Using today's git I confirm that test_gpuarray.py and test_driver.py work fine on Win XP with a GTX 480, CUDA 3.1, NVIDIA driver 258.96 (current stable).
test_math.py has the same 4 errors as Peter - the same errors at the exact same locations (but different numerical output due to use of random numbers). i. On 3 August 2010 08:07, Peter Schmidtke <pschmid...@ub.edu> wrote: > > 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 > > > _______________________________________________ > PyCUDA mailing list > PyCUDA@tiker.net > http://lists.tiker.net/listinfo/pycuda > -- Ian Ozsvald (A.I. researcher, screencaster) i...@ianozsvald.com http://IanOzsvald.com http://MorConsulting.com/ http://blog.AICookbook.com/ http://TheScreencastingHandbook.com http://FivePoundApp.com/ http://twitter.com/IanOzsvald _______________________________________________ PyCUDA mailing list PyCUDA@tiker.net http://lists.tiker.net/listinfo/pycuda
