[BioC] R: GPU Computing with Flow Cytometry

Manca Marco (PATH) m.manca at maastrichtuniversity.nl
Fri Nov 16 08:26:45 CET 2012


Dear Kevin

I am no expert of the package you are enquiring about, but in general you might be interested in substituting the generic BLAS on your machine with Intel's MKL (non free, but available with no costs for research via Intel) in order to enable native parallelization of most of the plausible functions, with no need to modify your code. Here are some tests:
http://planetflux.adamwilson.us/2012/05/compiling-r-with-intels-math-kernel.html

You can get inspiration on the way to install MKL and force its use by R, by reading this blog
http://www.r-bloggers.com/compiling-64-bit-r-2-10-1-with-mkl-in-linux/

The use of your GPU is a completely different issue, and despite having a CUDA enabled GPU myself I gave up on it for lack of time and frustration... but maybe you are smarter and luckier than me.
You can start reading from this page and then follow the links:
http://brainarray.mbni.med.umich.edu/Brainarray/rgpgpu/

I hope this helps. Good luck and best regards,
Marco


--
Dr Marco Manca
University of Maastricht
Faculty of Health, Medicine and Life Sciences (FHML)
Cardiovascular Research Institute (CARIM)

Mailing address: PO Box 616, 6200 MD Maastricht (The Netherlands)
Visiting address: UNS40 West building - 5th floor Room5.544, Universiteit Singel 40, 6229  HX Maastricht

E-mail: m.manca at maastrichtuniversity.nl
Office telephone: +31(0)433884289
Personal mobile: +31(0)626441205
Twitter: @markomanka


*********************************************************************************************************************

This email and any files transmitted with it are confidential and solely for the use of the intended recipient.

It may contain material protected by privacy or doctor-patient/consultant-client privilege. If you are not the intended recipient or the person responsible for

delivering to the intended recipient, be advised that you have received this email in error and that any use is STRICTLY PROHIBITED.

If you have received this email in error please notify us by telephone on +31626441205 Dr Marco MANCA

*********************************************************************************************************************
________________________________________
Da: bioconductor-bounces at r-project.org [bioconductor-bounces at r-project.org] per conto di Kevin Schiesser [schiesserk at medsfgh.ucsf.edu]
Inviato: venerdì 16 novembre 2012 0.18
A: kevin.schiesser at ucsf.edu
Cc: bioconductor at r-project.org
Oggetto: Re: [BioC] GPU Computing with Flow Cytometry

On 11/15/2012 02:52 PM, Kevin Schiesser wrote:
> Hi y'all,
>
> Does Bioconductor support GPU/multi-core computing for flow cytometry
> data analysis?
>
> For instance, in the flowMerge package there is a pFlowClust option.
> When I load the flowMerge library, and call flowClust(), I get the
> output 'Using the serial version of flowClust'. I have a quad-core
> processor (Intel(R) Core(TM) i5-3210M CPU @ 2.50GHz) and a geForce GT
> 650M video card (CUDA enabled) running on Debian Testing
> 3.2.0-4-amd64. What does pFlowClust look for to choose serial or
> parallel?
>
> I ask because on my lowly laptop the runtime for flowClust(dat,
> K=1:10) prohibits sound troubleshooting for a flowFrame with 9 stains.
> I would like to optimize the hardware I have access to, but I am
> running out of ideas. Any thoughts?
>
> Much thanks,
> Kevin
>
Nevermind... I found
http://www.findthatdoc.com/search-39323504-hPDF/download-documents-flowclustflowmergeslides-pdf.htm
to keep me busy for a while... Thanks Greg!

Still, GPU computing would be a very nice option for the
flowSuiteOfSoftwares. -k

_______________________________________________
Bioconductor mailing list
Bioconductor at r-project.org
https://stat.ethz.ch/mailman/listinfo/bioconductor
Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor



More information about the Bioconductor mailing list