# Consumable Resources in SLURM

SLURM, using the default node allocation plug-in, allocates nodes to jobs in exclusive mode which means that even when all the resources within a node are not utilized by a given job, another job will not have access to these resources. Nodes possess resources such as processors, memory, swap, local disk, etc. and jobs consume these resources. The exclusive use default policy in SLURM can result in inefficient utilization of the cluster and of its nodes resources.

A plug-in supporting cpu as a consumable resource is available in SLURM 0.5.0 and newer version of SLURM. Information on how to use this plug-in is described below.

## Using CPU Consumable Resource Node Allocation Plugin

1. This plug-in is available in SLURM 0.5.0 and newer version of SLURM
2. The consumable resource plugin can be enabled by defining a SelectType in the slurm.conf (e.g. SelectType=select/cons_res).
3. The select/cons_res plugin is enabled or disabled cluster-wide.
4. Partitions labeled as SHARED=Yes and SHARED=FORCE do not make sense in connection with the consumable resources support. Consumable resources support only make sense for SHARED=No. We have chosen to set SHARED to No within the SLURM code if the select/cons_res plugin is enabled. In the cases where the select/cons_res plugin is not enabled the normal SLURM behaviors are not disrupted. The only changes, users will see when using the select/cons_res plugin, are that jobs can be co-scheduled on nodes when cpu resources permits it. The rest of SLURM such as srun and switches, etc. are not affected by this plugin. SLURM is, from a user point of view, working the same way as when using the default node selection scheme.
5. We introduce a new switch --exclusive which will allow users to reserve/use nodes in exclusive mode even when consumable resources is enabled. see "man srun" for details.
6. SLURM's default select/linear plugin is using a best fit algorithm based on number of consecutive nodes. We have chosen the same node allocation approach for consistency.

## Example of Node Allocations Using Consumable Resource Plugin

The following example illustrates the different ways four jobs are allocated across a cluster using (1) SLURM's default allocation (exclusive mode) and (2) a processor as consumable resource approach.

It is important to understand that the example listed below is a contrived example and is only given here to illustrate the use of cpu as consumable resources. Job 2 and Job 3 call for the node count to equal the processor count. This would typically be done because that one task per node requires all of the memory, disk space, etc. The bottleneck would not be processor count.

Trying to execute more than one job per node will almost certainly severely impact parallel job's performance. The biggest beneficiary of cpus as consumable resources will be serial jobs or jobs with modest parallelism, which can effectively share resources. On a lot of systems with larger processor count, jobs typically run one fewer task than there are processors to minimize interference by the kernel and daemons.

The example cluster is composed of 4 nodes (10 cpus in total):

- linux01 (with 2 processors),
- linux02 (with 2 processors),
- linux03 (with 2 processors), and
- linux04 (with 4 processors).

The four jobs are the following:

- [2] srun -n 4 -N 4 sleep 120 &
- [3] srun -n 3 -N 3 sleep 120 &
- [4] srun -n 1 sleep 120 &
- [5] srun -n 3 sleep 120 &

The user launches them in the same order as listed above.

## Using SLURM's Default Node Allocation (Non-shared Mode)

The four jobs have been launched and 3 of the jobs are now pending, waiting to get resources allocated to them. Only Job 2 is running since it uses one cpu on all 4 nodes. This means that linux01 to linux03 each have one idle cpu and linux04 has 3 idle cpus.

    # squeue
      JOBID PARTITION     NAME     USER  ST       TIME  NODES NODELIST(REASON)
          3       lsf    sleep     root  PD       0:00      3 (Resources)
          4       lsf    sleep     root  PD       0:00      1 (Resources)
          5       lsf    sleep     root  PD       0:00      1 (Resources)
          2       lsf    sleep     root   R       0:14      4 xc14n[13-16]

Once Job 2 is finished, Job 3 is scheduled and runs on linux01, linux02, and linux03. Job 3 is only using one cpu on each of the 3 nodes. Job 4 can be allocated onto the remaining idle node (linux04) so Job 3 and Job 4 can run concurrently on the cluster.

Job 5 has to wait for idle nodes to be able to run.

