Job scheduling policies

Parent page: Running jobs

You can do much work on Compute Canada clusters by submitting jobs that specify only the number of cores and a runtime limit. However if you submit large numbers of jobs, or jobs that require large amounts of resources, you may be able to improve your productivity by understanding the policies affecting job scheduling.

Priority and fair-share

The order in which jobs are considered for scheduling is determined by priority. Priority on our systems is determined using the Fair Tree algorithm.

Each job is billed to a Resource Allocation Project (RAP). You specify the project with the --account argument to sbatch. The project might hold a grant of CPU or GPU time from a Resource Allocation Competition, in which case the account code will probably begin with rrg- or rpp-. Or it could be a non-RAC project, also known as a Rapid Access Service project, in which case the account code will probably begin with def-. See Accounts and Projects for how to determine what account codes you can use.

Every project has a target usage level. Non-RAC projects all have equal target usage, while RAC projects have target usages determined by the number of CPU-years or GPU-years granted with each RAC award.

As an example let us look at usage and share information for an imaginary research group with the account code def-prof1. Members of this imaginary group have user names prof1, grad2 and postdoc3. Note that we must append _cpu or _gpu to the end of the account code, as appropriate, since CPU and GPU use are tracked separately.

[prof1@gra-login4 ~]$ sshare -l -A def-prof1_cpu -u prof1,grad2,postdoc3
       Account       User  RawShares  NormShares  RawUsage  ... EffectvUsage  ...    LevelFS  ...
-------------- ---------- ---------- -----------  --------  ... ------------  ... ----------  ...
def-prof1_cpu                 434086    0.001607   1512054  ...     0.000043  ...  37.357207  ...
 def-prof1_cpu      prof1          1    0.100000         0  ...     0.000000  ...        inf  ...   
 def-prof1_cpu      grad2          1    0.100000     54618  ...     0.036122  ...   2.768390  ...
 def-prof1_cpu   postdoc3          1    0.100000    855517  ...     0.565798  ...   0.176741  ...

The output shown above has been simplified by removing several fields which are not relevant to this discussion.

• Account, obviously, is the project name with _cpu or _gpu appended.
• User: Notice that the first line of output does not include a user name. This line describes the status of the project relative to all other projects using the cluster. Successive lines describe the status of each user relative to other users in this project. The project by itself, or the user within a project, is referred to as an "association" in the Slurm documentation.
• RawShares is proportional to the number of CPU-years that was granted to the project for use on this cluster in the Resource Allocation Competition. All non-RAC projects with jobs in the system (pending or running) have equal values for RawShares. This means that RawShares, NormShares, and LevelFS are not meaningful for a def-* account with no jobs in the system. The list of active accounts is refreshed every 15 minutes.
• NormShares is the number of shares assigned to the user or account divided by the total number of assigned shares within the level. So for the first line, the NormShares of 0.001607 is the fraction of the shares held by the project, relative to all other projects. The NormShares of 0.10000 on the other three lines are the fraction of shares held by each member of the project relative to the other members. (This project has ten members, but we only asked for information about three.)
• RawUsage is a weighted reflection of the total number of resource-seconds (e.g. CPU, memory, GPU) that have been charged to this account.
• EffectvUsage is the association's usage normalized with its parent; that is, the project's usage relative to other projects, the user's relative to other users in that project. In this example, postdoc3 has 56.6% of the project's usage, and grad2 has 3.6%.
• LevelFS is the association's fairshare value compared to its siblings, calculated as NormShares / EffectvUsage. If an association is over-served, the value is between 0 and 1. If an association is under-served, the value is greater than 1. Associations with no usage receive the highest possible value, infinity.

A project which consistently uses its target amount will have a LevelFS near 1.0. If the project uses more than its target, then its LevelFS will be below 1.0 and the priority of new jobs belonging to that project will also be low. If the project uses less than its target usage then its LevelFS will be greater than 1.0 and new jobs will enjoy high priority.

Usage is not strictly a matter of CPU-hours or GPU-hours. Memory usage is also factored in. Past usage is discounted with a half-life of one week, so usage more than a few weeks in the past will have only a small effect on priority.

See also: Allocations and resource scheduling.

