[Eeglablist] Installing CUDAICA on Windows 10 (2022 update)

Tue Apr 26 11:33:55 PDT 2022

Dear Yunhui,

Thank you for the update. I confirmed on your Github site that several
files are updated 3 days ago.
https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_CloudyDory_cudaica-5Fwin&d=DwIFaQ&c=-35OiAkTchMrZOngvJPOeA&r=kB5f6DjXkuOQpM1bq5OFA9kKiQyNm1p6x6e36h3EglE&m=PgZMt7bBO3CX7fPe9D9HjtrqwW4mX1lqWU9J8vyDcz1EmiBfUPIwwvz80FxGgw_0&s=3kDda6GRxyWu12KS-uBT8aZ68xeCe8pqYfyL3MWN1L8&e= 

> Microsoft Visual Studio is NOT needed if you use the pre-built binary.

This kind of clarification is really helpful for non-technical users. I
appreciate your effort and contribution to the community!

Makoto

On Sat, Apr 23, 2022 at 7:16 PM 周云晖 <yhzhou17 at fudan.edu.cn> wrote:

> Dear colleagues,
>
> Due to a new round of COVID lockdown I finally have some time to update
> CUDAICA for Windows. It is now compatitable with the new Intel OneAPI base
> toolkit now. The old Intel MKL 2020 is still supported. I have actually
> compiled two binary exe files (for the old and new Intel MKL), and
> "cudaica.m" will automatically select which exe file to run depending on
> your installed MKL version. No procedure in actual ICA calculation is
> changed.
>
> Since there are two exe files now, the installation process is simplified.
> Modifying "icadefs.m" is no longer needed. Selecting the correct exe file
> is now automatically done in "cudaica.m".
>
> Best,
>
> Yunhui Zhou
>
> > -----原始邮件-----
> > 发件人: "Makoto Miyakoshi via eeglablist" <eeglablist at sccn.ucsd.edu>
> > 发送时间: 2021-11-21 01:56:10 (星期日)
> > 收件人: "eeglablist at sccn.ucsd.edu" <eeglablist at sccn.ucsd.edu>
> > 抄送:
> > 主题: Re: [Eeglablist] Installing CUDAICA on Windows 10 (2021 update)
> >
> > Update--
> > John Kiat (UC Davis) emailed me who happened to be working on CUDAICA
> > installation independently in the same period (what a coincidence).
> > He uploaded a Matlab movie of running CUDAICA to Youtube. If you want to
> > feel the speed, check it out.
> >
> https://urldefense.proofpoint.com/v2/url?u=https-3A__www.youtube.com_watch-3Fv-3DeVgpmvQ9LVU&d=DwIFaQ&c=-35OiAkTchMrZOngvJPOeA&r=kB5f6DjXkuOQpM1bq5OFA9kKiQyNm1p6x6e36h3EglE&m=hNn0oeQyLnH7lF_0qTcOEiiBrn-SwURSUdUvP2HKoG0v_UCZ2wi7HXTDonUb6kg2&s=1KcsNL2LFlPcNNgBNjCVSI6zinY1R1VvAjoE4ylIJs4&e=
> > John told me that in his environment (Ryzen 7 3800x, GTX1080ti) he
> > confirmed x15 boost even after addressing the 'drawnow' slowing issue.
> That
> > is a huge difference.
> > I updated my Wiki section with these link and info.
> >
> https://sccn.ucsd.edu/wiki/Makoto%27s_useful_EEGLAB_code#By_using_CUDAICA_.2811.2F20.2F2021_updated:_Thank_you_Ugo.2C_Yunhui.2C_and_John.21.29
> >
> > Makoto
> >
> > On Thu, Nov 18, 2021 at 3:48 PM Scott Makeig <smakeig at gmail.com> wrote:
> >
> > > Makoto and all -
> > >
> > > I was writing to the case where the data was to be compressed into a
> much
> > > smaller number of dimensions than channels. If the data are
> rank-deficient,
> > > reducing the dimension of the data to its true rank using PCA is quite
> > > acceptable (and necessary to allow ICA decomposition). Sorry if I
> > > misunderstood the case here. The Artoni paper shows that for the
> (typical)
> > > EEG study he was analyzing, removing further dimensions not only
> reduced
> > > the number of interpretable ICs but reduced their 'dipolarity', i.e.
