Simple Linux Utility for Resource Management

# Generic Resource (GRES) Scheduling

Generic resource (GRES) scheduling is supported through a flexible plugin mechanism. Support is currently provided for Graphics Processing Units (GPUs) and Intel® Many Integrated Core (MIC) processors.

## Configuration

SLURM supports no generic resources in the default configuration. One must explicitly specify which resources are to be managed in the slurm.conf configuration file. The configuration parameters of interest are:

- GresTypes a comma delimited list of generic resources to be managed (e.g. GresTypes=gpu,mic). This name may be that of an optional plugin providing additional control over the resources.
- Gres the specific generic resource and their count associated with each node (e.g. NodeName=linux[0-999] Gres=gpu:1,mic:2).

Note that the Gres specification for each node works in the same fashion as the other resources managed. Depending upon the value of the FastSchedule parameter, nodes which are found to have fewer resources than configured will be placed in a DOWN state.

Note that the Gres specification is not supported on BlueGene systems.

Each compute node with generic resources must also contain a gres.conf file describing which resources are available on the node, their count, associated device files and CPUs which should be used with those resources. The configuration parameters available are:

- Name name of a generic resource (must match GresTypes values in slurm.conf ).
- Count Number of resources of this type available on this node. The default value is set to the number of File values specified (if any), otherwise the default value is one. A suffix of "K", "M" or "G" may be used to multiply the number by 1024, 1048576 or 1073741824 respectively (e.g. "Count=10G"). Note that Count is a 32-bit field and the maximum value is 4,294,967,295.
- CPUs Specify the CPU index numbers for the specific CPUs which can use this resources. For example, it may be strongly preferable to use specific CPUs with specific devices (e.g. on a NUMA architecture). Multiple CPUs may be specified using a comma delimited list or a range may be specified using a "-" separator (e.g. "0,1,2,3" or "0-3"). If not specified, then any CPU can be used with the resources. If any CPU can be used with the resources, then do not specify the CPUs option for improved speed in the SLURM scheduling logic.
- File Fully qualified pathname of the device files associated with a resource. The name can include a numberic range suffix to be interpreted by SLURM (e.g. File=/dev/nvidia[0-3]). This field is generally required if enforcement of generic resource allocations is to be supported (i.e. prevents a users from making use of resources allocated to a different user). If File is specified then Count must be either set to the number of file names specified or not set (the default value is the number of files specified). NOTE: If you specify the File parameter for a resource on some node, the option must be specified on all nodes and SLURM will track the assignment of each specific resource on each node. Otherwise SLURM will only track a count of allocated resources rather than the state of each individual device file.

Sample gres.conf file:

    # Configure support for our four GPUs
    Name=gpu File=/dev/nvidia0 CPUs=0,1
    Name=gpu File=/dev/nvidia1 CPUs=0,1
    Name=gpu File=/dev/nvidia2 CPUs=2,3
    Name=gpu File=/dev/nvidia3 CPUs=2,3
    Name=bandwidth Count=20M

## Running Jobs

Jobs will not be allocated any generic resources unless specifically requested at job submit time using the --gres option supported by the salloc, sbatch and srun commands. The option requires an argument specifying which generic resources are required and how many resources. The resource specification is of the form name[:count]. The name is the same name as specified by the GresTypes and Gres configuration parameters. count specifies how many resources are required and has a default value of 1. For example:
sbatch --gres=gpu:2 ....

Jobs will be allocated specific generic resources as needed to satisfy the request. If the job is suspended, those resources do not become available for use by other jobs.

Job steps can be allocated generic resources from those allocated to the job using the --gres option with the srun command as described above. By default, a job step will be allocated all of the generic resources allocated to the job. If desired, the job step may explicitly specify a different generic resource count than the job. This design choice was based upon a scenario where each job executes many job steps. If job steps were granted access to all generic resources by default, some job steps would need to explicitly specify zero generic resource counts, which we considered more confusing. The job step can be allocated specific generic resources and those resources will not be available to other job steps. A simple example is shown below.

    #!/bin/bash
    #
    # gres_test.bash
    # Submit as follows:
    # sbatch --gres=gpu:4 -n4 -N1-1 gres_test.bash
    #
    srun --gres=gpu:2 -n2 --exclusive show_device.sh &
    srun --gres=gpu:1 -n1 --exclusive show_device.sh &
    srun --gres=gpu:1 -n1 --exclusive show_device.sh &
    wait

## GPU Management

In the case of SLURM's GRES plugin for GPUs, the environment variable CUDA_VISIBLE_DEVICES is set for each job step to determine which GPUs are available for its use on each node. This environment variable is only set when tasks are launched on a specific compute node (no global environment variable is set for the salloc command and the environment variable set for the sbatch command only reflects the GPUs allocated to that job on that node, node zero of the allocation). CUDA version 3.1 (or higher) uses this environment variable in order to run multiple jobs or job steps on a node with GPUs and insure that the resources assigned to each are unique. In the example above, the allocated node may have four or more graphics devices. In that case, CUDA_VISIBLE_DEVICES will reference unique devices for each file and the output might resemble this:

    JobStep=1234.0 CUDA_VISIBLE_DEVICES=0,1
    JobStep=1234.1 CUDA_VISIBLE_DEVICES=2
    JobStep=1234.2 CUDA_VISIBLE_DEVICES=3

NOTE: Be sure to specify the File parameters in the gres.conf file and insure they are in the increasing numeric order.

## MIC Management

SLURM can be used to provide resource management for systems with the Intel® Many Integrated Core (MIC) processor. SLURM sets an OFFLOAD_DEVICES environment variable, which controls the selection of MICs available to a job step. The OFFLOAD_DEVICES environment variable is used by both Intel LEO (Language Extensioins for Offload) and the MKL (Math Kernel Library) automatic offload. (This is very similar to how the CUDA_VISIBLE_DEVICES environment variable is used to control which GPUs can be used by CUDA™ software.) If no MICs are reserved via GRES, the OFFLOAD_DEVICES variable is set to -1. This causes the code to ignore the offload directives and run MKL routines on the CPU. The code will still run but only on the CPU. This also gives a somewhat cryptic warning:

    offload warning: OFFLOAD_DEVICES device number -1 does not correspond
    to a physical device

The offloading is automatically scaled to all the devices, (e.g. if --gres=mic:2 is defined) then all offloads use two MICs unless explicitly defined in the offload pragmas.

Last modified 25 October 2012