CUDA错误当大数组用作输入数据时

时间:2017-04-12 10:18:07

标签: python cuda numba numba-pro

我有一个代码可以通过python3.5使用numba和CUDA8.0在GPU中进行一些计算。输入大小为(50,27)的数组时,它会成功运行并获得正确的结果。我将输入数据更改为大小(200,340),它有一个错误。

我在代码中使用共享内存。没有足够的共享内存吗?或者网格大小和块大小不好?我不知道如何识别它并为网格和块选择合适的大小。

我设置了小网格大小和块大小,错误是一样的。

我该怎么做才能解决这个问题?谢谢你的一些建议。

我简化了我的代码并且它有相同的错误。在此处设置输入数据的大小很方便:df = np.random.random_sample((300, 200)) + 10

代码:

import os,sys,time,math
import pandas as pd
import numpy as np

from numba import cuda, float32

os.environ['NUMBAPRO_NVVM']=r'D:\NVIDIA GPU Computing Toolkit\CUDA\v8.0\nvvm\bin\nvvm64_31_0.dll'
os.environ['NUMBAPRO_LIBDEVICE']=r'D:\NVIDIA GPU Computing Toolkit\CUDA\v8.0\nvvm\libdevice'

bpg = 8
tpb = (4,32) 

tsize = (3,4) 
hsize = (1,4)

@cuda.jit
def calcu_T(D, T):

    gw = cuda.gridDim.x
    bx = cuda.blockIdx.x
    tx = cuda.threadIdx.x
    bw = cuda.blockDim.x
    ty = cuda.threadIdx.y
    bh = cuda.blockDim.y

    c_num = D.shape[1]
    c_index = bx

    while c_index<c_num*c_num:
        c_x = int(c_index/c_num)
        c_y = c_index%c_num

        if c_x==c_y:
            T[c_x,c_y] = 0.0
        else:
            X = D[:,c_x]
            Y = D[:,c_y]

            hbuf = cuda.shared.array(hsize, float32)

            h = tx

            Xi = X[h:]
            Xi1 = X[:-h]
            Yih = Y[:-h]

            sbuf = cuda.shared.array(tsize, float32)

            L = len(Xi)

            #mean
            if ty==0:
                Xi_m = 0.0
                Xi1_m = 0.0
                Yih_m = 0.0
                for i in range(L):
                    Xi_m += Xi[i]
                    Xi1_m += Xi1[i]
                    Yih_m += Yih[i]
                Xi_m = Xi_m/L
                Xi1_m = Xi1_m/L
                Yih_m = Yih_m/L
                sbuf[0,tx] = Xi_m
                sbuf[1,tx] = Xi1_m
                sbuf[2,tx] = Yih_m

            cuda.syncthreads()

            sl = cuda.shared.array(tpb, float32)

            r_index = ty
            s_l = 0.0
            while r_index<L:
                s1 = 0.0
                for i in range(L):
                    s1 += (Xi[r_index]+Xi1[i])/sbuf[0,tx]

                s_l += s1
                r_index +=bh
            sl[tx,ty] = s_l
            cuda.syncthreads()

            #
            if ty==0:
                ht = 0.0
                for i in range(bh):
                    ht += sl[tx,i]
                hbuf[0,tx] = ht/L
            cuda.syncthreads()

            #max
            if tx==0 and ty==0:
                m_t = 0.0
                for index,ele in enumerate(hbuf[0]):
                    if index==0:
                        m_t = ele
                    elif ele>m_t:
                        m_t = ele

                T[c_x,c_y] = m_t

        c_index +=gw



df = np.random.random_sample((300, 200)) + 10
D = np.array(df, dtype=np.float32)
r,c = D.shape

T = np.empty([c,c])

dD = cuda.to_device(D)
dT = cuda.device_array_like(T)

calcu_T[bpg, tpb](dD,dT)
dT.copy_to_host(T)

错误:

Traceback (most recent call last):
      File "G:\myworkspace\python3.5\forte\forte170327\test10fortest8.py", line 118, in <module>
        dT.copy_to_host(T)
      File "D:\python3.5.3\lib\site-packages\numba\cuda\cudadrv\devicearray.py", line 198, in copy_to_host
        _driver.device_to_host(hostary, self, self.alloc_size, stream=stream)
      File "D:\python3.5.3\lib\site-packages\numba\cuda\cudadrv\driver.py", line 1481, in device_to_host
        fn(host_pointer(dst), device_pointer(src), size, *varargs)
      File "D:\python3.5.3\lib\site-packages\numba\cuda\cudadrv\driver.py", line 259, in safe_cuda_api_call
        self._check_error(fname, retcode)
      File "D:\python3.5.3\lib\site-packages\numba\cuda\cudadrv\driver.py", line 296, in _check_error
        raise CudaAPIError(retcode, msg)
    numba.cuda.cudadrv.driver.CudaAPIError: [719] Call to cuMemcpyDtoH results in UNKNOWN_CUDA_ERROR