> the
> > > degree to which they were compatible with a scalp projection from a
> > > localized cortical source area. I assume this might also reduce their
> > > independence (i.e., increase the pairwise mutual information of their
> time
> > > courses, although I don't think we checked this).
> > >
> > > Example 1:  I record 128 channels, then decide that  the signals from
> 17
> > > channels are 'bad'. I then decide to linearly interpolate signals for
> those
> > > 17. I then convert the recomposed to average reference.  The data rank
> > > should then be 128-17-1 = 110, since linear interpolation does not add
> > > *independent information* to the data - and converting to average
> reference
> > > loses a further dimension. Applying PCA reduction to 110 dimensions as
> a
> > > precursor to ICA is necessary and correct here.
> > >
> > > Example 2: I record 128 channels but want to do a 'quick' ICA
> > > decomposition of dimension 64. So I reduce the data to the largest 64
> PCs
> > > and then perform ICA decomposition. Though the data volume (RMS)
> accounted
> > > for by the 64 removed dimensions is very likely small, noticeable
> > > degradation of the 'brain' ICs results.   This is because the PCs 'cut
> > > across' all the IC effective sources - and the remaining 64 dimensions
> sum
> > > in part the activities of all 128 IC effective source processes, not
> > > allowing the derived ICs to be truly independent of each other or
> aligned
> > > with only one effective source.
> > >
> > > Scott
> > >
> > > On Thu, Nov 18, 2021 at 6:27 PM Makoto Miyakoshi via eeglablist <
> > > eeglablist at sccn.ucsd.edu> wrote:
> > >
> > >> Dear Scott,
> > >>
> > >> Apart from the value of the study, I don't like the side effect
> Fiorenzo's
> > >> PCA paper caused: It made non-engineers superstitious about the use
> of PCA
> > >> (and now you push this fear campaign.)
> > >>
> > >> In the Artoni paper in question, at the very first line of the result
> > >> section he reported that there were only 8+/-2.5 PCs were left to
> obtain
> > >> 95% of variance out of 71 scalp electrodes. You can easily imagine
> what
> > >> happens when you reject 63/71 PCs before ICA. In this sense, the
> > >> conclusion
> > >> of this study is 'duh' to me (is it not?)
> > >>
> > >> 'Applying PCA before ICA is suboptimal' is a qualitative statement.
> But
> > >> what if you reject only one or two PCs just to make the data
> full-ranked?
> > >> These questions can be answered only by performing numerical and
> > >> simulation
> > >> studies. We should educate people to reject qualitative statements and
> > >> instead think quantitatively.
> > >>
> > >> Dear John,
> > >>
> > >> It's not about CUDAICA per se, but if you care the obtimality of the
> ICA
> > >> results, I recommend you compare results from channel rejection
> against
> > >> results from PCA dimension reduction so that you obtain 70 ICs in both
> > >> results. Compare the results side by side and determine both visually
> and
> > >> quantitatively using ICLabel. Most likely, they do not show much
> > >> difference. Then you feel better to go with PCA because so that you
> don't
> > >> need to lose scalp electrodes.
> > >>
> > >> Makoto
> > >>
> > >>
> > >>
> > >> On Wed, Nov 17, 2021 at 1:22 PM Scott Makeig <smakeig at gmail.com>
> wrote:
> > >>
> > >> > John,
> > >> >
> > >> > Makoto  seems to forget the result of Fiorenzo Artoni that applying
> PCA
> > >> > before ICA is suboptimal - better to reduce the number of channels
> and
> > >> run
> > >> > ICA decomposition full-rank. Or, if you are more ambitious /
> exacting,
> > >> run
> > >> > multiple ICA decompositions on different channel subsets (for
> example,
> > >> > random sets of 70 channels picked from 128) and then apply
> clustering to
> > >> > the resulting independent component (IC) maps - I haven't seen that
> > >> > approach applied yet ...
