[Eeglablist] Installing CUDAICA on Windows 10 (2021 update)
Bruzadin Nunes, Ugo
ugob at siu.edu
Thu Nov 11 15:48:46 PST 2021
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!
Ugo Bruzadin Nunes, Ph.D. Candidate
Visiting Assistant Professor, Psychology
Office Location: ISB room 316
Office Number: (314) 968-7677
Ugo at webster.edu<mailto:UgoBruzadinNunes at webster.edu>
From: Makoto Miyakoshi<mailto:mmiyakoshi at ucsd.edu>
Sent: Thursday, November 11, 2021 11:56 AM
To: eeglablist at sccn.ucsd.edu<mailto:eeglablist at sccn.ucsd.edu>
Cc: Bruzadin Nunes, Ugo<mailto:ugob at siu.edu>
Subject: Re: [Eeglablist] Installing CUDAICA on Windows 10 (2021 update)
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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.
On Wed, Nov 10, 2021 at 10:14 PM Richards, John <RICHARDS at mailbox.sc.edu<mailto: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 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<mailto:richards-john at sc.edu>
From: eeglablist <eeglablist-bounces at sccn.ucsd.edu<mailto: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<mailto:eeglablist at sccn.ucsd.edu>>; ugob at siu.edu<mailto: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.
WARNING: The installation was difficult.
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