我的设备信息:

Device 0: 
  CUDA Driver Version / Runtime Version          8.0 / 8.0
  CUDA Capability Major/Minor version number:    5.0
  Total amount of global memory:                 2048 MBytes (2147483648 bytes)
  ( 5) Multiprocessors, (128) CUDA Cores/MP:     640 CUDA Cores
  Maximum Texture Dimension Size (x,y,z)         1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
  Maximum Layered 1D Texture Size, (num) layers  1D=(16384), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(16384, 16384), 2048 layers
  Total amount of constant memory:               65536 bytes
  Total amount of shared memory per block:       49152 bytes
  Total number of registers available per block: 65536
  Warp size:                                     32
  Maximum number of threads per multiprocessor:  2048
  Maximum number of threads per block:           1024
  Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
  Max dimension size of a grid size    (x,y,z): (2147483647, 65535, 65535)
  Maximum memory pitch:                          2147483647 bytes
  Texture alignment:                             512 bytes

1 个答案:

答案 0 :(得分:3)

您的代码没有任何问题。如果我在GTX970上运行你的代码,我会得到这个:

In [11]: main??
Signature: main()
Source:   
def main():

    df = np.random.random_sample((300, 200)) + 10
    D = np.array(df, dtype=np.float32)
    r,c = D.shape

    T = np.empty([c,c])

    dD = cuda.to_device(D)
    dT = cuda.device_array_like(T)

    calcu_T[bpg, tpb](dD,dT)
    dT.copy_to_host(T)
File:      ~/SO/crash.py
Type:      function

In [12]: %timeit -n 3 -r 3 main()
3 loops, best of 3: 6.61 s per loop

即。没有运行时错误,但包含内核的python代码需要6.6秒才能运行。如果我使用CUDA探查器分析代码:

$ nvprof python crash.py

==13828== NVPROF is profiling process 13828, command: python crash.py
All finished
==13828== Profiling application: python crash.py
==13828== Profiling result:
Time(%)      Time     Calls       Avg       Min       Max  Name
100.00%  6.59109s         1  6.59109s  6.59109s  6.59109s  cudapy::__main__::calcu_T$241(Array<float, int=2, A, mutable, aligned>, Array<double, int=2, A, mutable, aligned>)
  0.00%  26.271us         1  26.271us  26.271us  26.271us  [CUDA memcpy DtoH]
  0.00%  21.279us         1  21.279us  21.279us  21.279us  [CUDA memcpy HtoD]

==13828== API calls:
Time(%)      Time     Calls       Avg       Min       Max  Name
 98.51%  6.59118s         1  6.59118s  6.59118s  6.59118s  cuMemcpyDtoH
  1.42%  94.890ms         1  94.890ms  94.890ms  94.890ms  cuDevicePrimaryCtxRetain
  0.05%  3.4116ms         1  3.4116ms  3.4116ms  3.4116ms  cuModuleLoadDataEx
  0.01%  417.96us         1  417.96us  417.96us  417.96us  cuLinkCreate
  0.00%  227.57us         1  227.57us  227.57us  227.57us  cuLinkAddData
  0.00%  195.72us         2  97.859us  95.710us  100.01us  cuMemAlloc
  0.00%  190.10us         1  190.10us  190.10us  190.10us  cuLinkComplete
  0.00%  139.04us         1  139.04us  139.04us  139.04us  cuMemGetInfo
  0.00%  53.193us         1  53.193us  53.193us  53.193us  cuMemcpyHtoD
  0.00%  29.538us         1  29.538us  29.538us  29.538us  cuDeviceGetName
  0.00%  17.895us         1  17.895us  17.895us  17.895us  cuLaunchKernel
  0.00%  2.0250us         1  2.0250us  2.0250us  2.0250us  cuCtxPushCurrent
  0.00%  2.0150us         5     403ns     255ns     752ns  cuFuncGetAttribute
  0.00%  1.6260us         2     813ns     547ns  1.0790us  cuDeviceGetCount
  0.00%  1.1430us         1  1.1430us  1.1430us  1.1430us  cuModuleGetFunction
  0.00%     951ns         2     475ns     372ns     579ns  cuDeviceGet
  0.00%     796ns         1     796ns     796ns     796ns  cuLinkDestroy
  0.00%     787ns         1     787ns     787ns     787ns  cuDeviceComputeCapability

你可以看到你发布的内核需要6.5秒才能运行。

您没有提供任何详细信息,但我猜您在Windows上运行,您的GPU是显示GPU,您的代码运行速度足够慢,以至于它达到了WDDM显示管理器监视程序超时限制。这是非常详细的记录,之前已被问过数百次 - 例如here

您选择的搜索引擎和CUDA Windows入门指南将为您提供有关从操作系统和硬件角度改善情况的替代方案的信息。然而,最明显的是改进代码以使其运行更快。