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Job Arrays with LLsub Triples in 3 Steps

If you are currently running a Job Array, you can take advantage of SuperCloud's triples mode submission by submitting your job array with LLsub. In most cases, this can be done in a few easy steps.

What is triples mode? It's a different way to submit your job by specifying three numbers:

  • Nodes: The number of nodes you want to use up
  • NPPN: The number of processes that should run per node
  • NTPP: The number of threads per process

Why would you want to use triples mode to submit your job? Triples mode provides a few advantages. Since it does whole-node scheduling you don't have to worry about other jobs impacting yours (or your job impacting someone else's). We also do task-pinning when you request resources with a triple, so your processes are arranged in the best way possible for the layout of the hardware architecture. Finally, submitting a Job Array with LLsub triples mode the way we describe here will greatly reduce the startup time for your jobs, over a job array with many pending tasks.

Step 1: Batch Up your Array

The first thing you want to do is set up your job so that your script splits up your inputs among each process/task and each process/task iterates a subset of your inputs. This way you can change your input set without changing the number of tasks or processes that you schedule. This step alone will save you on startup time. Anytime you submit more jobs than your allocation allows, those additional jobs will remaining pending until some of your first running jobs complete. Then, when one of your first jobs complete, the scheduler now has to find the pending job some resources and start it running. If you batch up your job array, you only have to go through the scheduling process once. If you've set up a job array following our instructions in the past you may already be doing this, and you can skip to the next section.

First you want to take a look at your code. Code that can be submitted as a Job Array usually has one big for loop. If you are iterating over multiple parameters or files, and have nested for loops, you'll first want to enumerate all the combinations of what you are iterating over so you have one big loop.

If your code is written so it uses the $SLURM_ARRAY_TASK_ID and uses that to run a single thing, first add a for loop that iterates over the full set of things you want to run your code on. If you can't rewrite your code in such a way that it iterates over multiple inputs, you can use LLMapReduce to submit your job with triples mode, see this example.

Then you add a few lines to your code to take in two arguments, a process/task ID and the number of processes/tasks, and use those numbers to split up the thing you are iterating over. For example, I might have a list of filenames, fnames. In python I would add:

# Grab the arguments that are passed in
my_task_id = int(sys.argv[1])
num_tasks = int(sys.argv[2])

# Assign indices to this process/task
my_fnames = fnames[my_task_id:len(fnames):num_tasks]

for f in my_fnames: ...

# Grab the arguments that are passed in
task_id = parse(Int,ARGS[1])
num_tasks = parse(Int,ARGS[2])

# Assign indices to this process/task
my_fnames = fnames[task_id+1:num_tasks:length(fnames)]

for f in my_fnames
    ...

Notice that I am iterating over my_fnames, which is a subset of the full list of filenames determined by the task ID and number of tasks. This subset will be different for each task in the array. Note that the third line of code will be different for languages with arrays that start at index 1 (see the Julia Job Array code for an example of this).

Step 2: Changing Your Submission Script

If you have been running your Job Arrays with sbatch, you most likely have a few environment variables in your submission script. To submit with LLsub triples, you can just replace these:

  • $SLURM_ARRAY_TASK_ID -> $LLSUB_RANK
  • $SLURM_ARRAY_TASK_COUNT -> $LLSUB_SIZE

If you have any SBATCH flags in your submission script, remove those as well (LLsub will see these and submit with sbatch, ignoring any command line arguments you give it). For example, if you are running a python script, your final submission script will look something like this:

#!/bin/bash

# Load Module(s)
module load anaconda/2023a

echo "My task ID: " $LLSUB_RANK
echo "Number of Tasks: " $LLSUB_SIZE

python top5each.py $LLSUB_RANK $LLSUB_SIZE

You will also need to make your script executable. You can do that with this simple command line command:

chmod u+x submit_LLsub.sh

Step 3: Submit your Job with LLsub Triples

Now when you submit your job, you can run:

LLsub ./submit.sh [NODES,NPPN,NTPP]

where

  • NODES is the number of nodes you want to use up
  • NPPN is the number of processes that should run per node
  • NTPP is the number of threads per process

The total number of threads per node, or NTPP*NPPN, should not be more than the number of cores on the node, otherwise you may overwhelm the node with too many running processes and/or threads. For example, if you are running on the 48-core Xeon-P8 nodes, if you are running with NTPP=1, you should not set NPPN more than 48. If NTPP=2, NPPN should be at most 24, etc. The numbers you choose depend on your application, if it is multithreaded it may be worth increasing NTPP and decreasing NPPN. If your application consumes a lot of memory, you may need to decrease NPPN so each process has the memory it needs to proceed. The best way to determine what numbers to choose is to tune your triples, which is a relatively quick exercise and can improve your speedup in the long run by helping you select the ideal numbers for your triple.

So if you want to run on 2 nodes, 10 processes per node, and 4 threads per process, you would run:

LLsub ./submit.sh [2,10,4]

If you were to run LLstat after running this command, you would see what looks like a 2 task job array, for example:

$ LLstat
LLGrid: txe1 (running slurm-wlm 20.11.3)
JOBID ARRAY_JOB_ NAME USER START_TIME PARTITION CPUS FEATURES MIN_MEMORY ST NODELIST(REASON)
9651412_1 9651412 submit_LLsub.sh studentx 2021-03-19T10:32:26 normal 40 xeon-g6 8500M R d-13-8-2
9651412_2 9651412 submit_LLsub.sh studentx 2021-03-19T10:32:26 normal 40 xeon-g6 8500M R d-13-8-1

However, you are still running 2*10 = 20 total processes. Triples mode uses slurm to request full nodes, then takes care of launching the processes on each node. This is why you'll always see one task for each node in the LLstat output, rather than each process. One advantage of this is it makes for a more compact LLstat output that is easier to read, instead of having to scroll through tons of tasks.

You can check on your individual processes by looking at the log files. LLsub with Triples mode will create a directory with the prefix "LLSUB" followed by a unique number to hold all the log files. Within this directory will be one launch log file, which will capture any errors that occur during launch ,and one directory per node and put the log files for each process in its node's directory. These subdirectories are labeled with the process ID range and the node name.

For example, here are the log files from a triples run using [2,4,10]:

$ ls LLSUB.23004
README.md helpers.py submit_LLsub.sh submit_sbatch.sh top5each.py

$ ls LLSUB.23004/
llsub-triple-mode-launch.log-10006591 p0-p3_d-19-4-1 p4-p7_d-19-3-4

$ ls LLSUB.23004/p0-p3_d-19-4-1/
submit_LLsub.sh.log.4 submit_LLsub.sh.log.5 submit_LLsub.sh.log.6 submit_LLsub.sh.log.7

$ ls LLSUB.23004/p4-p7_d-19-3-4/
submit_LLsub.sh.log.4 submit_LLsub.sh.log.5 submit_LLsub.sh.log.6 submit_LLsub.sh.log.7

In this example, LLSUB.23004 is the directory containing the log files, llsub-triple-mode-launch.log-10006591 is the log file for the job launch. p0-p3_d-19-4-1 and p4-p7_d-19-3-4 are the directories for each node, and submit_LLsub.sh.log.0, submit_LLsub.sh.log.1, ..., submit_LLsub.sh.log.7 are the log files for each process.