Using resources effectively

Overview

Teaching: 10 min
Exercises: 20 min

Questions

• How can I review past jobs?

• How can I use this knowledge to create a more accurate submission script?

Objectives

• Look up job statistics.

• Make more accurate resource requests in job scripts based on data describing past performance.

We’ve touched on all the skills you need to interact with an HPC cluster: logging in over SSH, loading software modules, submitting parallel jobs, and finding the output. Let’s learn about estimating resource usage and why it might matter.

Estimating Required Resources Using the Scheduler

Although we covered requesting resources from the scheduler earlier with the π code, how do we know what type of resources the software will need in the first place, and its demand for each? In general, unless the software documentation or user testimonials provide some idea, we won’t know how much memory or compute time a program will need.

Read the Documentation

Most HPC facilities maintain documentation as a wiki, a website, or a document sent along when you register for an account. Take a look at these resources, and search for the software you plan to use: somebody might have written up guidance for getting the most out of it.

A convenient way of figuring out the resources required for a job to run successfully is to submit a test job, and then ask the scheduler about its impact using sacct -u yourUsername. You can use this knowledge to set up the next job with a closer estimate of its load on the system. A good general rule is to ask the scheduler for 20% to 30% more time and memory than you expect the job to need. This ensures that minor fluctuations in run time or memory use will not result in your job being cancelled by the scheduler. Keep in mind that if you ask for too much, your job may not run even though enough resources are available, because the scheduler will be waiting for other people’s jobs to finish and free up the resources needed to match what you asked for.

Stats

Since we already submitted pi.py to run on the cluster, we can query the scheduler to see how long our job took and what resources were used. We will use sacct -u yourUsername to get statistics about parallel-pi.sh.

501 ruth% sacct -u yourUsername

      JobID    JobName  Partition  AllocCPUS      State Exit
------------ ---------- ---------- ---------- ---------- ----
212339         hostname     serial      2  COMPLETED
212340         hostname     serial      2  COMPLETED
212341              env     serial      2  COMPLETED
212342           mpirun       fast      2  COMPLETED
212343           mpirun       fast      2  COMPLETED
212344              cpi       fast      2  COMPLETED
212345              cpi       fast      2  COMPLETED
212346             bash       fast      2  COMPLETED
212346.0           bash                 2  COMPLETED
212346.1            cpi                 2  COMPLETED
212347              cpi       fast      2     FAILED

This shows all the jobs we ran recently (note that there are multiple entries per job). To get info about a specific job, we change command slightly.

501 ruth% sacct -u yourUsername --format=JobID,JobName,Submit,Start,State,ReqCPUS,Reserved,Elapsed,MaxRSS 1965

It will show a lot of info, in fact, every single piece of info collected on your job by the scheduler. It may be useful to redirect this information to less to make it easier to view (use the left and right arrow keys to scroll through fields).

501 ruth% sacct -u yourUsername --format=JobID,JobName,Submit,Start,State,ReqCPUS,Reserved,Elapsed,MaxRSS

Discussion

This view can help compare the amount of time requested and actually used, duration of residence in the queue before launching, and memory footprint on the compute node(s).

How accurate were our estimates?

Improving Resource Requests

From the job history, we see that pi.py jobs finished executing in at most a few minutes, once dispatched. The time estimate we provided in the job script was far too long! This makes it harder for the queuing system to accurately estimate when resources will become free for other jobs. Practically, this means that the queuing system waits to dispatch our pi.py job until the full requested time slot opens, instead of “sneaking it in” a much shorter window where the job could actually finish. Specifying the expected runtime in the submission script more accurately will help alleviate cluster congestion and may get your job dispatched earlier.

Narrow the Time Estimate

Edit parallel_pi.sh to set a better time estimate. How close can you get?

Hint: use -t.

Solution

The following line tells Slurm that our job should finish within 2 minutes:

#SBATCH -t 00:02:00

Key Points

• Accurate job scripts help the queuing system efficiently allocate shared resources.