PyCUDA September 2009

pycuda@tiker.net

7 participants
10 discussions

Has anyone made a working parallel prefix sum/scan with pycuda?

by Peter Berrington

This may be a stupid question, but has anyone got any experience in implementing a parallel prefix sum with pycuda? I am trying to replace a portion of numpy code I had which was used to calculate the root mean square difference between two images. Theres a few steps in this procedure: a new array is created from the pairwise difference between the two source arrays. Each element of this new array is squared, and finally the elements of the new array are summed and divided by the array length in order to calculate the average square error between the two source images. My numpy code looks like this (hopefully the indentation is intact): def rmse(first, second): """ Returns the root mean square error between two image arrays. """ assert numpy.size(first) == numpy.size(second) difference = numpy.diff(numpy.dstack( (first.astype(numpy.float64), second.astype(numpy.float64)))) return 1 - math.sqrt(numpy.mean(difference**2)) / 255.0 It's not hard to see how pycuda could be used to accomplish the first few steps of calculating the squared pairwise difference between two source arrays: I was thinking that I would upcast both image arrays to floats, since I would be unable to accurately square or subtract them from each other as 8-bit unsigned integers. Off the top of my head, the kernel might start looking like this mod = SourceModule(""" __global__ void diff_and_square(float *dest, float *a, float *b) { const int i = threadIdx.x; dest[i] = a[i]*a[i] + b[i]*b[i] - (2 * a[i] * b[i]); } """) but I'm stuck on the last step: summation. I've read through this page on implementing parallel prefix sum with CUDA: http://http.developer.nvidia.com/GPUGems3/gpugems3_ch39.html It's a pretty good resource for understanding how to implement a prefix scan while avoiding bank conflicts and remaining work efficient. Unfortunately, the source code offered for download on that page is no longer hosted, it seems that its been replaced with similar functionality in the CUDA Data Parallel Primitives library- I was trying to skim through this code to see if it might be possible to plug it in and it seems quite complicated, using a plan manager class to control the behaviour of the prefix scan in a very genericized way. I was hoping someone might be able to offer some advice on the most painless way to accomplish this with pycuda. Thanks in advance!

10 years, 8 months

using libraries with pycuda

by Dan Goodman

Hi all, Has anyone tried using pycuda with a library of GPU code such as CUDPP [1] or chag:pp [2]? How do you use these in pycuda? Is it just a matter of passing some header and lib file directories to nvcc via the options=... keyword for SourceModule? Thanks, Dan

10 years, 9 months

Nvidia GTC: PyCUDA talk, Meetup

by Andreas Klöckner

Hi all, If you are attending Nvidia's GPU Technology Conference next week, there are two things I'd like to point out: - I'll be giving a talk about PyCUDA on Friday, October 2 at 2pm, where I'll both introduce PyCUDA and talk about some exciting new developments. The talk will be 50 minutes in length, and I'd be happy to see you there. - Also, I'd like to propose a PyCUDA meetup on Thursday, October 1 at noon. (ie. lunchtime) I'll be hanging out by the "Terrace" seminar room around that time. I'm looking forward to meeting some of you in person. See you next week, Andreas

10 years, 9 months

problem with pycuda._driver.pyd

by mailboxalpha

10 years, 9 months

Let's stay in touch on LinkedIn

by James Rubino

LinkedIn ------------ I'd like to add you to my professional network on LinkedIn. - James Accept James Rubino's invite: https://www.linkedin.com/e/isd/750889857/cREGKYEI/ ------ (c) 2009, LinkedIn Corporation

10 years, 9 months

Trying to get test_driver.py to run on OSX

by Aaron Cohen

Hi all: After suffering though the linkage problems in the CUDA sdk and the MacPorts boost and fixing that stuff with help from this mailing list, I've run into another roadblock. When I run test_driver.py I get this error message: $ python2.5 test_driver.py Traceback (most recent call last): File "test_driver.py", line 25, in <module> assert isinstance(pycuda.autoinit.device.get_attributes(), dict) File "/opt/local/lib/python2.5/site-packages/pycuda-0.94beta-py2.5- macosx-10.5-i386.egg/pycuda/driver.py", line 51, in device_get_attributes for att in dir(device_attribute) File "/opt/local/lib/python2.5/site-packages/pycuda-0.94beta-py2.5- macosx-10.5-i386.egg/pycuda/driver.py", line 52, in <genexpr> if att[0].isupper()) pycuda._driver.LogicError: cuDeviceGetAttribute failed: not found I have verified that the CUDA installation is working, because the NVIDIA sample apps work. The NVIDIA deviceQuery program gives this output: CUDA Device Query (Runtime API) version (CUDART static linking) There is 1 device supporting CUDA Device 0: "GeForce 9400M" CUDA Driver Version: 2.30 CUDA Runtime Version: 2.30 CUDA Capability Major revision number: 1 CUDA Capability Minor revision number: 1 Total amount of global memory: 266010624 bytes Number of multiprocessors: 2 Number of cores: 16 Total amount of constant memory: 65536 bytes Total amount of shared memory per block: 16384 bytes Total number of registers available per block: 8192 Warp size: 32 Maximum number of threads per block: 512 Maximum sizes of each dimension of a block: 512 x 512 x 64 Maximum sizes of each dimension of a grid: 65535 x 65535 x 1 Maximum memory pitch: 262144 bytes Texture alignment: 256 bytes Clock rate: 0.00 GHz Concurrent copy and execution: No Run time limit on kernels: Yes Integrated: Yes Support host page-locked memory mapping: Yes Compute mode: Default (multiple host threads can use this device simultaneously) Test PASSED Press ENTER to exit... One potential problem is that I installed the latest CUDA package, 2.3. Does PyCUDA work with 2.3? The documentation implies that it will. Should I downgrade to CUDA Driver 2.2? How do I uninstall the 2.3 package? I'm not sure what else to try at this point. Any ideas? Thanks, -Aaron

