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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 is:

  1. Advertise the GPU: configure HTCondor so that execute nodes include information about available GPUs in their machine ClassAd.
  2. Job requests a GPU: jobs identify their request for a GPU and specify any further specific requirements of the GPU, in order to acquire a suitable GPU
  3. Identify the GPU: Jobs modify their arguments or environment to learn which GPU they may use.

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. The HTCondor condor_gpu_discovery tool is designed to assist in detecting GPUs and in providing configuration 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 mechanism is used to define a resource. These examples use the resource type name GPUs. These resource type names are case insensitive, but all characters within the name are significant, so be consistent.

Define GPUs as a custom resource by adding the following definitions to the configuration of the execute node. 

    MACHINE_RESOURCE_GPUs = $(LIBEXEC)/condor_gpu_discovery -properties
    ENVIRONMENT_FOR_AssignedGPUs = CUDA_VISIBLE_DEVICES, GPU_DEVICE_ORDINAL

MACHINE_RESOURCE_GPUs tells HTCondor to run the condor_gpu_discovery tool, and use its output to define a custom resource called 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 CUDA_VISIBLE_DEVICES and GPU_DEVICE_ORDINAL. If you know for certain that your devices will be CUDA, then you can omit GPU_DEVICE_ORDINAL in the configuration above. If you know for certain that your devices are OpenCL only, then you can omit CUDA_VISIBLE_DEVICES. In addition, AssignedGPUs will always be published into the job's environment as _CONDOR_AssignedGPUs, so the second line above is not strictly necessary, but it is recommended.

The output of the condor_gpu_discovery tool will report DetectedGPUs and list 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 command above tells condor_gpu_discovery to also list significant attributes of the device(s). These attributes will then be published in the slot ClassAds.

Here is typical output of condor_gpu_discovery 

> condor_gpu_discovery -properties
DetectedGPUs="CUDA0, CUDA1, CUDA2, CUDA3"
CUDACapability=3.0
CUDADeviceName="GeForce GTX 690"
CUDADriverVersion=5.50
CUDAECCEnabled=false
CUDAGlobalMemory=2048
CUDARuntimeVersion=5.0

This output indicates that 4 GPUs were detected, all of which have the same properties. If you are using a static slot configuration, and you wish to control how many GPUs are assigned to each slot, use the SLOT_TYPE_<n> configuration syntax to specify Gpus, the same as you would for Cpus or Memory. If you don't specify, slots default to GPUS=auto which will assign GPUs proportionally to slots until there are no more GPUs to assign, then it will simply 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. Slots with GPUs assigned with include the following attributes. 

Cpus=1
GPUs=1
TotalCpus=8
TotalGPUs=4
TotalSlotCpus=1
TotalSlotGPUs=1
CUDACapability=3.0
CUDADeviceName="GeForce GTX 690"
CUDADriverVersion=5.50
CUDAECCEnabled=false
CUDAGlobalMemory=2048
CUDARuntimeVersion=5.0

If you are using a Partitionable slot, the by default the Partitionable slot will be assigned both GPUs. Dynamic slots created from this partitionable slot will be assigned GPUs when the job requests them.

Job requests a GPU

User jobs that require a GPU must specify this requirement. In a job's submit description file, it might do something as simple as 

Request_GPUs = 1

or as complex as 

Request_GPUs = 2
Requirements = CUDARuntimeVersion >= 5.5 \
    && (CUDACapability >= 3.0) \
    && (CUDAGlobalMemoryMb >= 1500)

specifying 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.

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 in the slot ClassAd as AssignedGPUs. They are also published into the job's environment as _CONDOR_AssignedGPUs, and (if the configuration at the top is used) as CUDA_VISIBLE_DEVICES and GPU_DEVICE_ORDINAL. You can pass Assigned GPUs to your jobs arguments using the $$() syntax. For example, if your job takes an argument "--device=X" where X is the device to use, you might do something like 

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

Or your job might look to the environment variable _CONDOR_AssignedGPUs, or CUDA_VISIBLE_DEVICES