Dear Lev, dear all,
the problem with DataTypeError I have solved. I did not mention that I
pass float64 instead of float32.
In attach you will find the code, it works......but....GPU brings wrong
solution. I print the results in program. They dont match.
Could you explain why?
I read the issues on the scikits.cuda to provide the corrections...but
it did not help.
Best regards,
Evgeny
Am 24.02.2014 23:23, schrieb Lev Givon:
Received from Evgeny Lazutkin on Mon, Feb 24, 2014 at 05:08:43PM EST:
(snip)
So, I did the following:
# Transfer to GPU
a_gpu = pycuda.gpuarray.to_gpu(A)
b_gpu = pycuda.gpuarray.to_gpu(B)
#pointer
p1 = pycuda.gpuarray.GPUArray(a_gpu, shape(A)).ptr()
and ot raises the error - in gpuarray.py in __init__
dtype = np.dtype(dtype)
TypeError: data type not understood
pycuda.gpuarray.to_gpu() creates a GPUArray instance; not sure why you are
passing it to the GPUArray constructor. Also, note that since ptr is a property,
it shouldn't be invoked as a method.
There are examples of how to use pycuda with wrapped library functions in
scikits.cuda in the docstrings of some of the wrapper functions (e.g., see
scikits/cuda/cublas.py).
import pycuda.driver as drv
import pycuda.tools
import pycuda.autoinit
import numpy
import pycuda.gpuarray as gpuarray
from scikits.cuda.cula import *
from scikits import *
from scipy import *
A = array([[ 5.18649167 , 0. , 1.03279556 , 0. , -0.14549722, 0.
, 0. , 0. , 0. , 0. , 0. , 0. ],
[-5.0819889 , 0., 2.52459667, 0., 0.64549722 , 0. ,
0. , 0., 0. , 0. , 0. , 0. ],
[ 9.01848057, 0. , -8.13118224, 0. , 5.18649167, 0. ,
0. , 0., 0. , 0. , 0. , 0. ],
[-0.5 , 4.43649167, 0. , 1.03279556, 0. , -0.14549722,
0. , 0. , 0. , 0. , 0. , 0. ],
[ 0. , -5.0819889 ,-0.5, 1.77459667 , 0. , 0.64549722,
0. , 0. , 0. , 0. , 0. , 0. ],
[ 0. , 9.01848057 ,0., -8.13118224, -0.5 , 4.43649167,
0. , 0. , 0. , 0. , 0. , 0. ],
[ 0. , 0. ,0., 0. , 0. , 0. ,
5.18649167 , 0. , 1.03279556 , 0. , -0.14549722 , 0. ],
[ 0. , 0. ,0., 0. , 0. , 0. ,
-5.0819889 , 0. , 2.52459667 , 0. , 0.64549722 , 0. ],
[ 0. , 0. ,0., 0. , 0., 0. ,
9.01848057 , 0. , -8.13118224 , 0. , 5.18649167 , 0. ],
[ 0. , 0. , 0. , 0. , 0. , 0. ,
-0.5 , 4.43649167, 0. , 1.03279556 , 0. , -0.14549722] ,
[ 0. , 0. , 0. , 0. , 0. , 0. ,
0. , -5.0819889 , -0.5 , 1.77459667 , 0. , 0.64549722] ,
[ 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 9.01848057 , 0. , -8.13118224, -0.5 , 4.43649167]], dtype =
numpy.float32)
B = array([[-5.32379001 , 0. , 0., 0. ],
[ 2.661895 , 0. , 0., 0. ],
[-5.32379001, 0. , 0. , 0. ],
[ 0. , -5.32379001, 0. , 0. ],
[ 0. , 2.661895 , 0. , 0. ],
[ 0. , -5.32379001, 0. , 0. ],
[ 0. , 0. , -5.32379001, 0. ],
[ 0. , 0. , 2.661895 , 0. ],
[ 0. , 0., -5.32379001, 0. ],
[ 0. , 0., 0. , -5.32379001],
[ 0. , 0., 0. , 2.661895 ],
[ 0. , 0., 0. , -5.32379001]], dtype = numpy.float32)
def cpu_solve(k_,y_):
start=drv.Event()
end=drv.Event()
start.record()
result = np.linalg.solve(k_, y_)
end.record()
end.synchronize()
print "numpy array time: %fs" %(start.time_till(end)*1e-3)
return result
def gpu_solve(k_,y_):
k_gpu=gpuarray.to_gpu(k_)
y_gpu=gpuarray.to_gpu(y_)
(m,n)=k_.shape
lda=m
nrhs=shape(y_)[1]
ipiv=np.empty(n,dtype=np.int32)
ipiv_gpu=gpuarray.to_gpu(ipiv)
ldb=y_.shape[0]
start=drv.Event()
end=drv.Event()
start.record()
culaDeviceSgesv(n, nrhs, k_gpu.ptr, lda, ipiv_gpu.ptr, y_gpu.ptr, ldb)
end.record()
end.synchronize()
print t
print "GPU array time: %fs" %(start.time_till(end)*1e-3)
return y_gpu
culaInitialize()
print cpu_solve(A,B) # check: correct? ---> YES.
print gpu_solve(A,B) # check: corretc? ---> NO!
culaShutdown()
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