similar to: R performance

Dear R-Fans, The more I work with matrices (e.g., data.frames) the more I think it would be helpful to have functions to apply (several!) mathematical and/or logical operators column- or row-wise in a matrix. I know the function apply() and all its derivates (e.g., lapply) but I think this does not help for solving (e.g.) the following task: assume there is a 3x3 matrix: 1 2 4 4

Hi R Fans, I stumbled across a strange (I think) bug in R 2.9.1. I have read in a data file with 5934 rows and 9 columns with the commands: daten = data.frame(read.table("C:/fussball.dat",header=TRUE)) Then I needed a subset of the data file: newd = daten[daten[,1]!=daten[,2],] --> two values do not meet the logical specification and are dropped. The strange thing about it:

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

dimesio wrote: > > durammx wrote: > > Hi all! > > I trying to run this program (http://appdb.winehq.org/objectManager.php?sClass=version&iId=17469) but I'm a really noob on wine. :? > > Can a big master of wine test? > > > > Also posted a bug (http://bugs.winehq.org/show_bug.cgi?id=19678) on bugzilla. > > > > Hope I did it well. >

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

Dear R-help, A colleague of mine was running some code on two of our boxes, and noticed a rather large difference in running time. We've so far isolated the problem to the difference between R 1.7.1 and 1.8.0, but not more than that. The exact same code took 933.5 seconds in 1.7.1, and 3594.4 seconds in 1.8.1, on the same box. Basically, the code calls boot() to bootstrap fitting mixture

Hi, I am about to write functions for multivariate kernel densitiy estimation with mixed categorical and continuous date (accoring to Jeff Racine and Qi Li), and the leave-one-out window esitmation needs a lot of computation. I am now optimizing the code performance and therefore fhe following questions: As R uses call-by-value for functions, is it computational expensive to pass large matrices

Hi We use CentOS and RHEL, the 5.5 RHEL ISO for x86_64 is 3.7GB (**), the CentOS one is 4602MB (***) split over two DVDs. Is this reasonable and correct? Any ideas why would there be such a discrepancy if they are built from the same (or very similar) source? Regards Anthony Caetano ** the md5sum checks out, and RHN lists the size as 3,532 MB *** CentOS-5.5-x86_64-bin-DVD-1of2.iso +

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, Is nchar function knowingly slow in R? I'm doing some string formatting that requires multiple call to nchar, and nchar seems to be very slow. Experiment 1, pass nchar inside sprintf, and it takes 0.7 seconds > system.time(for (i in 1:10000) + str = sprintf('0005%020d', nchar(op)) + )[3] [1] 0.7 Experiment 2, get the length of op separately using nchar, and then pass

I wondered if there were standard practices in CRAN for delivery of R source implementing functions in R packages. I has encountered a couple of packages where the gzipped version of source contains very little, primarily the Help files describing the functions in the package. In some cases I can find the source as the value of the function name. Given that these packages are released as GPL,

On Thu, 26 Mar, 2020 at 18:39:56 -0600, R C wrote: > well,? sorry,? I thought it was somewhat "self-explaining", since that > terminology was used up until Centos 7 (see > > links), andof course I meant the official download page. > > > minimal: as in approx 3Gb or so that fits on a regular 4-5Gb rewritable DVD > as with Centos 7 > > download from :

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

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.

Hi all, I have some long-running code that I'm trying to profile. I am seeing a lot of time spent inside the <Anonymous> function. Of course, this can in fact be any of several functions, but I am unable to see how I could use the information from Rprof.out to discern which function is taking the most time. An example line from my Rprof.out is: rbernoulli <Anonymous>

This looks like a bug? Unable to use scan(pipe()) while profiling. I have no idea whether this version of R violates the "do not use `Rprof' in an executable built for profiling" warning in ?Rprof. Thanks -Don > version _ platform powerpc-apple-darwin1.4 arch powerpc os darwin1.4 system powerpc, darwin1.4 status Patched major 1 minor 3.1 year

Hi, I would like to ask how the paste(S1, S2, sep="") function internally works. Are the two stings copied to a new String? I have a program where successively strings are build up. First the program calls an external function and depending on the result it builds up strings to visualize the result. The external function is really fast, also for huge input data. But the

Hello, Appending information to the profiler's output seems to generate problems. Here is a small example of code : <code r> require(boot) Rprof( memory.profiling = TRUE) Rprof(NULL) for(i in 1:2){ Rprof( memory.profiling = TRUE, append = TRUE) example(boot) Rprof(NULL) } </code> The problem is that the file Rprof.out contains more than once the header information: $ grep

Hi, I' am interested in differences between sample's result when samples consist of full elements and consist of only distinct elements. When sample consist of full elements it take about 120 sec., but when consist of only distinct elements it take about 4.5 or 5 times more sec. I expected that opposite of this result, because unique(sample) has less elements than full sample. Code as

Rprof seems to ignore all time spent inside system() calls. E.g., this simple example actually takes about 10 seconds, but Rprof thinks the total time is only 0.12 seconds: > Rprof("sleep-system.out") ; system.time(system(command="sleep 10")) ; Rprof(NULL) user system elapsed 0.000 0.004 10.015 > summaryRprof("sleep-system.out")$by.total