ProcessAcceleratorCUDA.py

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import FWCore.ParameterSet.Config as cms

import os

from HeterogeneousCore.Common.PlatformStatus import PlatformStatus

class ProcessAcceleratorCUDA(cms.ProcessAccelerator):
    def __init__(self):
        super(ProcessAcceleratorCUDA, self).__init__()
        self._label = "gpu-nvidia"

    def labels(self):
        return [ self._label ]

    def enabledLabels(self):
        # Check if CUDA is available, and if the system has at least one usable device.
        # These should be checked on each worker node, because it depends both
        # on the architecture and on the actual hardware present in the machine.
        status = PlatformStatus(os.waitstatus_to_exitcode(os.system("cudaIsEnabled")))
        return self.labels() if status == PlatformStatus.Success else []

    def apply(self, process, accelerators):

        if self._label in accelerators:
            # Ensure that the CUDAService is loaded
            if not hasattr(process, "CUDAService"):
                from HeterogeneousCore.CUDAServices.CUDAService_cfi import CUDAService
                process.add_(CUDAService)

            # Propagate the CUDAService messages through the MessageLogger
            if not hasattr(process.MessageLogger, "CUDAService"):
                process.MessageLogger.CUDAService = cms.untracked.PSet()

        else:
            # Make sure the CUDAService is not loaded
            if hasattr(process, "CUDAService"):
                del process.CUDAService

            # Drop the CUDAService messages from the MessageLogger
            if hasattr(process.MessageLogger, "CUDAService"):
                del process.MessageLogger.CUDAService


# Ensure this module is kept in the configuration when dumping it
cms.specialImportRegistry.registerSpecialImportForType(ProcessAcceleratorCUDA, "from HeterogeneousCore.CUDACore.ProcessAcceleratorCUDA import ProcessAcceleratorCUDA")