[Eeglablist] Installing CUDAICA on Windows 10 (2021 update)

Makoto Miyakoshi mmiyakoshi at ucsd.edu
Mon Nov 15 09:58:30 PST 2021


Dear Ugo,

So cool! I'll forward it to EEGLAB developers.
Thank you for your contribution Ugo.

Makoto

On Sun, Nov 14, 2021 at 12:43 PM Bruzadin Nunes, Ugo <ugob at siu.edu> wrote:

> Dear Makoto,
>
> Thank you for your kind words, it means a lot to me.
>
> I'm not sure if I'm eligible for a full hacking lecture, but I do hack
> eeglab functions quite a lot, so maybe!
>
> Since you mentioned that DipFit is one of the slowest functions on a
> pipeline, I thought I could share at least one of my functions, which may
> be useful if you are running dipfit.
>
> https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_UgoBruzadin_Par-5FDipFit&d=DwIFaQ&c=-35OiAkTchMrZOngvJPOeA&r=kB5f6DjXkuOQpM1bq5OFA9kKiQyNm1p6x6e36h3EglE&m=LjlXQElvrzu5WSqfQQn2Tj0i4JzJzXesDhCLZ15vFi_1iJA7sG-ihSroTF0n19Wo&s=bozvyYqIvDE8gaoJPdQLii1CwMkRBx4xrN3_B-O10b0&e= 
> <https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_UgoBruzadin_Par-5FDipFit&d=DwMGaQ&c=-35OiAkTchMrZOngvJPOeA&r=pyiMpJA6aQ3IKcfd-jIW1kWlr8b1b2ssGmoavJHHJ7Q&m=zgsxd6SVydK-goOqcsWaEpTuUVwrIkL1QYXSo2jiylIx9WHS9mQimHf-JRHQU-7W&s=_qg7paxLvbF2rNVobHB8399cQ7wlqHwx0siKAemj730&e=>
>
> <https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_UgoBruzadin_Par-5FDipFit&d=DwMGaQ&c=-35OiAkTchMrZOngvJPOeA&r=pyiMpJA6aQ3IKcfd-jIW1kWlr8b1b2ssGmoavJHHJ7Q&m=zgsxd6SVydK-goOqcsWaEpTuUVwrIkL1QYXSo2jiylIx9WHS9mQimHf-JRHQU-7W&s=_qg7paxLvbF2rNVobHB8399cQ7wlqHwx0siKAemj730&e=>
> GitHub - UgoBruzadin/Par_DipFit: Contains the function for parallel dipfit
> <https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_UgoBruzadin_Par-5FDipFit&d=DwMGaQ&c=-35OiAkTchMrZOngvJPOeA&r=pyiMpJA6aQ3IKcfd-jIW1kWlr8b1b2ssGmoavJHHJ7Q&m=zgsxd6SVydK-goOqcsWaEpTuUVwrIkL1QYXSo2jiylIx9WHS9mQimHf-JRHQU-7W&s=_qg7paxLvbF2rNVobHB8399cQ7wlqHwx0siKAemj730&e=>
> Contains the function for parallel dipfit. Contribute to
> UgoBruzadin/Par_DipFit development by creating an account on GitHub.
> github.com
> **
>
> If you have Parallel Computing Toolbox, you can run this function instead
> of the normal multifit, and it will perform the dipfit using as many
> cores/workers as you have available, considerably speeding up the process
> (by as many cores as you have, the more the merrier!) My environment has 12
> cores, so this par_multifit runs ~12x faster; in my other environment I
> have 4 cores, so it runs par_multifit ~4 times faster than the normal
> multifit.
>
> The coding isn't perfect, but it should be useful for anyone that
> streamlines dipfitting, or uses it on a daily basis. It may use a bit more
> RAM than normal, since the EEG needs to be copied and preallocated for
> parallel processing. It may require editing other functions to include this
> in your pipeline. Any suggestions or bugs, please let me know!
>
> I have the whole order of dipfit functions running my defaults in one
> button on my big plugin called QuickLab, which should come out soon, but it
> uses defaults that may be different for different people, so the plugin
> still needs lots of adjustments to be independent of my own dataset.
>
> In any case, I hope it's useful!