10 years, 9 months

statistics and spectral analysis libraries in pycuda ?

by Michael Rule

We're generating a lot of data using our PyCuda simulations, and we've gotten to the point where analysis of the simulation output takes far more time than generating said output. I was thinking of collecting PyCuda accelerated statistics / spectral analysis functions into one place for convenience. Hopefully most of these would just be a few lines long and define operations in terms of the existing PyCuda base. I was just wondering if anything like this already existed ? Sincerely, mrule. p.s.: Does PyCuda have access to the Cuda FFT implementation ? I think this was answered before on list, and the answer is no. Is there any GPU accelerated FFT that is easily accessible from PyCuda ?

10 years, 9 months

Scan/reduction 2D arrays...

by Vince Fulco

Do the suggestions from this post earlier in the summer still remain valid? I do a lot of 2D (array, sub-array) calcs in R and trying to get up to speed on the interchange between numpy indexing & PyCuda processing. TIA, V. <snip> Message: 2 Date: Tue, 16 Jun 2009 16:18:42 -0400 From: Andreas Kl?ckner <lists(a)informa.tiker.net> Subject: Re: [PyCUDA] Pointer arithmetic To: pycuda(a)tiker.net Message-ID: <200906161618.44292.lists(a)informa.tiker.net> Content-Type: text/plain; charset="iso-8859-1" On Dienstag 16 Juni 2009, Andrew Wagner wrote: > Suppose I have a column-major array stored in linear memory on the > gpu, and want to run a kernel on one column. The "right" way isn't quite supported yet, which would be to just write a[:,i] and get the right view delivered. This is mostly because PyCUDA doesn't know about strides just yet, and assumes that arrays are contiguous chunks of memory. Of course, your particular case doesn't violate that assumption, so you can feel free to hack just that case into GPUArray.__getitem__. (It already deals with the 1D case.) (Or feel free to hack stride treatment into PyCUDA--even a tiny step in that direction would be pretty cool.) Another way is to obtain a 1D view of the 2D array (which would require you to implement a .flat attribute mimicking numpy, also simple by copying what's happening in __getitem__). The last way is to just grab the pointer from ary.gpuarray, increment it by the right multiple of ary.dtype.itemsize and run with that. -- Vince Fulco, CFA, CAIA 612.424.5477 (universal) vfulco1(a)gmail.com A posse ad esse non valet consequentia “the possibility does not necessarily lead to materialization”

10 years, 9 months

PyCUDA paper: Call for Contributions

by Andreas Klöckner

Hi all, I am writing an article on PyCUDA, to be submitted to a parallel computation journal. I am doing this in the hope of increasing awareness of PyCUDA within the academic community. The article's focus is on run-time code generation ("RTCG"--aka "metaprogramming" if you like fancy words). To support the argument that GPU RTCG is indeed a helpful technique, and that PyCUDA is a tool that supports the technique well, I am including a section where I would like to showcase research projects that have benefited from PyCUDA. The deal is pretty simple: - You get to describe your research in a paragraph or two, plus possibly a picture. The paragraph should establish how your project obtained a tangible benefit from using PyCUDA and/or RTCG. - You'll be listed as a coauthor. If you're interested, please get in touch with me off-list, and we can work from there. I can't wait to hear how you are making use of PyCUDA! Andreas

10 years, 10 months

Re: [PyCUDA] PyCuda / PyOpenCL

by Andreas Klöckner

Dear Romain, On Mittwoch 02 September 2009, you wrote: > We use your package PyCuda, which is very nice, for our neural simulator > Brian (http://www.briansimulator.org). I noticed you recently released > PyOpenCL, and I was wondering whether you would have any advice on which > one we should choose for our project. In fact I just set up a google > group to discuss GPU issues for our simulations and I would very happy > if you could give us some insight about it: > http://groups.google.fr/group/brian-on-gpu. Thanks a lot and > congratulations for your great work! > > Best wishes, > Romain Brette I put together a wiki page with the best answer I can provide, here: http://wiki.tiker.net/CudaVsOpenCL As I've mentioned on the page, I deliberately put this on a Wiki--if you feel like you can contribute, please don't hesitate to do so. The fact that I've released PyOpenCL doesn't mean that I'm abandoning PyCUDA, quite the contrary actually. Writing PyOpenCL was a way for me to take a close look at the CL spec, and I liked what I saw. CUDA on the other hand is nice, too. Both CL and CUDA have their advantages. Neither PyCUDA nor PyOpenCL is going away--I expect there'll be a niche for both of them. HTH, Andreas

10 years, 10 months

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PyCUDA September 2009