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。当输入一个 size(50,27) 的数组时，它运行成功并得到正确的结果。我将输入数据更改为 size(200,340)，它有一个错误。
我在我的代码中使用共享内存。是否没有足够的共享内存？还是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。
我应该怎么做才能解决这个问题？感谢您的一些建议。
我简化了我的代码，它有同样的错误。在这里设置输入数据的大小很方便：df = np.random.random_sample((300, 200)) + 10.
编码：
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