On 03/08/2010, at 06:27, Andreas Kloeckner wrote:
> On Tue, 03 Aug 2010 00:16:21 -0400, Andreas Kloeckner
> <li...@informa.tiker.net> wrote:
>> On Wed, 21 Jul 2010 14:12:22 +0200, Peter Schmidtke <pschmid...@ub.edu>
>> wrote:
>>> however seen the imprecision issues with gpuarrays I suppose
>>> I'd better not use them on Fermis right now?
>>
>> A week and a half or so back, I loosened the test bounds for these
>> functions to accommodate Fermi. (perhaps CUDA 3.1 in general) I wouldn't
>> make too much of this. Yes, precision has somewhat regressed. But a) not
>> catastrophically, and probably for a handsome speed gain, and b) the
>> purpose of these tests is to make sure that something reasonable (i.e.
>> not garbage) is returned.
>>
>> (Just to clarify: These precision issues are in Nvidia's special
>> functions, and have nothing to do with GPUArray itself, which just uses
>> them.)
>
> I must be getting old. The loosening of the test bounds was in PyOpenCL,
> not PyCUDA. Sorry for the confusion. In any case, test_gpuarray fully
> passes for me on Fermi with current git. Peter, Julien, anyone: are you
> still seeing problems/failures?
>
I see 4 failures in test_gpuarray using the svn version of pycuda (6fbf2a5),
however test_driver works fine now.
I also tried the undistributed tests and on the measure_speed.py I get a
cuMemAlloc failed error after 268435456. The Tesla has 3Gb of RAM.
Here are the errors I get on test_gpuarray on a S2050 :
=================================== 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/python2.7/site-packages/pycuda-0.94rc-py2.7-linux-x86_64.egg/pycuda/tools.py:503:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <test_gpuarray.TestGPUArray instance at 0xd05a200>
@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(3.697862848639488e-05, dtype=float32),
2.9394869e-06, <type 'numpy.float32'>, 'min')
test_gpuarray.py:424: 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/python2.7/site-packages/pycuda-0.94rc-py2.7-linux-x86_64.egg/pycuda/tools.py:503:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <test_gpuarray.TestGPUArray instance at 0xd084b48>
@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(0.00017937482334673405, dtype=float32) == 4.4573098e-06
test_gpuarray.py:458: 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/python2.7/site-packages/pycuda-0.94rc-py2.7-linux-x86_64.egg/pycuda/tools.py:503:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <test_gpuarray.TestGPUArray instance at 0xd085638>
@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(1553.26220703125, dtype=float32) - 99926.32)) /
abs(99926.32)) < 0.0001
test_gpuarray.py:405: AssertionError
------------------------------- Captured stdout --------------------------------
*** compiler output in /tmp/tmpKfGlhU
*** compiler output in /tmp/tmpvekHdO
____________________________ 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/python2.7/site-packages/pycuda-0.94rc-py2.7-linux-x86_64.egg/pycuda/tools.py:503:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <test_gpuarray.TestGPUArray instance at 0xd09de60>
@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(754.5011596679688, dtype=float32) - 49924.496)) /
abs(49924.496)) < 0.0001
test_gpuarray.py:472: AssertionError
> Andreas
>
Peter Schmidtke
-----------------
PhD Student
Department of Physical Chemistry
School of Pharmacy
University of Barcelona
Barcelona, Spain
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