[Eeglablist] EEGlab parallel computing & hardware specs

Brian Roach brian.roach at ncire.org
Thu Feb 20 09:39:50 PST 2014


Hi All,

I know that there is a lightning fast cudaica implementation that 
leverages GPUs on Linux, but has anyone tried making use of new CUDA gpu 
functionality that is now built in to the latest versions of matlab to 
optimize runica?  I'm particularly interested in this so that we could 
make use of CUDA GPUs on windows machines within matlab.

Thanks,
Brian

On 6/12/13 12:31 PM, Makoto Miyakoshi wrote:
> Dear Avi,
>
> runica() runs slowly, and spending 50 min is not surprising (assuming 
> you have >32 channels and >30min data). binica() runs faster, but does 
> not run on windows. We have cudaica() which runs super-fast but 
> requires configuration of GPU.
>
> Makoto
>
>
> 2013/6/9 Avi Lazarovits <avila at post.bgu.ac.il 
> <mailto:avila at post.bgu.ac.il>>
>
>     Hello,
>     Thank you very much, I will try to run this function with the new
>     filter.
>     About the inconsistency, actually I run a bandpass filter and by
>     mistake I quoted the code for the low pass filter which is much
>     faster. The code for the highpass is: EEG = pop_eegfilt( EEG, 1,
>     0, [], 0,0,0, 0, 'firls', 0);
>
>     Another function I intend to use is running ICA on these files. Is
>     there any way to accelerate the ICA (in my computer it took about
>     50 min wuth deafult settings of ICA)?
>
>     Thanks
>     Avi
>
>
>
>
>     2013/6/9 Andreas Widmann <widmann at uni-leipzig.de
>     <mailto:widmann at uni-leipzig.de>>
>
>         Hi Avi,
>
>         short answer:
>         upgrading to a recent 12.x EEGLAB version and using
>         pop_eegfiltnew should solve your problem.
>
>         Long answer:
>         * I have to slightly correct my previous statement. The legacy
>         (fir1/firls) FIR filter uses MATLAB filtfilt instead of MATAB
>         filter and is thus not multi-threaded by default.
>         * The legacy filter is flawed (see e.g.
>         http://www.frontiersin.org/Perception_Science/10.3389/fpsyg.2012.00233/full
>         and https://sccn.ucsd.edu/bugzilla/show_bug.cgi?id=631for a
>         more detailed description) and is not recommended.
>         * The new FIR filter in EEGLAB 12.x uses plain MATLAB filter
>         an is thus genuinely multi-threaded. Filtering a 1086734-point
>         66-channel dataset takes 5 seconds with 265-point filter
>         (default for 100 Hz-lowpass) and 2 min 16 seconds with
>         6145-point filter (your filter length) on my 1.7 Ghz dual core
>         i5. Should be much faster on your machine.
>         * Note: there is an inconsistency between your command (EEG =
>         pop_eegfilt( EEG, 0, 100, [], 0,0, 0, 'firls', 0);) and your
>         reported output. This command should give a 60-point lowpass
>         filter and *not* a 6144-point highpass. Did you change
>         something in your EEGLAB code?
>
>         Hope this helps! Best,
>         Andreas
>
>         > I am using EEGlab to process EEG recordings, and running low
>         pass 100 Hz is very slow. I am looking for code modifications
>         I can make to accelerate the running time, and also
>         recommendation for optimal hardware specs for this process.
>         >
>         > The files I process are recordings of 66 channles @ 2KHz ~10
>         minutes long (1086734 samples) bug. I can't resample the files
>         because I need high temporal resolution.
>         >
>         > I use EEGlab version 11.0.3.1b, and run "firls" filter. I
>         run the filter with the following command:
>         > EEG = pop_eegfilt( EEG, 0, 100, [], 0,0, 0, 'firls', 0);
>         > When I run this command I get the following message:
>         >
>         > eegfilt() - performing 6144-point highpass filtering.
>         > eegfilt() - highpass transition band width is 0.2 Hz.
>         >
>         > The computer I run the processing on now is i5 3470 (4
>         cores@ 3.2 Ghz) with 4Gb RAM running windows 7 enterprise 64
>         bit and Matlab 2012a.
>         >
>         > I tried running the same process on an i7 extreme edition
>         3930k with 64Gb RAM system and it didn't shorten the running
>         time at all. I also tried running this filter on a workstation