>
> Best wishes,
>
> Ugo Bruzadin Nunes, Ph.D. Candidate *(He/Him/His)*
>
> Visiting Assistant Professor, Psychology
>
> Webster University
>
> Office Location: ISB room 316
>
> Office Number: (314) 968-7677
>
> Ugo at webster.edu <UgoBruzadinNunes at webster.edu>
>
> ------------------------------
> *From:* Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
> *Sent:* Friday, November 12, 2021 3:40 PM
> *To:* eeglablist at sccn.ucsd.edu <eeglablist at sccn.ucsd.edu>
> *Subject:* Re: [Eeglablist] Installing CUDAICA on Windows 10 (2021 update)
>
> Dear Ugo,
>
> Thank you for your time and detailed comment Ugo.  I'm excited to hear back
> from the original author of the report.
> So in your environment, CUDAICA on RTX3070 is about 12 times faster than
> BINICA on Ryzen 5 5900X. My comparison of CUDAICA vs. runica is about only
> 4-5 times of difference--this is probably my GTX1660 is not as fast as
> RTX3070.
>
> There are other parts in my pipeline that take longer time than others,
> including CleanLine (slowest only next to ICA), clean_rawdata (RANSAC in
> the electrode rejection stage, ASR), and Dipfit (gradient descent). It's
> exciting that you have a solution to speed up the dipole fitting process.
> Your 'library of hacks' sound so interesting--one day you should give us an
> EEGLAB hacking lecture!
>
> Please keep us posted Ugo, your input is tremendously useful for all of us
> in the community!
>
> Makoto
>
> On Thu, Nov 11, 2021 at 3:48 PM Bruzadin Nunes, Ugo <ugob at siu.edu> wrote:
>
> > Dear Makoto and everyone,
> >
> >
> >
> > I am glad these instructions were useful, and I’m sorry to was so hard to
> > reproduce my results! I had a really rough time installing CUDAICA on
> > windows in the beginning, which is why I made these instructions in the
> > first place. Now I also learned how to do it a bit faster/easier, but I
> > didn’t take the time to edit my instructions (although, to be fair, I
> still
> > used those instructions when I make new installs). It was painful
> because I
> > didn’t know anything about libraries or APIs at the time, and still
> CUDAICA
> > gets a bit finicky, especially with the icadefs and the .sc defaults.
> >
> >
> >
> > In terms of speed, I’ve been using CUDAICA on a RTX3070 comparing it with
> > BINICA one my 12 cores AMD Ryzen 5 5900X. I run BINICA in parallel, so I
> > run 12 files at a time, in which case it’s a tight race; my files are big
> > in continuous mode, which is why I thought CUDAICA was so important and
> > still are. For smaller PCAs, binica and cudaica run equally fast, at
> least
> > in my setup, so 12x binica is much faster than 1 cudaica at a time, and I
> > am working on a script that would run 11 binicas and 1 cudaica at a time,
> > which would save me lots of time.
> >
> >
> >
> > My take away is that CUDAICA is extremely useful for running big files in
> > continuous mode, and for ICA or large PCAs (50+). In my setup, I can run
> an
> > ICA for a continuous file (5 to 10 min long) using CUDAICA in 20 to 30
> > seconds. For the same files, it takes binica about 250 to 300 seconds,
> and
> > the old runica several minutes. I do not have the exact numbers
> precisely,
> > but I do use them almost everyday and that’s about what I remember. I was
> > not aware there was a hack for speeding up runica, I’ll look into it!
> >
> >
> >
> > If I run 12 files at a time in parallel, 250/12 seconds is almost exactly
> > the same speed as 20 seconds per file using CUDAICA, which is why I
> > generally run binica instead of cudaica.
> >
> >
> >
> > For the sake of science, I’ve tried running CUDAICA in parallel, but it
> > does not produce any different, it only slows it down (it wouldn’t make
> > sense to work, but I’ve tried it anyway, it runs but it’s way slower).
> >
> >
> >
> > In summary, CUDAICA is absolutely useful if you have large continuous
> > files in need of large PCAs or ICAs and a good GPU, allowing one to run
> ICA
> > in huge files in as little as 20 seconds. In my old RazerBlade, with a
> > GTX970m, cudaica was as fast as binica; But, because one can run 4 files
> at
> > a time in the CPU, is gives the advantage to binica. My guess is that
> this
> > is the case for the majority of the computers, IF you are running a
> > pipeline and can run files in parallel. On individual files, with a good
> > enough GPU, it’s absolutely worth the time save, especially in manual
> file
> > processing. I just wish it was a bit easier to install.