> > >> >
> > >> > Artoni, F., Delorme, A. and Makeig, S., 2018. Applying dimension
> > >> > reduction to EEG data by Principal Component Analysis reduces the
> > >> quality
> > >> > of its subsequent Independent Component decomposition
> > >> > <
> > >>
> https://urldefense.proofpoint.com/v2/url?u=https-3A__www.sciencedirect.com_science_article_pii_S1053811918302143-3Fcasa-5Ftoken-3DKT3XImh-2D-2Dl0AAAAA-3Aut0ozF7mVGYDngMVu-2Di0PowjzqzZEhuIl153z6MNgM8NRHDXZZj2CWlYEd0948glBn11q-5FXk7B8&d=DwMFaQ&c=-35OiAkTchMrZOngvJPOeA&r=pyiMpJA6aQ3IKcfd-jIW1kWlr8b1b2ssGmoavJHHJ7Q&m=YlyRvbJOBqbcxuJuFRpFCdKF7Tkg1Qj32rXx8Aa2570eE8IWixleWy79aWfkbQP9&s=n4PV6FRx_PCHi9cIjG7_rLRcb9j62ChP5inSC0zrMnE&e=
> > >> >
> > >> > . *NeuroImage*, *175*, pp.176-187.
> > >> >
> > >> > Fiorenzo's RELICA plug-in does something related - applying
> (full-rank)
> > >> > ICA decomposition to randomly selected subsets of data points,
> followed
> > >> by
> > >> > component clustering. Zeynep Akalin Acar has recently demonstrated
> that
> > >> > using RELICA component cluster scalp map means *and* variances  can
> > >> > increase the accuracy of high-resolution source location estimation.
> > >> >
> > >> > Acar, Z.A. and Makeig, S., 2020, October. Improved cortical source
> > >> > localization of ICA-derived EEG components using a source scalp
> > >> projection
> > >> > noise model
> > >> > <
> > >>
> https://urldefense.proofpoint.com/v2/url?u=https-3A__ieeexplore.ieee.org_iel7_9287816_9287978_09288020.pdf-3Fcasa-5Ftoken-3DdOEYtrVfetoAAAAA-3AQsY-2D7AQl9TzSyk3IMqlsy7KnMhUI-2DJ-2DQ68H5cKzjpbPWRVpN-2D4xrR-5FJPMBwDEDF-5FQ9nLkIHLH5U&d=DwMFaQ&c=-35OiAkTchMrZOngvJPOeA&r=pyiMpJA6aQ3IKcfd-jIW1kWlr8b1b2ssGmoavJHHJ7Q&m=YlyRvbJOBqbcxuJuFRpFCdKF7Tkg1Qj32rXx8Aa2570eE8IWixleWy79aWfkbQP9&s=Y8l5g2Z8tUOx5dUtb8pXqMm0HnvqsCYHwlarrNfdP_c&e=
> > >> >.
> > >> > In *2020 IEEE 20th International Conference on Bioinformatics and
> > >> > Bioengineering (BIBE)* (pp. 543-547). IEEE.
> > >> >
> > >> > Scott
> > >> >
> > >> > On Wed, Nov 17, 2021 at 3:24 PM Makoto Miyakoshi via eeglablist <
> > >> > eeglablist at sccn.ucsd.edu> wrote:
> > >> >
> > >> >> Dear John,
> > >> >>
> > >> >> I found it interesting that in your case runica()'s processing time
> > >> >> linearly increased (63 -> 168 min) as the input data length
> increased
> > >> (8
> > >> >> ->
> > >> >> 25 min), but that for CUDAICA did not (2.3 -> 2.8 min).
> > >> >>
> > >> >> If you have 126 ch, you want to have 126^2*30 = 476280 data points
> as a
> > >> >> minimum (from SCCN's never-verified rule of thumb). But you have
> > >> 275*474=
> > >> >> 130350 datapoints, which seems suboptimal to ensure a good
> learning.
> > >> >> Perhaps you want to apply dimension reduction using PCA to obtain
> 70
> > >> ICs,
> > >> >> so that the same rule of thumb predicts 70^2*30 = 147000
> datapoints for
> > >> >> learning, which is much closer.
> > >> >>
> > >> >> Do you want to know more detail about this optimization?
> > >> >> In fact, without running a simulation you can't theoretically
> determine
> > >> >> what number is a good number. This is why I wrote this simulator
> as an
> > >> >> EEGLAB plugin. Try it out to 'feel' how much deviation/violation
> from
> > >> the
> > >> >> 'rule of thumb' can negatively impact the decomposition.
