[Eeglablist] EEGlab parallel computing & hardware specs

Wed Jun 12 12:31:02 PDT 2013

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>

> 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>
>
>> 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/fulland
>> 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>
>> > 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>:
>> > >
>> > >> 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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