PyCUDA March 2015

pycuda@tiker.net

6 participants
6 discussions

by Jerome Kieffer

On Mon, 30 Mar 2015 12:46:42 -0400 Ananth Sridharan <ananth(a)umd.edu> wrote: > > I have a simulation code which requires the use of multiple kernels. Each > of these kernels (global functions) needs to call a common set of device > functions. To organize code better, I'd like to provide multiple source > modules - one (or more) for the kernels, and one for the common > dependencies. Sometimes I concatenate sources (as python strings) before compilation ... in OpenCL. This allows me to have less source code to maintain and a better factorisation. This trick applies to your case a well. Cheers, -- Jérôme Kieffer Data analysis unit - ESRF

6 years, 4 months

multiple source modules

by Ananth Sridharan

> Hi, > I have a simulation code which requires the use of multiple kernels. Each of these kernels (global functions) needs to call a common set of device functions. To organize code better, I'd like to provide multiple source modules - one (or more) for the kernels, and one for the common dependencies. > > I'm missing the syntax (if it exists) to let the source module containing the kernels "know" the functions in the source module containing the device functions. Can someone help me out? > (I'm a pyCuda novice, and have basic working knowledge of cuda-c/cuda-fortran) > Ananth

6 years, 4 months

Re: [PyCUDA] Is this a candidate that could be ported to pycuda?

by Craig Stringham

One other tip on setting up your program is to remember to reduce memory accesses as much as possible, so try to maximize the computations you perform for every memory transfer. So you'll probably want to load a large chunk of tf and compute on several indicies of Mf and farray. Craig On Sat, Mar 28, 2015 at 8:19 PM Craig Stringham <stringham(a)mers.byu.edu> wrote: > Hi Bruce, > That's an excellent problem for a GPU. However, because each problem uses > a fair amount of memory being careful about how the memory is accessed will > dominate your performance gains (as is typical when using a GPU). For > example tf won't fit in the shared memory or cache of a multi-processor so > you'll also want to divide the problem again. > If you don't need to get this working for routine usage though, you might > just try using numba primitives to move it to a GPU. I haven't used them, > so I can't attest that it will give you a good answer. On the other hand, > this is the sort of problem that makes learning CUDA and PyCUDA easy, so > you might as well give it a shot. > Regards, > Craig > > On Sat, Mar 28, 2015 at 8:29 AM Bruce Labitt <bdlabitt(a)gmail.com> wrote: > >> From reading the documentation, I am confused if paralleling of this kind >> of function is worth doing in pycuda. >> >> I'm trying to add the effect of phase noise in to a radar simulation. >> The simulation is written in Scipy/numpy. Currently I am using joblib to >> run multiple cores. It is too slow for the scenarios I wish to try. It >> does work for a small number of targets and reduced phase noise array >> sizes. The following is the current approach: >> >> Function to parallelize >> >> def MSIN( farray, Mf, tf, jj ): >> """ >> farray, Mf, tf, ii >> >> farray array of frequencies (size = 10000) >> Mf array of coefficients (size = 10000) >> tf 2D array ~[2048 x 256] of time >> jj list of indices (fraction of the problem to solve) >> >> """ >> Msin = 0.0 >> for ii in jj: >> Msin = Msin + Mf[ii] * 2.0*cos( 2.0*pi*farray[ii]*tf ) >> return Msin >> >> Current method to call function in parallel (multiprocessing) >> >> """ >> ==================================================== >> Parallel computes the function MSIN with njobs cores >> ==================================================== >> """ >> MMM = Parallel(n_jobs=njobs, max_nbytes=None)\ >> (delayed(MSIN)( f, aa, tf1, ii ) for ii in idx) >> Msin = reduce(add, MMM) # add all the results of the cores together >> >> Any suggestions to port this to pycuda? Reasonable candidate? >> >> In essence, it is accumulating a scalar weighted cos function for many >> elements of a 2D array. It 'feels' like it should be portable. Any road >> blocks forseen? The 2D array of times is continuous in the sense of >> stride. But there are discontinuous jumps in time values in the array, >> which I do not think is a problem. >> >> I have from DumpProperties.py >> Device #0: GeForce GTX 680M >> Compute Capability: 3.0 >> Total Memory: 4193984 KB >> CAN_MAP_HOST_MEMORY: 1 >> CLOCK_RATE: 758000 >> MAX_BLOCK_DIM_X: 1024 >> MAX_BLOCK_DIM_Y: 1024 >> MAX_BLOCK_DIM_Z: 64 >> MAX_GRID_DIM_X: 2147483647 >> MAX_GRID_DIM_Y: 65535 >> MAX_GRID_DIM_Z: 65535 >> >> CUDA6.5 >> >> Thanks in advance for any insight, or suggestions on how to attack the >> problem >> >> -Bruce >> >> _______________________________________________ >> PyCUDA mailing list >> PyCUDA(a)tiker.net >> http://lists.tiker.net/listinfo/pycuda >> >

