我想使用Numbapro API在python中执行CUDA内核。我有这段代码：
import math
import numpy
from numbapro import jit, cuda, int32, float32
from matplotlib import pyplot
@cuda.jit('void(float32[:], float32[:], float32[:], float32[:], float32, float32, float32, int32)')
def calculate_velocity_field(X, Y, u_source, v_source, x_source, y_source, strength_source, N):
start = cuda.blockIdx.x * cuda.blockDim.x + cuda.threadIdx.x
end = N
stride = cuda.gridDim.x * cuda.blockDim.x
for i in range(start, end, stride):
u_source[i] = strength_source/(2*math.pi) * (X[i]-x_source)/((X[i]-x_source)**2 + (Y[i]-y_source)**2)
v_source[i] = strength_source/(2*math.pi) * (Y[i]-x_source)/((X[i]-x_source)**2 + (Y[i]-y_source)**2)
def main():
N = 200 # number of points in each direction
x_start, x_end = -4.0, 4.0 # boundaries in the x-direction
y_start, y_end = -2.0, 2.0 # boundaries in the y-direction
x = numpy.linspace(x_start, x_end, N) # creates a 1D-array with the x-coordinates
y = numpy.linspace(y_start, y_end, N) # creates a 1D-array with the y-coordinates
X, Y = numpy.meshgrid(x, y) # generates a mesh grid
strength_source = 5.0 # source strength
x_source, y_source = -1.0, 0.0 # location of the source
start = timer()
#calculate grid dimensions
blockSize = 1024
gridSize = int(math.ceil(float(N)/blockSize))
#transfer memory to device
X_d = cuda.to_device(X)
Y_d = cuda.to_device(Y)
u_source_d = cuda.device_array_like(X)
v_source_d = cuda.device_array_like(Y)
#launch kernel
calculate_velocity_field[gridSize,blockSize](X_d,Y_d,u_source_d,v_source_d,x_source,y_source,strength_source,N)
#transfer memory to host
u_source = numpy.empty_like(X)
v_source = numpy.empty_like(Y)
u_source_d.to_host(u_source)
v_source_d.to_host(v_source)
elapsed_time = timer() - start
print("Exec time with GPU %f s" % elapsed_time)
if __name__ == "__main__":
main()
给我这个错误：
NvvmError Traceback (most recent call last)
<ipython-input-17-85e4a6e56a14> in <module>()
----> 1 @cuda.jit('void(float32[:], float32[:], float32[:], float32[:], float32, float32, float32, int32)')
2 def calculate_velocity_field(X, Y, u_source, v_source, x_source, y_source, strength_source, N):
3 start = cuda.blockIdx.x * cuda.blockDim.x + cuda.threadIdx.x
4 end = N
5 stride = cuda.gridDim.x * cuda.blockDim.x
~/.anaconda3/lib/python3.4/site-packages/numba/cuda/decorators.py in kernel_jit(func)
89 # Force compilation for the current context
90 if bind:
---> 91 kernel.bind()
92
93 return kernel
~/.anaconda3/lib/python3.4/site-packages/numba/cuda/compiler.py in bind(self)
319 Force binding to current CUDA context
320 """
--> 321 self._func.get()
322
323 @property
~/.anaconda3/lib/python3.4/site-packages/numba/cuda/compiler.py in get(self)
254 cufunc = self.cache.get(device.id)
255 if cufunc is None:
--> 256 ptx = self.ptx.get()
257
258 # Link
~/.anaconda3/lib/python3.4/site-packages/numba/cuda/compiler.py in get(self)
226 arch = nvvm.get_arch_option(*cc)
227 ptx = nvvm.llvm_to_ptx(self.llvmir, opt=3, arch=arch,
--> 228 **self._extra_options)
229 self.cache[cc] = ptx
230 if config.DUMP_ASSEMBLY:
~/.anaconda3/lib/python3.4/site-packages/numba/cuda/cudadrv/nvvm.py in llvm_to_ptx(llvmir, **opts)
420 cu.add_module(llvmir.encode('utf8'))
421 cu.add_module(libdevice.get())
--> 422 ptx = cu.compile(**opts)
423 return ptx
424
~/.anaconda3/lib/python3.4/site-packages/numba/cuda/cudadrv/nvvm.py in compile(self, **options)
211 for x in opts])
212 err = self.driver.nvvmCompileProgram(self._handle, len(opts), c_opts)
--> 213 self._try_error(err, 'Failed to compile\n')
214
215 # get result
~/.anaconda3/lib/python3.4/site-packages/numba/cuda/cudadrv/nvvm.py in _try_error(self, err, msg)
229
230 def _try_error(self, err, msg):
--> 231 self.driver.check_error(err, "%s\n%s" % (msg, self.get_log()))
232
233 def get_log(self):
~/.anaconda3/lib/python3.4/site-packages/numba/cuda/cudadrv/nvvm.py in check_error(self, error, msg, exit)
118 sys.exit(1)
119 else:
--> 120 raise exc
121
122
NvvmError: Failed to compile
libnvvm : error: -arch=compute_52 is an unsupported option
NVVM_ERROR_INVALID_OPTION
我尝试了另一个numbapro示例和相同的错误ocurrs。 我不知道是不是Numbapro的bug不支持5.2计算能力，或者它是Nvidia NVVM的问题...建议？
理论上it should be supported，但我不知道发生了什么。
我正在使用Linux和CUDA 7.0以及驱动程序版本346.29
答案 0 :(得分：3)
最后我找到了解决方案here
conda update cudatoolkit
make
看起来我更新CUDA工具包不会更新到CUDA 7.0。可以做第二个解决方案：
conda install -c numba cudatoolkit
Fetching package metadata: .... # All requested packages already installed. # packages in environment at ~/.anaconda3: # cudatoolkit 6.0 p0
在：
Fetching package metadata: ......
Solving package specifications: .
Package plan for installation in environment ~/.anaconda3:
The following packages will be downloaded:
package | build
---------------------------|-----------------
cudatoolkit-7.0 | 1 190.8 MB
The following packages will be UPDATED:
cudatoolkit: 6.0-p0 --> 7.0-1
Proceed ([y]/n)? y
后：
In [4]: check_cuda()
------------------------------libraries detection-------------------------------
Finding cublas
located at ~/.anaconda3/lib/libcublas.so.6.0.37
trying to open library... ok
Finding cusparse
located at ~/.anaconda3/lib/libcusparse.so.6.0.37
trying to open library... ok
Finding cufft
located at ~/.anaconda3/lib/libcufft.so.6.0.37
trying to open library... ok
Finding curand
located at ~/.anaconda3/lib/libcurand.so.6.0.37
trying to open library... ok
Finding nvvm
located at ~/.anaconda3/lib/libnvvm.so.2.0.0
trying to open library... ok
finding libdevice for compute_20... ok
finding libdevice for compute_30... ok
finding libdevice for compute_35... ok
-------------------------------hardware detection-------------------------------
Found 1 CUDA devices
id 0 b'GeForce GTX 970' [SUPPORTED]
compute capability: 5.2
pci device id: 0
pci bus id: 7
Summary:
1/1 devices are supported
PASSED
Out[4]: True