> >
> >
> >
> > I have a library of hacks for speeding up EEGLAB; I run a modified
> version
> > of the 2020 eeglab for our laboratory, which we standardize in all
> > computers, and which contains several modifications that significantly
> > speed up data processing. I intend to release these changes as a plugin
> > sometime soon. This includes a dipfit that runs in parallel, cutting time
> > significantly, a partial component and channel interpolator based on
> > eegplot_w – which allows for removal of parts of components without the
> > removal of the full component - a viewprops+, which allows for quicker
> > component selection and removal, and an autopipeliner, which I use to run
> > to test my pipelines in parallel and stores all the backups in organized
> > folders, using simple commands instead of having to code full scripts
> every
> > time.
> >
> >
> >
> > Thanks for all the feedback, and I’m pleased that this was a useful tool.
> > Again, I am sorry for the extreme level of detail and often unnecessary
> > instructions. I didn’t know what worked, so I put all that worked in text
> > for future reference. CUDAICA and GPU arrays have a lot of potential in
> > saving time, for long processes such as ICA, PCA, BSS, and DIPFIT, and I
> > personally enjoy to a fault making scripts run faster!
> >
> >
> >
> > Best wishes,
> >
> >
> >
> > Ugo
> >
> >
> >
> > Ugo Bruzadin Nunes, Ph.D. Candidate
> >
> > Visiting Assistant Professor, Psychology
> >
> > Webster University
> >
> > Office Location: ISB room 316
> >
> > Office Number: (314) 968-7677
> >
> > Ugo at webster.edu <UgoBruzadinNunes at webster.edu>
> >
> >
> >
> > *From: *Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
> > *Sent: *Thursday, November 11, 2021 11:56 AM
> > *To: *eeglablist at sccn.ucsd.edu
> > *Cc: *Bruzadin Nunes, Ugo <ugob at siu.edu>
> > *Subject: *Re: [Eeglablist] Installing CUDAICA on Windows 10 (2021
> update)
> >
> >
> >
> > *[EXTERNAL EMAIL ALERT]: *Verify sender before opening links or
> > attachments.
> >
> > Dear John,
> >
> > Thank you for your comment.
> > It was difficult for me partly because I'm not very experienced in
> > building an environment, and also because of recent changes in the
> > dependent softwares between May 2019 and Nov 2011.
> >
> >    1. Microsoft updated Intel Parallel Studio XE to oneAPI which made a
> >    critical part in Ugo's suggestions no longer valid.
> >    2. I did not find 'C:\Users\Ugo\AppData\Local\Programs\Microsoft VS
> >    Code\bin' just by following Ugo's suggestions. I found that
> installing Microsoft
> >    Visual Studio Code is necessary in my case, which may be due to
> Microsoft's
> >    update on Windows Visual Studio (but probably this is not a part of
> >    requirements.)
> >
> > Ugo posted his solution only 2.5 years ago. Yesterday, I spent 10 hours
> to
> > make it work, which shows fow fast technology is left behind time. And it
> > is not because the technology becomes obsolete but because it becomes a
> > lost technology due to software updates.
> >
> >
> >
> > > We use the 48core computers for the runica, but it does not appear to
> > profit from the multiple CPUs.
> >
> >
> >
> > You'll definitely benefit from running AMICA using all the cores! It may
> > not be as fast as CUDAICA, but AMICA has some nice extra features
> including
> > auto data rejection, time-series data of model's log likelihood, etc.
