PyCUDA September 2014

pycuda@tiker.net

8 participants
15 discussions

by Freddie Witherden

Hi Andreas, On 29/09/14 16:17, Andreas Kloeckner wrote: > GPUArrays don't actually care who "owns" the data, so if you're OK with > building a GPUArray as a 'descriptor' structure (which is quick and > lightweight) without moving any data around, then that would likely be a > reasonable way of going about this. > > How does that sound? That sounds like exactly what I am looking for. Do you have an API reference for wrapping an allocation with a GPUArray structure? Regards, Freddie.

5 years, 9 months

Reductions

by Freddie Witherden

Hi all, In my application I need to perform a (relatively) simple reduction of the form: sum(f(x[i], y[i])) over two device allocations x, y. If possible I would very much like to use the rather nice reduction code already in the pycuda.reduction module. However, the module only appears to work with GPUArrays and no raw device allocations. Is anyone aware of any simple workarounds for this -- other than creating actual GPUArrays for my data? Regards, Freddie.

5 years, 9 months

Re: [PyCUDA] 2nd try: 2014.1 git build, without siteconf.py

by Tomasz Rybak

Dnia 2014-09-18, czw o godzinie 22:30 +1200, Chris O'Halloran pisze: [ cut ] > However, I think I've determined that the opengl part of the > python-pycuda package hasn't been enabled properly. > > > Normally when you install pycuda from the tar package and run > configure you need to pass the ---cuda-enable-gl option. I note in > pycuda 2014 that has changed to --no-cuda-enable-gl > > Thanks for trying PyCUDA packages. > I find on my machine I can > > > import pycuda.driver as cuda_drv > > import pycuda.compiler as Source_module > > > etc > > > but I cannot > > import pycuda.gl > Sorry - it's my fault. OpenGL is enabled, but Python files from pycuda/gl/ packages are not installed when package is build. I'll look into it and let you know when it's fixed. Best regards. -- Tomasz Rybak GPG/PGP key ID: 2AD5 9860 Fingerprint A481 824E 7DD3 9C0E C40A 488E C654 FB33 2AD5 9860 http://member.acm.org/~tomaszrybak

5 years, 9 months

Re: [PyCUDA] Shutting down/Re-initializing PyCUDA

by Thomas Unterthiner

[sending to pycuda(a)tiker.net again, I think I replied to the wrong address last time so I'm not sure they ended up on the list] On 2014-09-25 00:53, Andreas Kloeckner wrote: > Thomas Unterthiner <thomas_unterthiner(a)web.de> writes: >> The C library uses the runtime API, thus it does not do any explicit >> context management. It calls cudaDeviceReset() before it returns to >> Python (which as far as I understand should undo any implicit context >> allocations it did before, according to >> http://developer.download.nvidia.com/compute/cuda/4_1/rel/toolkit/docs/onli… >> ). >> >> If needed, I can see if I can provide a minimal C library that exhibits >> the behavior. I just wanted to make sure I didn't have any mistakes in >> my PyCUDA code beforehand. > > I seem to remember that what Nvidia hacked together in terms of their runtime/driver API > interoperability requires that the runtime API (in your case the C > library) be in charge of managing CUDA contexts. > > Andreas > From what little I found by googling around, mixing runtime API/context APIs seems to be quite a mess. I found no clear indication of how to handle this within the C library. Do you by any chance have any pointers? (I have forgotten to point this out before: I do have the source code to the C library and can modify it if there's something I can do from there. But I'd rather not rewrite the whole thing using the driver API if it can be avoided). Ideally what I'd want is to shut down PyCUDA completely before calling the C library, and re-initializing it from the ground up again afterwards. But for some reason this doesn't seem to work the way I envisioned. So either 1) I forgot some steps when shutting down PyCUDA 2) I forgot some steps when re-initializing PyCUDA 3) the C library doesn't clean up properly before exiting 4) as both PyCUDA and the C library operate within the same process/thread, I can't avoid some sort of co-dependence between the two I was hoping it would be one of the cases 1-3, as these are probably easiest to remedy. Can you confirm that I did 1 and 2 correctly (the code I used for these two steps is included in the first email to the list)? Cheers Thomas