    # squeue
      JOBID PARTITION     NAME     USER  ST       TIME  NODES NODELIST(REASON)
          5       lsf    sleep     root  PD       0:00      1 (Resources)
          3       lsf    sleep     root   R       0:11      3 xc14n[13-15]
          4       lsf    sleep     root   R       0:11      1 xc14n16

Once Job 3 finishes, Job 5 is allocated resources and can run.

The advantage of the exclusive mode scheduling policy is that the a job gets all the resources of the assigned nodes for optimal parallel performance. The drawback is that jobs can tie up large amount of resources that it does not use and which cannot be shared with other jobs.

## Using a Processor Consumable Resource Approach

The output of squeue shows that we have 3 out of the 4 jobs allocated and running. This is a 2 running job increase over the default SLURM approach.

Job 2 is running on nodes linux01 to linux04. Job 2's allocation is the same as for SLURM's default allocation which is that it uses one cpu on each of the 4 nodes. Once Job 2 is scheduled and running, nodes linux01, linux02 and linux03 still have one idle cpu each and node linux04 has 3 idle cpus. The main difference between this approach and the exclusive mode approach described above is that idle cpus within a node are now allowed to be assigned to other jobs.

It is important to note that assigned doesn't mean oversubscription. The consumable resource approach tracks how much of each available resource (in our case cpus) must be dedicated to a given job. This allows us to prevent per node oversubscription of resources (cpus).

Once Job 2 is running, Job 3 is scheduled onto node linux01, linux02, and Linux03 (using one cpu on each of the nodes) and Job 4 is scheduled onto one of the remaining idle cpus on Linux04.

Job 2, Job 3, and Job 4 are now running concurrently on the cluster.

    # squeue
      JOBID PARTITION     NAME     USER  ST       TIME  NODES NODELIST(REASON)
          5       lsf    sleep     root  PD       0:00      1 (Resources)
          2       lsf    sleep     root   R       0:13      4 linux[01-04]
          3       lsf    sleep     root   R       0:09      3 linux[01-03]
          4       lsf    sleep     root   R       0:05      1 linux04

    # sinfo -lNe
    NODELIST     NODES PARTITION       STATE CPUS MEMORY TMP_DISK WEIGHT FEATURES REASON
    linux[01-03]    3      lsf*   allocated    2   2981        1      1   (null) none
    linux04         1      lsf*   allocated    4   3813        1      1   (null) none

Once Job 2 finishes, Job 5, which was pending, is allocated available resources and is then running as illustrated below:

    # squeue
      JOBID PARTITION     NAME     USER  ST       TIME  NODES NODELIST(REASON)
          3       lsf    sleep     root   R       1:58      3 linux[01-03]
          4       lsf    sleep     root   R       1:54      1 linux04
          5       lsf    sleep     root   R       0:02      3 linux[01-03]
    # sinfo -lNe
    NODELIST     NODES PARTITION       STATE CPUS MEMORY TMP_DISK WEIGHT FEATURES REASON
    linux[01-03]     3      lsf*   allocated    2   2981        1      1   (null) none
    linux04          1      lsf*        idle    4   3813        1      1   (null) none

Job 3, Job 4, and Job 5 are now running concurrently on the cluster.

    # squeue
      JOBID PARTITION     NAME     USER  ST       TIME  NODES NODELIST(REASON)
          5       lsf    sleep     root   R       1:52      3 linux[01-03]

Job 3 and Job 4 have finshed and Job 5 is still running on nodes linux[01-03].

The advantage of the consumable resource scheduling policy is that the job throughput can increase dramatically. The overall job throughput/productivity of the cluster increases thereby reducing the amount of time users have to wait for their job to complete as well as increasing the overall efficiency of the use of the cluster. The drawback is that users do not have the entire node dedicated to their job since they have to share nodes with other jobs if they do not use all of the resources on the nodes.

We have added a "--exclusive" switch to srun which allow users to specify that they would like their allocated nodes in exclusive mode. For more information see "man srun". The reason for that is if users have mpi//threaded/openMP programs that will take advantage of all the cpus within a node but only need one mpi process per node.

Last modified 13 May 2005