Whole nodes versus cores

Parallel calculations which can efficiently use 32 or more cores may benefit from being scheduled on whole nodes. Some of the nodes in each cluster are reserved for jobs which request one or more entire nodes. See whole nodes for more discussion. Parallel work on Graham requiring multiples of 32 cores should use Slurm batch job scripts including resource requests of the form:

#SBATCH --nodes=N
#SBATCH --ntasks-per-node=32

On Cedar, there are 24-core GPU-enabled nodes, 32-core nodes and 48-core nodes. The 48-core nodes are the ones for which whole-node jobs are preferentially scheduled. So, to request whole nodes on Cedar, use

#SBATCH --nodes=N
#SBATCH --ntasks-per-node=48

If you have huge amounts of serial work and can efficiently use GNU Parallel or other techniques to pack serial processes onto a single node, you may similarly use --nodes.

Note that requesting an inefficient number of processors for a calculation simply in order to take advantage of any whole-node scheduling advantage will be construed as abuse of the system. For example, a program which takes just as long to run on 32 cores as on 16 cores should request --ntasks=16, not --nodes=1 --ntasks-per-node=32. (Although --nodes=1 --ntasks-per-node=16 is fine if you need all the tasks to be on the same node.) Similarly, using whole nodes commits the user to a specific amount of memory--- submitting whole-node jobs that underutilize memory is as abusive as underutilizing cores.

Whole-node memory

The most common compute nodes at Cedar and Graham have 128GB of memory, but a small piece of that memory is reserved for the use of the operating system. If you request --mem=128G your job will not qualify to run on these "base" nodes, and therefore may wait longer than necessary to start. A memory request of --mem=128000M will allow your job to run on these nodes and therefore probably start sooner.

Requesting --mem=0 grants the job access to all of the memory on each node. If you don't specifically need large nodes (that is, if you don't need nodes with more than 128G of memory), using --mem=0 is recommended.

Time limits

Cedar and Graham will accept jobs of up to 28 days in run-time. However, jobs of that length will be restricted to use only a small fraction of the cluster. (Approximately 10%, but this fraction is subject to change without notice.)

There are several partitions for jobs of shorter and shorter run-times. Currently there are partitions for jobs of

• 3 hours or less,
• 12 hours or less,
• 24 hours (1 day) or less,
• 72 hours (3 days) or less,
• 7 days or less, and
• 28 days or less.

Because any job of 3 hours is also less than 12 hours, 24 hours, and so on, shorter jobs can always run in partitions with longer time-limits. A shorter job will have more scheduling opportunities than an otherwise-identical longer job.

Backfilling

The scheduler employs backfilling to improve overall system usage.

Without backfill scheduling, each partition is scheduled strictly in priority order, which typically results in significantly lower system utilization and responsiveness than otherwise possible. Backfill scheduling will start lower priority jobs if doing so does not delay the expected start time of any higher priority jobs. Since the expected start time of pending jobs depends upon the expected completion time of running jobs, reasonably accurate time limits are important for backfill scheduling to work well.

Backfilling will primarily benefit jobs with short time limits, e.g. under 3 hours.

Preemption

You can access more resources if your application can be checkpointed, stopped, and restarted efficiently.

TODO: Instructions on submitting a preemptible job

Percentage of the nodes you have access to

This section aims at giving some insight into how the general-purpose clusters (Cedar and Graham) are partitioned.

First, the nodes are partitioned into four different categories:

• Base nodes, which have 4 or 8 GB of memory per core
• Large memory nodes, which have 16 to 96 GB of memory per core
• GPU nodes
• Large GPU nodes (on Cedar only)

Upon submission, your job will be routed to one of these categories based on what resources are requested.

Second, within each of the above categories, some nodes are reserved for jobs which can make use of complete nodes (i.e. jobs which use all of the resources available on the allocated nodes). If your job only uses a few cores (or a single core) out of each node, it is only allowed to use a subset of the category. These are referred to as "by-node" and "by-core" partitions.

Finally, the nodes are partitioned based on the walltime requested by your job. Shorter jobs have access to more resources. For example, a job with less than 3 hours of requested walltime can run on any node that allows 12 hours, but there are nodes which accept 3 hour jobs that do *not* accept 12 hour jobs.