5 years, 9 months

Shutting down/Re-initializing PyCUDA

by Thomas Unterthiner

Hi! I have a program that makes extensive use of pycuda, but also calls out to a C library which also uses CUDA internally (it does not share any state or memory with the pycuda code, and uses the CUDA runtime API). However, after the call to the C library ends, all my PyCUDA calls fail with a "LogicError: cuFuncSetBlockShape failed: invalid handle". (The same call with the same parameters worked fine before calling out to the C library). I have tried explicitly initializing/shutting down the PyCUDA contexts, but I still can't get stuff to work. The relevant parts of my program look as follows: # initialize PyCUDA def init_pycuda(gpu_id): import pycuda.driver as cuda global __pycuda_context, __pycuda_device pycuda_drv.init() __pycuda_device = pycuda_drv.Device(gpu_id) __pycuda_context = __pycuda_device.make_context() import scikits.cuda.misc scikits.cuda.misc.init() init_pycuda(0) use_pycuda() # trying to shut down PyCUDA import scikits.cuda.misc from pycuda.tools import clear_context_caches cuda_memory_pool.free_held() cuda_hostmemory_pool.free_held() scikits.cuda.misc.shutdown() __pycuda_context.pop() clear_context_caches() __pycuda_context = None __pycuda_device = None # ... now I'm calling out to the other library call_external_library() init_pycuda(0) use_pycuda() # this will now fail with a LogicError As said before, the C library uses the CUDA runtime API, so it uses cudaSetDevice to initialize and calls cudaDeviceReset at the end. Is there something I'm overlooking wrt. how to (de)initializing PyCUDA? Cheers Thomas

5 years, 9 months

Re: [PyCUDA] 2nd try: 2014.1 git build, without siteconf.py

by Chris O'Halloran

Hello all, Following up on the above post. I've had pycuda working successfully on ubuntu 12.04 laptop using an Optimus card using Nvidia and Intel graphics cards. I then upgraded to 14.04 but have subsequently found Bumblebee to be a bit broken and am now using nvidia-prime as the nvidia driver. Trying to keep everything tidy, I thought I'd install python-pycuda using apt-get and it was very pleasing to see all the cuda toolkit automagically install. Great work. I'm not too fussy about being on the latest cuda toolkit since I'm a bit of a hobbyist. However, I think I've determined that the opengl part of the python-pycuda package hasn't been enabled properly. Normally when you install pycuda from the tar package and run configure you need to pass the ---cuda-enable-gl option. I note in pycuda 2014 that has changed to --no-cuda-enable-gl I find on my machine I can import pycuda.driver as cuda_drv import pycuda.compiler as Source_module etc but I cannot import pycuda.gl I've downloaded the ubuntu source deb package with a view to recompiling to enable gl but I get so many compilation errors I thought it best to describe the issue on this mailing list. Reading the debian/rules it seems as though the --cuda-enable-gl is selected for so I'm not really sure where to proceed. A lot of modifications have been made to the ubuntu source package. I should add that code that doesn't rely on pycuda.gl compiles and runs fine. Thanks for all the packaging work. Cheers and regards, Chris O'Halloran

