search for: highperformancecomput

...elp. So far I found the a manual by Xianhong Xie from Rnews_2005-2 (see page 13) Talking about R and condor: http://cran.r-project.org/doc/Rnews/Rnews_2005-2.pdf I also found several references for R and condor in the task views of High Performance Computing<http://cran.r-project.org/web/views/HighPerformanceComputing.html> : http://cran.r-project.org/web/views/HighPerformanceComputing.html Stating that: "The GridR<http://cran.r-project.org/web/packages/GridR/index.html> package by Wegener et al. can be used in a grid computing environment via a web service, via ssh or via Condor or Globus.&quot...

Check the "high performance task view" on CRAN ... https://cran.r-project.org/web/views/HighPerformanceComputing.html On Fri, Nov 8, 2024, 7:58 PM Val <valkremk at gmail.com> wrote: > Hi All, > > I am reading data file ( > 1B rows) and do some date formatting like > dat=fread(mydatafile) > dat$date1 <- as.Date(ymd(dat$date1)) > > However, I am getting an error...

...uot;bootstrap", parallel=TRUE)} > \dontshow{ci(roc2, method="bootstrap", parallel=TRUE, boot.n=20)} > } > #endif The "superseded" part is more confusing to me, though. The doSNOW package seems to be still available on CRAN with no special notice, listed in the HighPerformanceComputing view likewise, and under active development (last change a couple of days ago on R-Forge). I could find no mention of what it has been superseded with. Surprisingly, Google was no help on this. I could see the note is triggered in QC.R file of the tools package. However this finding is not much...

Dear all, I'm sitting here just in front of my new PC@work and wonder about the following question: * How can I adress multiple CPUs (cores) out of R to speed up the simulations I run? * What are the prerequisites to do so? Maybe anyone could give me a hint where to start reading? Regards, Thomas P.S.: I searched the R-archive to find an answer but did find none.

...\dontshow{ci(roc2, method="bootstrap", parallel=TRUE, boot.n=20)} >>> } >>> #endif >> The "superseded" part is more confusing to me, though. The doSNOW >> package seems to be still available on CRAN with no special notice, >> listed in the HighPerformanceComputing view likewise, and under active >> development (last change a couple of days ago on R-Forge). I could find >> no mention of what it has been superseded with. Surprisingly, Google was >> no help on this. >> >> I could see the note is triggered in QC.R file of the too...

Hello I am fortunate (or in really big trouble) in that the research group I work with will soon be receiving several high end dual quad core machines. We will use the Ubuntu OS on these. We intend to use this cluster for some extensive modeling applications. Our programming guru has demonstrated the ability to link much simpler machines to share CPUs and we purchased the new ones to take

Hi R list, I'm new to R software, so I'd like to ask about it is capabilities. What I'm looking to do is to run some statistical tests on quite big tables which are aggregated quotes from a market feed. This is a typical set of data. Each day contains millions of records (up to 10 non filtered). 2011-05-24 750 Bid DELL 14130770 400 15.4800 BATS

...} > > \dontshow{ci(roc2, method="bootstrap", parallel=TRUE, boot.n=20)} > > } > > #endif > > The "superseded" part is more confusing to me, though. The doSNOW > package seems to be still available on CRAN with no special notice, > listed in the HighPerformanceComputing view likewise, and under active > development (last change a couple of days ago on R-Forge). I could find > no mention of what it has been superseded with. Surprisingly, Google was > no help on this. > > I could see the note is triggered in QC.R file of the tools package. > Ho...

Hi all, Lots of big IT companies are renting out their computing facilities. Amazon has one such service. In my understanding, this will dramatically improve the speed of my R program -- currently the cross validation and model selection part is the bottle neck. It take a few days to just finish optimal parameter tuning via CV. Could anybody shed some lights on this? Is it completely transparent

Dear All, I am a researcher, working at the Department of Software Engineering at University of Minho, Portugal. I am working on in the area of Distributed data mining and attempting to develop distributed data mining system in “R” Environment. I have an enquiry. Suppose I have 3 different nodes (machines) running same program with different set of data and these 3 machines save results in 3

Graphics about... Bayesian ChemPhys Cluster Distributions Econometrics Environmetrics ExperimentalDesign Finance Genetics gR Graphics HighPerformanceComputing MachineLearning MedicalImaging Multivariate NaturalLanguageProcessing Optimization Pharmacokinetics Psychometrics Robust SocialSciences Spatial Survival TimeSeries Other URL: http://bm2.genes.nig.ac.jp/RGM2/index.php?clear=all -- Share your knowledge on our R group at: http://www.analyticbridg...

I heard form my friend there is a way to run R in system hard disk space not in the RAM .By that we may not run out of memory and have problem attached with the same.Someone could help me in this.Thanks. -- View this message in context: http://n4.nabble.com/R-on-Hard-drive-memory-tp1401528p1401528.html Sent from the R help mailing list archive at Nabble.com.

Dear R Fans, I was recently asking myself how quick R is in code execution. I have been running simulations lately that need quite a time for execution and I was wondering if it is reasonable at all to do more computational extensive projects with R. Of course, it is possible to compare execution time for the same code written in several languages but maybe someone has some experience on the

I have a 9 GB RAM Windows Vista machine. I installed the 64-bit version of R 2.11.1 for Windows from here: http://cran.r-project.org/bin/windows64/base/ I am running a program now in R. However, looking at Windows Task Manager, I see that Rgui.exe is only using 12% of CPU and 191,900K of memory. How do I max it out? I know the default memory limit is the amount of installed RAM, but it

Hola, soy nuevo en la lista y en R, disculpen esta pregunta, pero no he encontrado informaci?n actualizada. Mi pregunta, es R capaz de realizar procesamiento paralelo? -- Atentamente Ing. Javier Alfonso Vald?s Universidad de las Ciencias Inform?ticas.

Hi R List, What is the place to search for *corporate version of R *including 1) GUI 2) Documentation 3) Cloud Computing or HPC Regards, Ajay Website- http://decisionstats.com Graduate Student University of Tennessee, Knoxville. Go Vols! [[alternative HTML version deleted]]

Hi all, I am looking for a function similar to mclapply() that would work with GPU cores. I have looked at all possible packages related to GPU computing but they are mainly providing functionality for big dataset or big matrices. I use mainly mclapply to speed up simulations by running several simulations in parallel, which works nicely. Is it possible to do the same with a multicore GPU? I

...roblem using the lp.transport function in lpSolve. My PC has 10 cores, but by default R uses only one of them. Is there a straightforward way to make lp.transport use all cores available? I had a look at "High-performance and parallel computing in R" ( http://cran.r-project.org/web/views/HighPerformanceComputing.html), but I have the impression that using multiple cores would require me to change the function underlying lp.transport. The problem is that I'm not able whether I'm able to make those adjustments. Thanks, Michael [[alternative HTML version deleted]]

Hello, Sorry if this question has been posted before but could't find out exactly an answer to the question.... I'm doing bioinformatics and doing small RNA sequencing that make use of packages such as DESeq and EDGE. For those familiar with this data you will notice that you end up having large matrices with millions of entries. So i guess many people might be facing the same problem

Hi All, I am reading data file ( > 1B rows) and do some date formatting like dat=fread(mydatafile) dat$date1 <- as.Date(ymd(dat$date1)) However, I am getting an error message saying that Error: cons memory exhausted (limit reached?) The script was working when the number rows were around 650M. Is there another way to handle a big data set in R? Thank you.