or, for a listing ordered by node name (the 11th field)
cat cmd*log | sort -k 11,11

The vertical bar ( | ) above is the pipe operator, which connects one program's standard output to the next program's standard input.

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(Read more about the sort command at Linux fundamentals: cut, sort, uniq, and more about Piping)

You should see something like output below.

Command 1 of 16 (4 per node) ran on node c302c303-005.ls6.tacc.utexas.edu core 0
Command 10 of 16 (4 per node) ran on node c305c304-005.ls6.tacc.utexas.edu core 9
Command 11 of 16 (4 per node) ran on node c305c304-005.ls6.tacc.utexas.edu core 10
Command 12 of 16 (4 per node) ran on node c305c304-005.ls6.tacc.utexas.edu core 11
Command 13 of 16 (4 per node) ran on node c305c304-006.ls6.tacc.utexas.edu core 12
Command 14 of 16 (4 per node) ran on node c305c304-006.ls6.tacc.utexas.edu core 13
Command 15 of 16 (4 per node) ran on node c305c304-006.ls6.tacc.utexas.edu core 14
Command 16 of 16 (4 per node) ran on node c305c304-006.ls6.tacc.utexas.edu core 15
Command 2 of 16 (4 per node) ran on node c302c303-005.ls6.tacc.utexas.edu core 1
Command 3 of 16 (4 per node) ran on node c302c303-005.ls6.tacc.utexas.edu core 2
Command 4 of 16 (4 per node) ran on node c302c303-005.ls6.tacc.utexas.edu core 3
Command 5 of 16 (4 per node) ran on node c302c303-006.ls6.tacc.utexas.edu core 4
Command 6 of 16 (4 per node) ran on node c302c303-006.ls6.tacc.utexas.edu core 5
Command 7 of 16 (4 per node) ran on node c302c303-006.ls6.tacc.utexas.edu core 6
Command 8 of 16 (4 per node) ran on node c302c303-006.ls6.tacc.utexas.edu core 7
Command 9 of 16 (4 per node) ran on node c305c304-005.ls6.tacc.utexas.edu core 8

Notice that there are 4 different host names. This expression:

cat cmd*log | awk '{print $11}' | sort | uniq -c

should produce output something like this (read more about piping commands to make a histogram)

   4 c302-005.ls6.tacc.utexas.edu
   4 c302-006.ls6.tacc.utexas.edu
   4 c305-005.ls6.tacc.utexas.edu
   4 c305-006.ls6.tacc.utexas.edu

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We've already touched on the need to redirect standard output and standard error for each task. Just remember that funny redirection syntax:

my_program input_file1 output_file1 > file1.log 2>&1

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For example, you might have a script called align_bwa.sh (a bash script) or align_bowtie2.py (written in python Python) that performs multiple steps needed during the alignment process:

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The BioITeam maintains a set of such scripts in the /work/projects/BioITeam/common/script directory. Take a look at some of them after you feel more comfortable with initial NGS processing steps. They can be executed by anyone with a TACC account.

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# navigate through the symbolic link in your Home directory
cd ~scratch/core_ngs/slurm/simple 
ls ../wayness
ls ../..
ls -l ~/.bashrc

(Read more about Absolute and relative pathname syntax)

Interactive sessions (idev)

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idev -m 2060 -AN OTH211641 -NA 1OTH21164 -p normal --reservation=BIO_DATA_week_1r CoreNGS-Tue

Notes:

• -p normal requests nodes on the normal queue
  • this is the default for our reservation, while the development queue is the normal default
• -m 20 asks for a 20-minute session (120 minutes is the maximum for development) 60 asks for a 60 minute session
• -A OTH21164 -A UT-2015-05-18 specifies the TACC allocation/project to use
• -N 1 asks for 1 node
• --reservation=BIO_DATA_week_1CoreNGS-Tue gives us priority access to TACC nodes for the class. You normally won't use this argumentoption.

When you ask for an idev session, you'll see output as shown below. Note that the process may pause at repeat the "Sleeping for 7 seconds" job status:  PD" (pending) step while it waits for an available node.

  -> Checking on the status of development queue. OK

 -> Defaults file    : ~/.idevrc
 -> System           : ls6
 -> Queue            : normal     development    (cmd line: -p        )
 -> Nodes            : 1              (cmd line: -N        )
 -> Tasks per Node   : 128            (Queue default       )
 -> Time (minutes)   : 2060             (cmd line: -m        )
 -> Project          : OTH21164       (cmd line: -A        )

-----------------------------------------------------------------
          Welcome to the Lonestar6 Supercomputer
-----------------------------------------------------------------

--> Verifying valid submit host (login1)...OK
--> Verifying valid jobname...OK
--> Verifying valid ssh keys...OK
--> Verifying access to desired queue (development)...OK
--> Checking available allocation (OTH21164)...OK
Submitted batch job 232585235465

 -> After your idev job begins to run, a command prompt will appear,
 -> and you can begin your interactive development session.
 -> We will report the job status every 4 seconds: (PD=pending, R=running).

 -> job status:  PD
 -> job status:  R

 -> Job is now running on masternode= c303c302-006005...OK
 -> Sleeping for 7 seconds...OK
 -> Sleeping for 7 seconds...OK
 -> Sleeping for 7 seconds...OK
 -> Checking to make sure your job has initialized an env for you....OK
 -> Creating interactive terminal session (login) on master node c303c302-006005.
 -> ssh -Y  -o "StrictHostKeyChecking no" c303c302-006005   

Once the idev session has started, it looks quite similar to a login node environment, except for these differences:

• the hostname command on a login node will return a login server name like login3.ls6.tacc.utexas.edu
  • while in an idev session hostname returns a compute node name like c303-006.ls6.tacc.utexas.edu
• you cannot submit a batch job from inside an idev session, only from a login node
• your idev session will end when the requested time has expired
  • or you can just type exit to return to a login node session

...