我已经开始使用OpenCL进行编程，到目前为止我使用的是pyopencl软件包，这对于简单计算非常有用，但很难计算出更高级的东西。想法是使用pyViennaCL，高级替代。我已经安装了ViennaCL和pyOpenCL（通过pip），我也是pip-ed pyViennaCL。当我从ViennaCL页面运行官方示例时：
#!/usr/bin/python
# Import PyViennaCL and NumPy
import pyviennacl as p
import numpy as np
# Create our datastructures on the host
x = [1.0, 2.0, 3.0, 4.0, 5.0] # We can create PyViennaCL Vectors from lists
a = np.array([[1.0, 2.0, 3.0],
[0.0, 3.0, 4.0],
[0.0, 0.0, 5.0]]) # We can create PyViennaCL Matrices from arrays
# Create corresponding ViennaCL datastructures on the compute device
y = p.Vector(x)
b = p.Matrix(a) # This is a dense matrix
# Copy the data back to the host and check that it's equal
z = y.value # z is now a 1-D numpy array with dtype float64
c = b.value # c is now a 2-D numpy array with dtype float64
if (z == x).all() and (c == a).all():
print("Successfully transferred data to and from the compute device!")
# We can modify elements of the ViennaCL device structures, but since this
# incurs a compute kernel initialisation and buffer transfer, it is very slow!
#y[0] = 0.0
#b[2, 2] = -1.0
#x[0] = 0.0 # We should also modify our original data to keep track..
#a[2, 2] = -1.0
# And we can do comparisons seamlessly between NumPy and PyViennaCL types!
#if (a == b).all() and (x == y).all():
# print("Successfully modified data structures on host and device!")
# We also need to be sure that we are consistent with respect to the data-types
# we use. For instance, we should not mix integer and floating point types.
#
# By default, PyViennaCL objects inherit the dtype of the objects from which
# they are created, or (if that is ambiguous), float64.
print("a and b have dtypes of %s and %s" %
(np.result_type(a), np.result_type(b)))
# PyViennaCL makes an effort to convert objects to the appropriate dtype where
# dtypes have been mixed, but this is often ambiguous and unpredictable, and
# so it is recommended that users make efforts to keep they dtypes consistent.
i = 1L # Create a long integer
print("i has dtype of %s" % (np.result_type(i)))
y[0] = i # Remember, this sort of elementwise assignation is *very slow*!
print("y has values %s and dtype %s" %
(y, np.result_type(y)))
# And, of course, we can perform basic arithemetic operations with PyViennaCL,
# mixing native Python types with NumPy and PyViennaCL types:
z = (x + y + z) / 2.0
print("z is now of type %s, dtype %s, and with values %s"
% (type(z), np.result_type(z), z))
# Notice that z has `Div' type. This is because the z object represents the
# arithmetic expression `(x + y + z) / 2.0', and this is only computed when
# the result is needed, in order to maximise performance.
# And we can do less basic arithmetic!
print("The sine of the values of z is %s" %
p.sin(z))
# PyViennaCL exposes many elementwise mathematical functions.
# See help(p.math) for more information.
我得到了这个输出：
Successfully transferred data to and from the compute device!
a and b have dtypes of float64 and float64
i has dtype of int64
y has values [ 1. 2. 3. 4. 5.] and dtype float64
z is now of type <class 'pyviennacl.pycore.Div'>, dtype float64, and with values [ 1.5 3. 4.5 6. 7.5]
Stack dump:
0. Running pass 'Intel OpenCL Vectorizer' on module 'main'.
1. Running pass 'Intel OpenCL VectorizerCore' on function '@__Vectorized_.element_op'
2. Running pass 'WeightedInstCounter' on function '@__Vectorized_.element_op'
Segmentation fault (core dumped)
出了什么问题？