The utility partition-stats shows

• how many jobs are waiting to run ("queued") in each partition,
• how many jobs are currently running,
• how many nodes are currently idle, and
• how many nodes are assigned to each partition.

Here is some sample output from partition-stats:

[user@gra-login3 ~]$ partition-stats

Node type |                     Max walltime
          |   3 hr   |  12 hr  |  24 hr  |  72 hr  |  168 hr |  672 hr |
----------|-------------------------------------------------------------
       Number of Queued Jobs by partition Type (by node:by core)
----------|-------------------------------------------------------------
Regular   |   12:170 |  69:7066|  70:7335| 386:961 |  59:509 |   5:165 |
Large Mem |    0:0   |   0:0   |   0:0   |   0:15  |   0:1   |   0:4   |
GPU       |    5:14  |   3:8   |  21:1   | 177:110 |   1:5   |   1:1   |
----------|-------------------------------------------------------------
      Number of Running Jobs by partition Type (by node:by core)
----------|-------------------------------------------------------------
Regular   |    8:32  |  10:854 |  84:10  |  15:65  |   0:674 |   1:26  |
Large Mem |    0:0   |   0:0   |   0:0   |   0:1   |   0:0   |   0:0   |
GPU       |    5:0   |   2:13  |  47:20  |  19:18  |   0:3   |   0:0   |
----------|-------------------------------------------------------------
        Number of Idle nodes by partition Type (by node:by core)
----------|-------------------------------------------------------------
Regular   |   16:9   |  15:8   |  15:8   |   7:0   |   2:0   |   0:0   |
Large Mem |    3:1   |   3:1   |   0:0   |   0:0   |   0:0   |   0:0   |
GPU       |    0:0   |   0:0   |   0:0   |   0:0   |   0:0   |   0:0   |
----------|-------------------------------------------------------------
       Total Number of nodes by partition Type (by node:by core)
----------|-------------------------------------------------------------
Regular   |  871:431 | 851:411 | 821:391 | 636:276 | 281:164 |  90:50  |
Large Mem |   27:12  |  27:12  |  24:11  |  20:3   |   4:3   |   3:2   |
GPU       |  156:78  | 156:78  | 144:72  | 104:52  |  13:12  |  13:12  |
----------|-------------------------------------------------------------

Looking at the first entry in the table, at the upper left, the numbers 12:170, 0:0, and 5:14 mean that there were

• 12 jobs waiting to run which requested
  • whole nodes,
  • less than 8GB of memory per core, and
  • 3 hours or less of run time.
• 170 jobs waiting which requested
  • less than whole nodes and were therefore waiting to be scheduled on individual cores,
  • less than 8GB memory per core, and
  • 3 hours or less of run time.
• 5 jobs waiting which requested
  • a whole node equipped with GPUs and
  • 3 hours or less of run time.
• 14 jobs waiting which requested
  • single GPUs and
  • 3 hours or less of run time.

There were no jobs running or waiting which requested large-memory nodes and 3 hours of run time.

At the bottom of the table we find the division of resources by policy, independent of the immediate number of jobs. Hence there are 871 base nodes, called "regular" here (that is, nodes with 4 to 8 GB memory per core), which may receive whole-node jobs of up to 3 hours. Of those 871,

• 431 of them may also receive by-core jobs of up to three hours,
• 851 of them may receive whole-node jobs of up to 12 hours,
• and so on.

It may help to think of these partitions as being like Matryoshka (Russian) dolls. The 3-hour partition contains the nodes for the 12-hour partition as a subset. The 12-hour partition in turn contains the 24-hour partition, and so on.

The partition-stats utility does not give information about the number of cores represented by running or waiting jobs, nor the number of cores free in partly-assigned nodes in by-core partitions, nor about available memory associated with free cores in by-core partitions.

Running partition-stats is somewhat costly to the scheduler. Please do not write a script which automatically calls partition-stats repeatedly. If you have a workflow which you believe would benefit from automatic parsing of the information from partition-stats, please contact Technical support and ask for guidance.

Resource Allocation Competition