If it helps, I have similar problems on a TeslaC2050 and the latest pycuda
against CUDA3.1. Here is my test output:
garr...@alienbox:~/src/PyCUDA/pycuda/test$ python test_gpuarray.py
============================= test session starts
==============================
python: platform linux2 -- Python 2.6.5 -- pytest-1.2.1
test object 1: test_gpuarray.py
test_gpuarray.py ...F......F.....F................F.
=================================== FAILURES
===================================
___________________________ TestGPUArray.test_minmax
___________________________
def f(*args, **kwargs):
import pycuda.driver
# appears to be idempotent, i.e. no harm in calling it more than
once
pycuda.driver.init()
ctx = make_default_context()
try:
assert isinstance(ctx.get_device().name(), str)
assert isinstance(ctx.get_device().compute_capability(), tuple)
assert isinstance(ctx.get_device().get_attributes(), dict)
> inner_f(*args, **kwargs)
/usr/local/lib/python2.6/dist-packages/pycuda-0.94.1-py2.6-linux-x86_64.egg/pycuda/tools.py:503:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _
self = <test_gpuarray.TestGPUArray instance at 0x1e65c20>
@mark_cuda_test
def test_minmax(self):
from pycuda.curandom import rand as curand
if has_double_support():
dtypes = [numpy.float64, numpy.float32, numpy.int32]
else:
dtypes = [numpy.float32, numpy.int32]
for what in ["min", "max"]:
for dtype in dtypes:
a_gpu = curand((200000,), dtype)
a = a_gpu.get()
op_a = getattr(numpy, what)(a)
op_a_gpu = getattr(gpuarray, what)(a_gpu).get()
> assert op_a_gpu == op_a, (op_a_gpu, op_a, dtype, what)
E AssertionError: (array(5.4287724196910858e-05,
dtype=float32), 2.5250483e-06, <type 'numpy.float32'>, 'min')
test_gpuarray.py:450: AssertionError
_______________________ TestGPUArray.test_subset_minmax
________________________
def f(*args, **kwargs):
import pycuda.driver
# appears to be idempotent, i.e. no harm in calling it more than
once
pycuda.driver.init()
ctx = make_default_context()
try:
assert isinstance(ctx.get_device().name(), str)
assert isinstance(ctx.get_device().compute_capability(), tuple)
assert isinstance(ctx.get_device().get_attributes(), dict)
> inner_f(*args, **kwargs)
/usr/local/lib/python2.6/dist-packages/pycuda-0.94.1-py2.6-linux-x86_64.egg/pycuda/tools.py:503:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _
self = <test_gpuarray.TestGPUArray instance at 0x1f9c878>
@mark_cuda_test
def test_subset_minmax(self):
from pycuda.curandom import rand as curand
l_a = 200000
gran = 5
l_m = l_a - l_a // gran + 1
if has_double_support():
dtypes = [numpy.float64, numpy.float32, numpy.int32]
else:
dtypes = [numpy.float32, numpy.int32]
for dtype in dtypes:
a_gpu = curand((l_a,), dtype)
a = a_gpu.get()
meaningful_indices_gpu = gpuarray.zeros(l_m, dtype=numpy.int32)
meaningful_indices = meaningful_indices_gpu.get()
j = 0
for i in range(len(meaningful_indices)):
meaningful_indices[i] = j
j = j + 1
if j % gran == 0:
j = j + 1
meaningful_indices_gpu = gpuarray.to_gpu(meaningful_indices)
b = a[meaningful_indices]
min_a = numpy.min(b)
min_a_gpu = gpuarray.subset_min(meaningful_indices_gpu,
a_gpu).get()
> assert min_a_gpu == min_a
E assert array(0.00024149427190423012, dtype=float32) ==
5.105976e-06
test_gpuarray.py:484: AssertionError
____________________________ TestGPUArray.test_sum
_____________________________
def f(*args, **kwargs):
import pycuda.driver
# appears to be idempotent, i.e. no harm in calling it more than
once
pycuda.driver.init()
ctx = make_default_context()
try:
assert isinstance(ctx.get_device().name(), str)
assert isinstance(ctx.get_device().compute_capability(), tuple)
assert isinstance(ctx.get_device().get_attributes(), dict)
> inner_f(*args, **kwargs)