> > >> >>
> > >> >>
> > >>
> https://urldefense.proofpoint.com/v2/url?u=https-3A__www.youtube.com_watch-3Fv-3DCGOw04Ukqws&d=DwIFaQ&c=-35OiAkTchMrZOngvJPOeA&r=kB5f6DjXkuOQpM1bq5OFA9kKiQyNm1p6x6e36h3EglE&m=YfSSJdGbaWUJjsVGL_Bd3kbvoWDALgCGOA44Hn93INujbnQT8WNcijz3CAaY7Km2&s=HuuZdy7O3viOY-fIz_ayjeDkatQ_038Fa2wbfMDeg9I&e=
> > >> >>
> > >> >> Makoto
> > >> >>
> > >> >> On Tue, Nov 16, 2021 at 2:59 PM Richards, John <
> > >> RICHARDS at mailbox.sc.edu>
> > >> >> wrote:
> > >> >>
> > >> >> > I was curious about the speed differences for my applications.  I
> > >> have
> > >> >> > tested this before, did not write down my results.
> > >> >> >
> > >> >> > I ran an EEGlab file, 126 channels * 275 samples * 474 trials,
> about
> > >> 8
> > >> >> min
> > >> >> > of EEG data.  This is done on a linux node in a a linux cluster,
> > >> >> Intel(R)
> > >> >> > Xeon(R) CPU E5-2680 v4 @ 2.40GHz node, with 256G memory, 28
> cores.
> > >> The
> > >> >> > runica appears to be working on 12 cores.   The gpu was a dual
> Tesla
> > >> >> > P100-PCIE-16G.  The cudaica ran on one GPU.
> > >> >> >
> > >> >> > runica version took 63 min.  cudaica version took 2 min 15 s;
> > >> >> > runica appeared to be running on multiple CPUs, ~ 12 CPUs.
> > >> >> >
> > >> >> > I concatenated the data for 1422 trials, about 25 min
> > >> >> > Cudica took 2 min 50s|
> > >> >> > runica took 2.8 hours, 12 CPUs
> > >> >> >
> > >> >> > Most of our runs with infants take 8 to 10 min, some of our
> adults
> > >> runs
> > >> >> > are 25 min.
> > >> >> >
> > >> >> > I understand from the earlier conversation the binica might be
> able
> > >> to
> > >> >> > match these results?   I'm not going to do a full test, but this
> > >> >> convinces
> > >> >> > me to stick with cudaica for now.
> > >> >> >
> > >> >> > John
> > >> >> >
> > >> >> >
> > >> >> > -----Original Message-----
> > >> >> > From: Richards, John
> > >> >> > Sent: Thursday, November 11, 2021 1:15 AM
> > >> >> > To: Makoto Miyakoshi <mmiyakoshi at ucsd.edu>; ugob at siu.edu;
> > >> >> > eeglablist at sccn.ucsd.edu
> > >> >> > Subject: RE: [Eeglablist] Installing CUDAICA on Windows 10 (2021
> > >> update)
> > >> >> >
> > >> >> > Re CUDAICA.  I was able to install it, i don't remember it being
> that
> > >> >> > difficult.  I had to mess around with the CUDA version.
> > >> >> >
> > >> >> > I have found it "blazing" fast compared to runica. I have not
> timed
> > >> it.
> > >> >> > We have 10-15 min sessions with EGI 128, 250 hz, do the Prep
> > >> pipeline to
> > >> >> > get avg ref, and then CUDAICA.  It takes < 5 min to do the Prep,
> > >> and  <
> > >> >> 5
> > >> >> > min to do the CUDAICA; cf 45 min to 60 min with runica.  I may
> not be
> > >> >> using
> > >> >> > the most recent runica.   BTW, we have fairly powerful
> computers; we
> > >> >> use 48
> > >> >> > cores for the Prep pipeline which is a vast speedup, and V100's
> with
> > >> >> 16gb
> > >> >> > or 32gb.   Definitely not bargain chips.  We use the 48core
> computers
> > >> >> for
> > >> >> > the runica, but it does not appear to profit from the multiple
> CPUs.