6 years, 4 months

Re: [PyCUDA] Is this a candidate that could be ported to pycuda?

by Craig Stringham

Hi Bruce, That's an excellent problem for a GPU. However, because each problem uses a fair amount of memory being careful about how the memory is accessed will dominate your performance gains (as is typical when using a GPU). For example tf won't fit in the shared memory or cache of a multi-processor so you'll also want to divide the problem again. If you don't need to get this working for routine usage though, you might just try using numba primitives to move it to a GPU. I haven't used them, so I can't attest that it will give you a good answer. On the other hand, this is the sort of problem that makes learning CUDA and PyCUDA easy, so you might as well give it a shot. Regards, Craig On Sat, Mar 28, 2015 at 8:29 AM Bruce Labitt <bdlabitt(a)gmail.com> wrote: > From reading the documentation, I am confused if paralleling of this kind > of function is worth doing in pycuda. > > I'm trying to add the effect of phase noise in to a radar simulation. The > simulation is written in Scipy/numpy. Currently I am using joblib to run > multiple cores. It is too slow for the scenarios I wish to try. It does > work for a small number of targets and reduced phase noise array sizes. > The following is the current approach: > > Function to parallelize > > def MSIN( farray, Mf, tf, jj ): > """ > farray, Mf, tf, ii > > farray array of frequencies (size = 10000) > Mf array of coefficients (size = 10000) > tf 2D array ~[2048 x 256] of time > jj list of indices (fraction of the problem to solve) > > """ > Msin = 0.0 > for ii in jj: > Msin = Msin + Mf[ii] * 2.0*cos( 2.0*pi*farray[ii]*tf ) > return Msin > > Current method to call function in parallel (multiprocessing) > > """ > ==================================================== > Parallel computes the function MSIN with njobs cores > ==================================================== > """ > MMM = Parallel(n_jobs=njobs, max_nbytes=None)\ > (delayed(MSIN)( f, aa, tf1, ii ) for ii in idx) > Msin = reduce(add, MMM) # add all the results of the cores together > > Any suggestions to port this to pycuda? Reasonable candidate? > > In essence, it is accumulating a scalar weighted cos function for many > elements of a 2D array. It 'feels' like it should be portable. Any road > blocks forseen? The 2D array of times is continuous in the sense of > stride. But there are discontinuous jumps in time values in the array, > which I do not think is a problem. > > I have from DumpProperties.py > Device #0: GeForce GTX 680M > Compute Capability: 3.0 > Total Memory: 4193984 KB > CAN_MAP_HOST_MEMORY: 1 > CLOCK_RATE: 758000 > MAX_BLOCK_DIM_X: 1024 > MAX_BLOCK_DIM_Y: 1024 > MAX_BLOCK_DIM_Z: 64 > MAX_GRID_DIM_X: 2147483647 > MAX_GRID_DIM_Y: 65535 > MAX_GRID_DIM_Z: 65535 > > CUDA6.5 > > Thanks in advance for any insight, or suggestions on how to attack the > problem > > -Bruce > > _______________________________________________ > PyCUDA mailing list > PyCUDA(a)tiker.net > http://lists.tiker.net/listinfo/pycuda >

6 years, 4 months

Is this a candidate that could be ported to pycuda?

