HTCondor can help manage the allocation of GPUs (graphics processing units) to jobs within a pool of execute nodes, where HTCondor considers the GPUs as resources to jobs. The jobs can use the GPUs using an API such as OpenCL or CUDA.

The techniques described here depend on HTCondor version 8.1.4 or a later version. Those with less current versions may try an older, less flexible technique as described at HowToManageGpusInSeriesSeven.

The general technique to manage GPUs has 3 steps:

  1. Advertise the GPU by configuring HTCondor such that an execute node includes information about available GPUs in its machine ClassAd.
  2. A job requests a GPU, specifying any further specific requirements about the GPU, in order to acquire a suitable GPU
  3. The job identifies the GPU through the use of arguments or environment, to learn which GPU it may use.

1. Advertise the GPU

The availability of GPU resources must be advertised in the machine's ClassAd, in order for jobs that need GPUs to be matched with machines that have GPUs. As of HTCondor version 8.2.0 (actually 8.1.6), the detection and advertisement of GPUs is automated by adding a single line representing a metaknob to the configuration of the execute node.

   use feature : GPUs

Advertise additional attributes of the GPUs by also setting


The feature:GPUs metaknob will invoke the condor_gpu_discovery tool to generate and populate the machine ClassAd for a custom resource identified by the GPUs tag within ClassAd attribute names.

The HTCondor condor_gpu_discovery tool is designed to assist in detecting GPUs and in providing details that help to set up the advertisement of GPU information. This tool detects CUDA and OpenCL devices, and it outputs a list of GPU identifiers for all detected devices.

HTCondor has a general mechanism for declaring user-defined slot resources. GPUs are a user-defined slot resource, so this same mechanism is used to define a resource. Use of the metaknob always uses the resource type name GPUs. This resource type name is case insensitive, but all characters within the name are significant, so be consistent.

What the metaknob configures

The feature GPUs defines a custom resource with this configuration of the execute node:

    MACHINE_RESOURCE_INVENTORY_GPUs = $(LIBEXEC)/condor_gpu_discovery -properties

MACHINE_RESOURCE_INVENTORY_GPUs tells HTCondor to run the condor_gpu_discovery tool, and use its output to define a custom resource with the resource tag GPUs.

ENVIRONMENT_FOR_AssignedGPUs tells HTCondor to publish the value of machine ClassAd attribute AssignedGPUs for a slot in the job's environment using the environment variables GPU_DEVICE_ORDINAL and CUDA_VISIBLE_DEVICES. In addition, AssignedGPUs will always be published into the job's environment as _CONDOR_AssignedGPUs.

The output of the condor_gpu_discovery tool reports DetectedGPUs and lists the GPU id of each one. GPU ids will be CUDA<n> or OCL<n>, where <n> is an integer, and CUDA or OCL indicates whether the CUDA library or the OpenCL library is used to communicate with the device.

The -properties argument in the condor_gpu_discovery command tells it to also list significant attributes of the device(s). These attributes will then be published in each slot ClassAd.

Here is typical output of condor_gpu_discovery:

> condor_gpu_discovery -properties
DetectedGPUs="CUDA0, CUDA1, CUDA2, CUDA3"
CUDADeviceName="GeForce GTX 690"

This output indicates that 4 GPUs were detected, all of which have the same properties.

Extra configuration

If using a static slot configuration, to control how many GPUs are assigned to each slot, use the SLOT_TYPE_<n> configuration syntax to specify Gpus, the same as would be done for Cpus or Memory. If not specified, slots default to GPUS=auto, which will assign GPUs proportionally to slots until there are no more GPUs to assign, and then it will assign 0 GPUs to the remaining slots. So a machine with NUM_CPUS=8 and DetectedGPUs="CUDA0, CUDA1, CUDA2, CUDA3" will assign 1 GPUs each to the first 4 slots, and no GPUs to the remaining slots. Slot ClassAds with GPUs assigned will include the following attributes:

CUDADeviceName="GeForce GTX 690"

When using Partitionable slots, by default the Partitionable slot will be assigned all GPUs. Dynamic slots created from the Partitionable slot will be assigned GPUs when the job requests them.

2. A job requests a GPU

User jobs that require a GPU must specify this requirement. In a job's submit description file, the simple request is

request_GPUs = 1

A more complex request, such as:

request_GPUs = 2
requirements = CUDARuntimeVersion >= 5.5 \
    && (CUDACapability >= 3.0) \
    && (CUDAGlobalMemoryMb >= 1500)

specifies that the job requires a CUDA GPU with at least 1500 Mb of memory, the CUDA runtime version 5.5 or later, and a CUDA Capability of 3.0 or greater.

3. Identify the GPU

Once a job matches to a given slot, it needs to know which GPUs to use, if multiple are present. GPUs that the job are permitted to use are specified as defined values for the slot ClassAd attribute AssignedGPUs. They are also published into the job's environment with variable _CONDOR_AssignedGPUs. In addition, if the configuration is defined with ENVIRONMENT_FOR_AssignedGPUs set, environment variables CUDA_VISIBLE_DEVICES and GPU_DEVICE_ORDINAL are published. The AssignedGPUs attribute value can be accessed if passed to the job as arguments using the $$() substitution macro syntax. For example, if the job takes an argument "--device=X" where X is the device to use, specify this in the submit description file with

arguments = "--device=$$(AssignedGPUs)"

Alternatively, the job might look to the environment variable _CONDOR_AssignedGPUs, or CUDA_VISIBLE_DEVICES.