>         with Xeon CPU and it shortened the running time, but not
>         significantly. I looked on the CPU monitor and found that it
>         doesn't use more than 1 core, and that the maximal RAM memory
>         usage during the processing was about 6.5 Gb.
>         >
>         > 1.     I found that using SSD shortens the time
>         significantly, and therefore I assume that one of the big
>         bottlenecks of this processing is that the process is made on
>         the hard disk and not on the RAM. Is there a way to "make"
>         Matlab use RAM instead of the hard drive?
>         >
>         > 2.     Are there code changes I can make in EEGlab's code to
>         run this filter multithreaded so that the process will use all
>         the cores of my CPU? Is there any way to make this process on
>         GPU? Will it accelerate the process more than using parallel
>         computing of the CPU?
>         >
>         >
>         > Thank you
>         >
>         > Avi
>         >
>         >
>         >
>         > 2013/6/8 Arnaud Delorme <arno at sccn.ucsd.edu
>         <mailto:arno at sccn.ucsd.edu>>
>         > Thanks Andreas for the clarification.
>         >
>         > Arno
>         >
>         > On 8 Jun 2013, at 06:58, Andreas Widmann wrote:
>         >
>         > > Hi Avi and Arno,
>         > >
>         > > no using parfor won't help here, filtering is already
>         multithreaded since MATLAB R2007a.
>         > > http://www.mathworks.de/support/solutions/en/data/1-4PG4AN/
>         > >
>         > > I can try to narrow down the problem, but please on list
>         (eeglablist). This might be interesting also for others. Many
>         relevant parameters are missing:
>         > > * Length of dataset (samples or time and fs)
>         > > * Filter length or order
>         > > * OS and version
>         > > * EEGLAB version
>         > > * Why legacy filter (fir1/firls)? Using the new filter
>         will immediately halve computation time.
>         > >
>         > > Best,
>         > > Andreas
>         > >
>         > > Am 07.06.2013 um 22:20 schrieb Arnaud Delorme
>         <arno at sccn.ucsd.edu <mailto:arno at sccn.ucsd.edu>>:
>         > >
>         > >> Dear Avi,
>         > >>
>         > >> yes, it is possible to shorten the time.
>         > >> If you have multiple processor, you could try replacing
>         line 74 of firfilt.m
>         > >>
>         > >> for iDc = 1:(length(dcArray) - 1)
>         > >>
>         > >> with
>         > >>
>         > >> parfor iDc = 1:(length(dcArray) - 1)
>         > >>
>         > >>
>         > >> Channels will be filtered in parallel.
>         > >> GPU could help as well, but that's another story.
>         > >>
>         > >> Hope this helps,
>         > >>
>         > >> Arno
>         > >>
>         > >> On 2 Jun 2013, at 23:53, Avi Lazarovits wrote:
>         > >>
>         > >>> Hello,
>         > >>> My name is Avi and I work in lab with EEG recordings. I
>         am using the EEGlab on matlab 2012b and would like to thank
>         you for writing this useful toolbox.
>         > >>>
>         > >>> the Processing time of the fir1/firls lowpass filters is
>         very long (20 minutes for 66 channels SSD and I am looking for
>         ways to shorten it. Is there any way to shorten it by changing
>         the code for using parallel computingqGPU computing? will
>         EEGlab use parallel computing in next versions?
>         > >>> If not, what are the best hardware specs to run these
>         filters? I tried running it on an i7 3930k with 64Gb RAM
>         system and it didn't shorten the time vs. my i5 3470 4Gb RAM
>         system. I also tried to run this filter on a workstation
>         system with Xeon CPU and it shortened the running time, but
>         not significantly. I found that using SSD shortens the time
>         significantly. Do you have any other hardware specs
>         recommendations?
>         > >>>
>         > >>> Thank you
>         > >>> Avi
>         > >>
>         > >
>         >
>         >
>
>
>
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>
>
>
> -- 
> Makoto Miyakoshi
> Swartz Center for Computational Neuroscience
> Institute for Neural Computation, University of California San Diego
>
>
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