5 years, 9 months

Re: [PyCUDA] Slice generates RuntimeError: only contiguous arrays may be used as arguments to this operation

by Andreas Kloeckner

Bruce Labitt <bdlabitt(a)gmail.com> writes: > I'm trying to port an FDTD code to pycuda and have run into a problem. The > error occurs when using slices. > > Known error? > > All variables are gpuarray. Fails in main time loop. > > In [4]: run fdtd_pycuda.py > --------------------------------------------------------------------------- > RuntimeError Traceback (most recent call last) > /usr/lib/python2.7/dist-packages/IPython/utils/py3compat.pyc in > execfile(fname, *where) > 202 else: > 203 filename = fname > --> 204 __builtin__.execfile(filename, *where) > > /home/bruce/FDTD/fdtd_pycuda.py in <module>() > 554 > 555 bx[1:ie_tot,:,:] = D1hx[1:ie_tot,:,:] * bx[1:ie_tot,:,:] - > D2hx[1:ie_tot,:,:] * \ > --> 556 ( ( ez[1:ie_tot, 1:jh_tot, :] - ez[1:ie_tot, 0:je_tot, > :]) - ( ey[1:ie_tot,:,1:kh_tot] - ey[1:ie_tot,:,0:ke_tot] ) ) / delta > 557 """ > 558 above line generates RunTimeError: only contiguous arrays > may be used as arguments to this operation > > /usr/local/lib/python2.7/dist-packages/pycuda-2014.1-py2.7-linux-x86_64.egg/pycuda/gpuarray.pyc > in __sub__(self, other) > 425 if isinstance(other, GPUArray): > 426 result = self._new_like_me(_get_common_dtype(self, > other)) > --> 427 return self._axpbyz(1, other, -1, result) > 428 else: > 429 if other == 0: > > /usr/local/lib/python2.7/dist-packages/pycuda-2014.1-py2.7-linux-x86_64.egg/pycuda/gpuarray.pyc > in _axpbyz(self, selffac, other, otherfac, out, add_timer, stream) > 308 assert self.shape == other.shape > 309 if not self.flags.forc or not other.flags.forc: > --> 310 raise RuntimeError("only contiguous arrays may " > 311 "be used as arguments to this operation") > 312 > > RuntimeError: only contiguous arrays may be used as arguments to this > operation > > > Evaluating the following generates the RuntimeError. > > In [17]: ( ey[1:ie_tot,:,1:kh_tot] - ey[1:ie_tot,:,0:ke_tot] ) > --------------------------------------------------------------------------- > RuntimeError Traceback (most recent call last) > <ipython-input-17-1c6dfb98d933> in <module>() > ----> 1 ( ey[1:ie_tot,:,1:kh_tot] - ey[1:ie_tot,:,0:ke_tot] ) > > /usr/local/lib/python2.7/dist-packages/pycuda-2014.1-py2.7-linux-x86_64.egg/pycuda/gpuarray.pyc > in __sub__(self, other) > 425 if isinstance(other, GPUArray): > 426 result = self._new_like_me(_get_common_dtype(self, > other)) > --> 427 return self._axpbyz(1, other, -1, result) > 428 else: > 429 if other == 0: > > /usr/local/lib/python2.7/dist-packages/pycuda-2014.1-py2.7-linux-x86_64.egg/pycuda/gpuarray.pyc > in _axpbyz(self, selffac, other, otherfac, out, add_timer, stream) > 308 assert self.shape == other.shape > 309 if not self.flags.forc or not other.flags.forc: > --> 310 raise RuntimeError("only contiguous arrays may " > 311 "be used as arguments to this operation") > 312 > > RuntimeError: only contiguous arrays may be used as arguments to this > operation > > ey.shape = (111, 110, 111) > kh_tot = 111 > ke_tot = 110 > > Shapes are appropriate. > > Work arounds for slices? Straight numpy implementation works fine. None yet. I'd appreciate patches, but for now the linear algebra functionality in PyCUDA only works on contiguous arrays. Andreas