by Bruce Labitt

>From reading the documentation, I am confused if paralleling of this kind of function is worth doing in pycuda. I'm trying to add the effect of phase noise in to a radar simulation. The simulation is written in Scipy/numpy. Currently I am using joblib to run multiple cores. It is too slow for the scenarios I wish to try. It does work for a small number of targets and reduced phase noise array sizes. The following is the current approach: Function to parallelize def MSIN( farray, Mf, tf, jj ): """ farray, Mf, tf, ii farray array of frequencies (size = 10000) Mf array of coefficients (size = 10000) tf 2D array ~[2048 x 256] of time jj list of indices (fraction of the problem to solve) """ Msin = 0.0 for ii in jj: Msin = Msin + Mf[ii] * 2.0*cos( 2.0*pi*farray[ii]*tf ) return Msin Current method to call function in parallel (multiprocessing) """ ==================================================== Parallel computes the function MSIN with njobs cores ==================================================== """ MMM = Parallel(n_jobs=njobs, max_nbytes=None)\ (delayed(MSIN)( f, aa, tf1, ii ) for ii in idx) Msin = reduce(add, MMM) # add all the results of the cores together Any suggestions to port this to pycuda? Reasonable candidate? In essence, it is accumulating a scalar weighted cos function for many elements of a 2D array. It 'feels' like it should be portable. Any road blocks forseen? The 2D array of times is continuous in the sense of stride. But there are discontinuous jumps in time values in the array, which I do not think is a problem. I have from DumpProperties.py Device #0: GeForce GTX 680M Compute Capability: 3.0 Total Memory: 4193984 KB CAN_MAP_HOST_MEMORY: 1 CLOCK_RATE: 758000 MAX_BLOCK_DIM_X: 1024 MAX_BLOCK_DIM_Y: 1024 MAX_BLOCK_DIM_Z: 64 MAX_GRID_DIM_X: 2147483647 MAX_GRID_DIM_Y: 65535 MAX_GRID_DIM_Z: 65535 CUDA6.5 Thanks in advance for any insight, or suggestions on how to attack the problem -Bruce

6 years, 4 months

PyCUDA test_cumath.py fails on cosh with complex number

by Adrian Oeftiger

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Dear PyCUDA community, in lack of finding a better place, I'll submit a tentative "bug" report here. I have posted my situation on stackoverflow: https://stackoverflow.com/questions/28829536/pycuda-test-cumath-py-fails-on… - --- Essentially, I've installed pycuda on a machine featuring a TESLA C2075. I'm running on Ubuntu 14.04 with the CUDA-6.0 compiler installed. Using python 2.7.9 (via the anaconda distribution) and numpy 1.9.0, I have installed pycuda 2014.1 from the ZIP file that Andreas Kloeckner provides on his website. (http://mathema.tician.de/software/pycuda/) Running the tests provided by that ZIP file goes all well except for the test_cumath.py file. I receive the following error: E AssertionError: (2.3841858e-06, 'cosh', <type 'numpy.complex64'>) E assert <built-in method all of numpy.bool_ object at 0x7f00747f3880>() E + where <built-in method all of numpy.bool_ object at 0x7f00747f3880> = 2.3841858e-06 <= 2e-06.all test_cumath.py:54: AssertionError ===== 1 failed, 27 passed in 12.57 seconds ===== - --- I can consistently reproduce the same number (and error) on the four different C2075 that I have available. Is this simply a tolerance that has not been set appropriately? I guess, I am not the first one to run this test, so I'm wondering whether I am doing something wrong... ;-) Please point me to any better place to ask this question if this should be the wrong address. Thanks for your help, Adrian -----BEGIN PGP SIGNATURE----- Version: GnuPG v1 iQEcBAEBAgAGBQJU9dVtAAoJELJrqqxor1RqWaIH/RFC9oAF3me+2HfQlX3H5uM0 jEDheeJ7yoR13uSkm5eVNd/dZr/vqzsCgHshYkslZy09eL2qsvytvXb279kB+9FL oPUDxywICBad1h0RjHdC2vgrNJmyQcjLUcl7fnvjp+obtKR67lRsQDkcQsRLSsRe 649TS5kkdb2ukDPv8XoikVi5Lr/mZ7J6HHOkr9+e+lD8megOhOwYxQQVSRtt2XJQ TR7PJh0ycn/nKe1ksSOl9KXTZuTbQQ4w7g4n0SiiYra0nZTXd+oEFOxqMDiuJ89h YFcN1wv6TxP8ZaWKhlffOeGrZHQh5PmusDW0EQBTME10HqNKg9gKXxJOmRhS95s= =rwVG -----END PGP SIGNATURE-----

6 years, 4 months

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

PyCUDA March 2015