> >
> >
> >
> > Makoto
> >
> >
> >
> > On Wed, Nov 10, 2021 at 10:14 PM Richards, John <RICHARDS at mailbox.sc.edu
> >
> > wrote:
> >
> > 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=Huri_n6jWh8zdcR4owEEJ4-OCWk3Tuhb-lZ4bO5ZmEPVSvmQwaK_oBMvCoL8R4J1&s=eEYNpj01sEGAM90ppVv06ny9mss5jBeptXZmxzGMfIg&e=
> > <
> https://urldefense.proofpoint.com/v2/url?u=https-3A__nam11.safelinks.protection.outlook.com_-3Furl-3Dhttps-253A-252F-252Furldefense.proofpoint.com-252Fv2-252Furl-253Fu-253Dhttps-2D3A-5F-5Fjerlab.sc.edu-2526d-253DDwIFAw-2526c-253D-2D35OiAkTchMrZOngvJPOeA-2526r-253DpyiMpJA6aQ3IKcfd-2DjIW1kWlr8b1b2ssGmoavJHHJ7Q-2526m-253DHuri-5Fn6jWh8zdcR4owEEJ4-2DOCWk3Tuhb-2DlZ4bO5ZmEPVSvmQwaK-5FoBMvCoL8R4J1-2526s-253DeEYNpj01sEGAM90ppVv06ny9mss5jBeptXZmxzGMfIg-2526e-253D-26data-3D04-257C01-257Cugob-2540siu.edu-257C80d5d8673e504103874208d9a53c8282-257Cd57a98e7744d43f9bc9108de1ff3710d-257C0-257C0-257C637722501596961122-257CUnknown-257CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0-253D-257C1000-26sdata-3DYUqURUVDzW6NVPmrX2X7EKTd1GFdScJ-252Fx8WvBeiCY2k-253D-26reserved-3D0&d=DwMF-g&c=-35OiAkTchMrZOngvJPOeA&r=pyiMpJA6aQ3IKcfd-jIW1kWlr8b1b2ssGmoavJHHJ7Q&m=xCIaec-j68pAjBO5h2bJbv9UpgA1ggpMle00jnRJBaalmbJIS9bcXxyfKIzP4JVW&s=nnqOqOx5gwPTIf9jE3g-YBX-aSR9bQWmNcoTrT5UIps&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
> > <
> https://urldefense.proofpoint.com/v2/url?u=https-3A__nam11.safelinks.protection.outlook.com_-3Furl-3Dhttps-253A-252F-252Fsccn.ucsd.edu-252Fwiki-252FMakoto-252527s-5Fuseful-5FEEGLAB-5Fcode-2523By-5Fusing-5FCUDAICA-5F.2811.2F10.2F2021-5Fadded.29-26data-3D04-257C01-257Cugob-2540siu.edu-257C80d5d8673e504103874208d9a53c8282-257Cd57a98e7744d43f9bc9108de1ff3710d-257C0-257C0-257C637722501596971116-257CUnknown-257CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0-253D-257C1000-26sdata-3Dzw0zqq64KyJjpuKEga2TGl0hFmXAT33s9CLMCBfpTCg-253D-26reserved-3D0&d=DwMF-g&c=-35OiAkTchMrZOngvJPOeA&r=pyiMpJA6aQ3IKcfd-jIW1kWlr8b1b2ssGmoavJHHJ7Q&m=xCIaec-j68pAjBO5h2bJbv9UpgA1ggpMle00jnRJBaalmbJIS9bcXxyfKIzP4JVW&s=BfR4UwFvE-Oat0CSnFhbgvxSsPL5JTnajS0rX1fcg00&e=
> >
> >
> > WARNING: The installation was difficult.
> >
> > Makoto
> > _______________________________________________
> > Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
> > <
> https://urldefense.proofpoint.com/v2/url?u=https-3A__nam11.safelinks.protection.outlook.com_-3Furl-3Dhttp-253A-252F-252Fsccn.ucsd.edu-252Feeglab-252Feeglabmail.html-26data-3D04-257C01-257Cugob-2540siu.edu-257C80d5d8673e504103874208d9a53c8282-257Cd57a98e7744d43f9bc9108de1ff3710d-257C0-257C0-257C637722501596971116-257CUnknown-257CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0-253D-257C1000-26sdata-3D2z0JGE-252FK6zwR5hCwhG-252BXt8qyE9kQvVUrXGH5HBStRO8-253D-26reserved-3D0&d=DwMF-g&c=-35OiAkTchMrZOngvJPOeA&r=pyiMpJA6aQ3IKcfd-jIW1kWlr8b1b2ssGmoavJHHJ7Q&m=xCIaec-j68pAjBO5h2bJbv9UpgA1ggpMle00jnRJBaalmbJIS9bcXxyfKIzP4JVW&s=0gHgDXLlw1viQVbMNOafiBZqyFLJSY1oByZxQjoNEho&e=
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