5 years, 9 months

Slice generates RuntimeError: only contiguous arrays may be used as arguments to this operation

by Bruce Labitt

I'm trying to port an FDTD code to pycuda and have run into a problem. The error occurs when using slices. Known error? All variables are gpuarray. Fails in main time loop. In [4]: run fdtd_pycuda.py --------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) /usr/lib/python2.7/dist-packages/IPython/utils/py3compat.pyc in execfile(fname, *where) 202 else: 203 filename = fname --> 204 __builtin__.execfile(filename, *where) /home/bruce/FDTD/fdtd_pycuda.py in <module>() 554 555 bx[1:ie_tot,:,:] = D1hx[1:ie_tot,:,:] * bx[1:ie_tot,:,:] - D2hx[1:ie_tot,:,:] * \ --> 556 ( ( ez[1:ie_tot, 1:jh_tot, :] - ez[1:ie_tot, 0:je_tot, :]) - ( ey[1:ie_tot,:,1:kh_tot] - ey[1:ie_tot,:,0:ke_tot] ) ) / delta 557 """ 558 above line generates RunTimeError: only contiguous arrays may be used as arguments to this operation /usr/local/lib/python2.7/dist-packages/pycuda-2014.1-py2.7-linux-x86_64.egg/pycuda/gpuarray.pyc in __sub__(self, other) 425 if isinstance(other, GPUArray): 426 result = self._new_like_me(_get_common_dtype(self, other)) --> 427 return self._axpbyz(1, other, -1, result) 428 else: 429 if other == 0: /usr/local/lib/python2.7/dist-packages/pycuda-2014.1-py2.7-linux-x86_64.egg/pycuda/gpuarray.pyc in _axpbyz(self, selffac, other, otherfac, out, add_timer, stream) 308 assert self.shape == other.shape 309 if not self.flags.forc or not other.flags.forc: --> 310 raise RuntimeError("only contiguous arrays may " 311 "be used as arguments to this operation") 312 RuntimeError: only contiguous arrays may be used as arguments to this operation Evaluating the following generates the RuntimeError. In [17]: ( ey[1:ie_tot,:,1:kh_tot] - ey[1:ie_tot,:,0:ke_tot] ) --------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) <ipython-input-17-1c6dfb98d933> in <module>() ----> 1 ( ey[1:ie_tot,:,1:kh_tot] - ey[1:ie_tot,:,0:ke_tot] ) /usr/local/lib/python2.7/dist-packages/pycuda-2014.1-py2.7-linux-x86_64.egg/pycuda/gpuarray.pyc in __sub__(self, other) 425 if isinstance(other, GPUArray): 426 result = self._new_like_me(_get_common_dtype(self, other)) --> 427 return self._axpbyz(1, other, -1, result) 428 else: 429 if other == 0: /usr/local/lib/python2.7/dist-packages/pycuda-2014.1-py2.7-linux-x86_64.egg/pycuda/gpuarray.pyc in _axpbyz(self, selffac, other, otherfac, out, add_timer, stream) 308 assert self.shape == other.shape 309 if not self.flags.forc or not other.flags.forc: --> 310 raise RuntimeError("only contiguous arrays may " 311 "be used as arguments to this operation") 312 RuntimeError: only contiguous arrays may be used as arguments to this operation ey.shape = (111, 110, 111) kh_tot = 111 ke_tot = 110 Shapes are appropriate. Work arounds for slices? Straight numpy implementation works fine. Thanks, Bruce

5 years, 10 months

Distributing GPU applications

by marmaduke woodman

Hi, Does anyone have any experience or tips on distributing an application using PyCUDA for users/computers that have a suitable GPU & driver but otherwise unprepared for PyCUDA, i.e. not the full C++ compiler + CUDA SDK toolchain? Otherwise, I suspect the context cache is a place to start: I would compile all possible kernels, persist the cache and at runtime load the cache so that no compilation is necessary? Any information there upon would be welcome. Thanks, Marmaduke

5 years, 10 months

Re: [PyCUDA] MatrixTranspose.py example has CompileError

by Andreas Kloeckner

Bogdan Opanchuk <mantihor(a)gmail.com> writes: > P.S. Fixes include corrected invocations of prepare() and prepared_call(), > of course. Updated. For future reference--you can't sign up for user accounts, but you *can* edit anonymously on that wiki. Andreas

5 years, 10 months

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PyCUDA September 2014