90 lines
3.4 KiB
Python
90 lines
3.4 KiB
Python
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# Re export
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import sys
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from numba.cuda import cg
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from .stubs import (threadIdx, blockIdx, blockDim, gridDim, laneid, warpsize,
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syncwarp, shared, local, const, atomic,
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shfl_sync_intrinsic, vote_sync_intrinsic, match_any_sync,
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match_all_sync, threadfence_block, threadfence_system,
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threadfence, selp, popc, brev, clz, ffs, fma, cbrt,
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activemask, lanemask_lt, nanosleep, fp16,
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_vector_type_stubs)
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from .intrinsics import (grid, gridsize, syncthreads, syncthreads_and,
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syncthreads_count, syncthreads_or)
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from .cudadrv.error import CudaSupportError
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from numba.cuda.cudadrv.driver import (BaseCUDAMemoryManager,
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HostOnlyCUDAMemoryManager,
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GetIpcHandleMixin, MemoryPointer,
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MappedMemory, PinnedMemory, MemoryInfo,
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IpcHandle, set_memory_manager)
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from numba.cuda.cudadrv.runtime import runtime
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from .cudadrv import nvvm
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from numba.cuda import initialize
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from .errors import KernelRuntimeError
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from .decorators import jit, declare_device
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from .api import *
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from .api import _auto_device
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from .args import In, Out, InOut
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from .intrinsic_wrapper import (all_sync, any_sync, eq_sync, ballot_sync,
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shfl_sync, shfl_up_sync, shfl_down_sync,
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shfl_xor_sync)
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from .kernels import reduction
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reduce = Reduce = reduction.Reduce
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# Expose vector type constructors and aliases as module level attributes.
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for vector_type_stub in _vector_type_stubs:
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setattr(sys.modules[__name__], vector_type_stub.__name__, vector_type_stub)
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for alias in vector_type_stub.aliases:
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setattr(sys.modules[__name__], alias, vector_type_stub)
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del vector_type_stub, _vector_type_stubs
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def is_available():
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"""Returns a boolean to indicate the availability of a CUDA GPU.
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This will initialize the driver if it hasn't been initialized.
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"""
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# whilst `driver.is_available` will init the driver itself,
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# the driver initialization may raise and as a result break
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# test discovery/orchestration as `cuda.is_available` is often
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# used as a guard for whether to run a CUDA test, the try/except
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# below is to handle this case.
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driver_is_available = False
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try:
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driver_is_available = driver.driver.is_available
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except CudaSupportError:
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pass
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return driver_is_available and nvvm.is_available()
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def is_supported_version():
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"""Returns True if the CUDA Runtime is a supported version.
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Unsupported versions (e.g. newer versions than those known to Numba)
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may still work; this function provides a facility to check whether the
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current Numba version is tested and known to work with the current
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runtime version. If the current version is unsupported, the caller can
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decide how to act. Options include:
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- Continuing silently,
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- Emitting a warning,
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- Generating an error or otherwise preventing the use of CUDA.
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"""
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return runtime.is_supported_version()
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def cuda_error():
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"""Returns None if there was no error initializing the CUDA driver.
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If there was an error initializing the driver, a string describing the
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error is returned.
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"""
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return driver.driver.initialization_error
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initialize.initialize_all()
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