> > >> >> The
> > >> >> > Prep pipeline also is very slow on single CPUs, but very fast on
> the
> > >> 48
> > >> >> CPU
> > >> >> > machines.
> > >> >> >
> > >> >> > I would be glad to share more details if anyone is interested.
> > >> >> >
> > >> >> > John
> > >> >> >
> > >> >> >
> > >> >> > ***********************************************
> > >> >> > John E. Richards
> > >> >> > Carolina Distinguished Professor
> > >> >> > Department of Psychology
> > >> >> > University of South Carolina
> > >> >> > Columbia, SC  29208
> > >> >> > Dept Phone: 803 777 2079
> > >> >> > Fax: 803 777 9558
> > >> >> > Email: richards-john at sc.edu
> > >> >> >
> > >> >> >
> > >> >>
> > >>
> https://urldefense.proofpoint.com/v2/url?u=https-3A__jerlab.sc.edu&d=DwIFAw&c=-35OiAkTchMrZOngvJPOeA&r=pyiMpJA6aQ3IKcfd-jIW1kWlr8b1b2ssGmoavJHHJ7Q&m=XWfhosWnNSjs97eRAV2Ysofk5w2Z2_mbQvfeek3KRqTVlZ-2fBHSCo5P_bnFInes&s=yvIsDcwOpKjhTPokE_cuv5RlAl7bUeNjmpt7-e34zWk&e=
> > >> >> > *************************************************
> > >> >> >
> > >> >> > -----Original Message-----
> > >> >> > From: eeglablist <eeglablist-bounces at sccn.ucsd.edu> On Behalf Of
> > >> Makoto
> > >> >> > Miyakoshi via eeglablist
> > >> >> > Sent: Thursday, November 11, 2021 1:02 AM
> > >> >> > To: EEGLAB List <eeglablist at sccn.ucsd.edu>; ugob at siu.edu
> > >> >> > Subject: [Eeglablist] Installing CUDAICA on Windows 10 (2021
> update)
> > >> >> >
> > >> >> > Dear list members,
> > >> >> >
> > >> >> > I summarized the steps to install cudaica() which uses GPU
> > >> computation
> > >> >> to
> > >> >> > calculate infomax ICA (Raimondo et al., 2012). The result from
> the
> > >> speed
> > >> >> > comparison between runica() and cudaica() was not as dramatic as
> x25
> > >> >> > reported by the original paper, probably because Tjerk's smart
> hack
> > >> >> alone
> > >> >> > already gave x4-5 speed up to runica(). Still, using a relatively
> > >> cheap
> > >> >> > GTX1660 (the pre-COVID price range is $250), I confirmed x4-5
> speed
> > >> up
> > >> >> > compared with runica(). The detailed instruction can be found in
> the
> > >> >> > following link.
> > >> >> >
> > >> >> >
> > >> >> >
> > >> >>
> > >>
> https://sccn.ucsd.edu/wiki/Makoto%27s_useful_EEGLAB_code#By_using_CUDAICA_.2811.2F10.2F2021_added.29
> > >> >> >
> > >> >> > WARNING: The installation was difficult.
> > >> >> >
> > >> >> > Makoto
> > >> >> > _______________________________________________
> > >> >> > Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
> > >> >> > To unsubscribe, send an empty email to
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> > >> >> >
> > >> >> _______________________________________________
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> > >> >>
> > >> >
> > >> >
> > >> > --
> > >> > Scott Makeig, Research Scientist and Director, Swartz Center for
> > >> > Computational Neuroscience, Institute for Neural Computation,
> > >> University of
> > >> > California San Diego, La Jolla CA 92093-0559,
> > >> http://sccn.ucsd.edu/~scott
> > >> >
> > >> _______________________________________________
> > >> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
> > >> To unsubscribe, send an empty email to
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> > >> For digest mode, send an email with the subject "set digest mime" to
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> > >>
> > >
> > >
> > > --
> > > Scott Makeig, Research Scientist and Director, Swartz Center for
> > > Computational Neuroscience, Institute for Neural Computation,
> University of
> > > California San Diego, La Jolla CA 92093-0559,
> http://sccn.ucsd.edu/~scott
> > >
> > _______________________________________________
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