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numbapro python で cuda カーネルを実行しようとしていますが、リソース不足エラーが発生し続けます。次に、カーネルをループして実行し、より小さい配列を送信しようとしましたが、それでも同じエラーが発生しました。

これが私のエラーメッセージです：

Traceback (most recent call last):
File "./predict.py", line 418, in <module>
predict[griddim, blockdim, stream](callResult_d, catCount, numWords, counts_d, indptr_d, indices_d, probtcArray_d, priorC_d)
File "/home/mhagen/Developer/anaconda/lib/python2.7/site-packages/numba/cuda/compiler.py", line 228, in __call__
sharedmem=self.sharedmem)
File "/home/mhagen/Developer/anaconda/lib/python2.7/site-packages/numba/cuda/compiler.py", line 268, in _kernel_call
cu_func(*args)
File "/home/mhagen/Developer/anaconda/lib/python2.7/site-packages/numba/cuda/cudadrv/driver.py", line 1044, in __call__
self.sharedmem, streamhandle, args)
File "/home/mhagen/Developer/anaconda/lib/python2.7/site-packages/numba/cuda/cudadrv/driver.py", line 1088, in launch_kernel
None)
File "/home/mhagen/Developer/anaconda/lib/python2.7/site-packages/numba/cuda/cudadrv/driver.py", line 215, in safe_cuda_api_call
self._check_error(fname, retcode)
File "/home/mhagen/Developer/anaconda/lib/python2.7/site-packages/numba/cuda/cudadrv/driver.py", line 245, in _check_error
raise CudaAPIError(retcode, msg)
numba.cuda.cudadrv.driver.CudaAPIError: Call to cuLaunchKernel results in CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES

ここに私のソースコードがあります:

from numbapro.cudalib import cusparse
from numba import *
from numbapro import cuda

@cuda.jit(argtypes=(double[:], int64, int64, double[:], int64[:], int64[:], double[:,:], double[:] ))
def predict( callResult, catCount, wordCount, counts, indptr, indices, probtcArray, priorC ):

    i = cuda.threadIdx.x + cuda.blockIdx.x * cuda.blockDim.x

    correct = 0
    wrong = 0
    lastDocIndex = -1
    maxProb = -1e6
    picked = -1

    for cat in range(catCount):

        probSum = 0.0

        for j in range(indptr[i],indptr[i+1]):
            wordIndex = indices[j]
            probSum += (counts[j]*math.log(probtcArray[cat,wordIndex]))

        probSum += math.log(priorC[cat])
        if probSum > maxProb:
            maxProb = probSum
            picked = cat

    callResult[i] = picked

predictions = []
counter = 1000
for i in range(int(math.ceil(numDocs/(counter*1.0)))):
    docTestSliceList = docTestList[i*counter:(i+1)*counter]
    numDocsSlice = len(docTestSliceList)
    docTestArray = np.zeros((numDocsSlice,numWords))
    for j,doc in enumerate(docTestSliceList):
        for ind in doc:
            docTestArray[j,ind['term']] = ind['count']
    docTestArraySparse = cusparse.ss.csr_matrix(docTestArray)

    start = time.time()
    OPT_N = numDocsSlice
    blockdim = 1024, 1
    griddim = int(math.ceil(float(OPT_N)/blockdim[0])), 1 

    catCount = len(music_categories)
    callResult = np.zeros(numDocsSlice)
    stream = cuda.stream()
    with stream.auto_synchronize():
        probtcArray_d = cuda.to_device(numpy.asarray(probtcArray),stream)
        priorC_d = cuda.to_device(numpy.asarray(priorC),stream)
        callResult_d = cuda.to_device(callResult, stream)
        counts_d = cuda.to_device(docTestArraySparse.data, stream)
        indptr_d = cuda.to_device(docTestArraySparse.indptr, stream)
        indices_d = cuda.to_device(docTestArraySparse.indices, stream)
        predict[griddim, blockdim, stream](callResult_d, catCount, numWords, counts_d, indptr_d, indices_d, probtcArray_d, priorC_d)

        callResult_d.to_host(stream)
    #stream.synchronize()
    predictions += list(callResult)

    print "prediction %d: %f" % (i,time.time()-start)
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1 に答える 1

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これはcuda手順にあることがわかりました。

予測を呼び出すと、blockdim は 1024 に設定されます。

しかし、プロシージャは 1024 ではなく 1000 要素のスライス サイズで繰り返し呼び出されます。したがって、プロシージャでは、戻り配列の境界外にある 24 個の要素を書き込もうとします。

要素数パラメーター (n_el) を送信し、cuda プロシージャーでエラー チェック呼び出しを配置すると、問題が解決します。

@cuda.jit(argtypes=(double[:], int64, int64, int64, double[:], int64[:], int64[:], double[:,:], double[:] ))
def predict( callResult, n_el, catCount, wordCount, counts, indptr, indices, probtcArray, priorC ):

     i = cuda.threadIdx.x + cuda.blockIdx.x * cuda.blockDim.x

     if i < n_el:

         ....
于 2014-11-17T18:01:17.543 に答える