/usr/local/lib/python2.6/dist-packages/pycuda-0.94.1-py2.6-linux-x86_64.egg/pycuda/tools.py:503:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _
self = <test_gpuarray.TestGPUArray instance at 0x1fa9dd0>
@mark_cuda_test
def test_sum(self):
from pycuda.curandom import rand as curand
a_gpu = curand((200000,))
a = a_gpu.get()
sum_a = numpy.sum(a)
from pycuda.reduction import get_sum_kernel
sum_a_gpu = gpuarray.sum(a_gpu).get()
> assert abs(sum_a_gpu-sum_a)/abs(sum_a) < 1e-4
E assert (abs((array(1562.4339599609375, dtype=float32) - 100069.64))
/ abs(100069.64)) < 0.0001
test_gpuarray.py:431: AssertionError
____________________________ TestGPUArray.test_dot
_____________________________
def f(*args, **kwargs):
import pycuda.driver
# appears to be idempotent, i.e. no harm in calling it more than
once
pycuda.driver.init()
ctx = make_default_context()
try:
assert isinstance(ctx.get_device().name(), str)
assert isinstance(ctx.get_device().compute_capability(), tuple)
assert isinstance(ctx.get_device().get_attributes(), dict)
> inner_f(*args, **kwargs)
/usr/local/lib/python2.6/dist-packages/pycuda-0.94.1-py2.6-linux-x86_64.egg/pycuda/tools.py:503:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _
self = <test_gpuarray.TestGPUArray instance at 0x1fb7d88>
@mark_cuda_test
def test_dot(self):
from pycuda.curandom import rand as curand
a_gpu = curand((200000,))
a = a_gpu.get()
b_gpu = curand((200000,))
b = b_gpu.get()
dot_ab = numpy.dot(a, b)
dot_ab_gpu = gpuarray.dot(a_gpu, b_gpu).get()
> assert abs(dot_ab_gpu-dot_ab)/abs(dot_ab) < 1e-4
E assert (abs((array(777.169189453125, dtype=float32) - 50140.887)) /
abs(50140.887)) < 0.0001
test_gpuarray.py:498: AssertionError
===================== 4 failed, 31 passed in 17.31 seconds
=====================
cumath and driver pass everytime I have tried. The tesla is compute
cap.2.0. I have two compute cap.1.3 cards in the same machine... @Andreas,
is there a way to run your tests on a specific GPU instead of GPU0 (as
decided by CUDA)?
On Mon, Sep 27, 2010 at 3:00 PM, <pycuda-requ...@tiker.net> wrote:
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> 1. failed test_gpuarray on GTX480 (jmcarval)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Mon, 27 Sep 2010 04:42:00 -0700 (PDT)
> From: jmcarval <jmcar...@fe.up.pt>
> To: pycuda@tiker.net
> Subject: [PyCUDA] failed test_gpuarray on GTX480
> Message-ID: <1285587720039-5574551.p...@n2.nabble.com>
> Content-Type: text/plain; charset=us-ascii
>
>
> Hi.
> Installed PyCUDA 0.94.1 in several Linux boxes.
> All have Ubuntu 10.4 with CUDA 3.1 (drv 256.40) and python 2.6.5
>
> Boxes with 1.1 capability GPUs like 8600GT, 9400 GT or FX850 are ok and
> some
> user's are already trying them.
> Boxes with 1.3 (GTX280) and 2.0 (GTX480) have dificulties just running the
> supplied tests. On these:
>
> test_cumath.py passes all tests but is 5 times slower in the GTX280 and
> 40(!) times slower in the GTX480
> As far as I can see, this test never uses float64
>
> test_driver.py passes all tests but is 4 times slower in the GTX280 and
> 9(!)
> times slower in the GTX480
> As far as I can see, this test also doesn't use native float64. Testing
> fp_textures seem to be done with some sort of emulation, but I may be wrong
> here.
>
> test_gpuarray.py is 2 times slower in the GTX280 and fails the dot, sum,
> minmax and subset_minmax tests on the GTX480.
>
> Can anybody please point me to some mail thread, FAQ or instalation guide
> that helps me correct whatever I have done wrong?
>
> Thanks
>
> Joao
>
> --
> View this message in context:
> http://pycuda.2962900.n2.nabble.com/failed-test-gpuarray-on-GTX480-tp5574551p5574551.html
> Sent from the PyCuda mailing list archive at Nabble.com.
>
>
>
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