similar to: [LLVMdev] llvm-gcc-4.2 vs gcc 4.4

Here are the results from current llvm/llvm-gcc-4.2 svn and gcc 4.2.4 for the Polyhedron 2005 benchmarks under MacOS X 10.5.6 on a MacPro. Ave Run (secs) Ave Run (secs) llvm-gcc-4.2/ Benchmark llvm-gcc-4.2 svn gcc 4.2.4 gcc 4.2.4 ac 15.01 15.32 0.980 aermod 15.96 22.96 0.695 air

Since the fact that gfortran performance has improved over the major releases, I decided to benchmark the current releases on a MacPro with the Polyhedron 2005 benchmarks using -ffast-math -funroll-loops -msse3 -O3. The results are... gcc release gcc 4.2.4 gcc 4.3.3 gcc 4.4-pre gcc 4.3.3/ gcc 4.4-pre/

R Help - I'm trying to run a correlation matrix with a covariate of "age" and will at some point will also want to covary other variables concurrently. I'm using the "psych" package and have tried other methods such as writing a loop to extract semi-partial correlations, but it does not seem to be working. How can I accomplish this? library(psych) > set.cor(y =

Hi, A simple quantile question: I need to calculate the 95% and 5% quantiles (aka percentiles) for the following data: 67.12 64.51 62.06 55.45 51.41 43.78 10.74 10.14 if I use the formula: 95% quantile point= 95 (8+1)/100, I get the 8.55th point as the 95% quantile. Which does not make too much sense as I have only 8 data points. The other option is to use (95*8)/100 = 7.6th data point (which can

Hello, my test.R file contains two huge arrays (>3000 entries), from which R needs to calculate the Pearson Correlation, if I look at the file the numbers look correct. if I run R R < test.R --no-save I see things like this: 0.723, 0.838, 1.002, 0.364, 0.357, 0.227, 0.982+ , 0.963, 0.535, 1.214, 1.270, 0.832, 1.033, 0.632, 2.482, 1.239, 0.743, 1.077, 0.962, 1.052, 1.075, 1.427, 1.395,

Dear R fans, I would like to create a scatterplot showing the relationship between 4 continuous variables. I thought of using the package "scatterplot 3d" to have a 3-dimensional plot and then using the size of the symbols to represent the 4th variable. Does anybody know how to do this? I already tried to create this graph using the colour of the symbols, but I was unable to generate

Dear R-devel: I could use some advice about matrix calculations and steps that might make for faster computation of generalized inverses. It appears in some projects there is a bottleneck at the use of svd in calculation of generalized inverses. Here's some Rprof output I need to understand. > summaryRprof("Amelia.out") $by.self self.time self.pct

Hello, Using the following lines of code, I created the following graph:

Hello, Using the following lines of code, I created the following graph:

Hiya, I''m trying to track down some throughput latency that our customer seems to be attributing to our product, I can''t see what he''s talking about, but I want to try and get some deeper granularity than I might get with something like smokeping, and maybe even see if its down to something tunable on our end. I''ve been looking for some examples on how

Dear useRs, I am trying to model a time series with a transfer function. I think it can be put into the ARMA framework, and estimated with the 'arima' function (and others have made similar comments on this list). I have tried to do that, but the results have so far been disappointing. Maybe I am trying to make 'arima' do something it can't... The data are time series of

All, I would like to apply a set of rules to each row of the sample data set below. The rule sets are the guidelines for determining an individual's date for retirement eligibility. The rules are found in this document, http://www.opm.gov/feddata/RetirementPaperFinal_v4.pdf. I am only interested in the top two categories for retirement eligibility, the CSRS and FERS plans. The data set has

On Sat, 2011-10-29 at 15:16 -0500, Hal Finkel wrote: > On Sat, 2011-10-29 at 14:02 -0500, Hal Finkel wrote: > > On Sat, 2011-10-29 at 12:30 -0500, Hal Finkel wrote: > > > Ralf, et al., > > > > > > Attached is the latest version of my autovectorization patch. llvmdev > > > has been CC'd (as had been suggested to me); this e-mail contains > >

I'm fitting a GLMM to some questionnaire data. The structure is J individuals, nested within I areas, all of whom answer the same K (ordinal) questions. The model I'm using is based on so-called continuation ratios, so that it can be fitted using the lme4 package. The lmer function fits the model just fine, but using mcmcsamp to judge the variability of the parameter estimates produces

Ralf, et al., Attached is the latest version of my autovectorization patch. llvmdev has been CC'd (as had been suggested to me); this e-mail contains additional benchmark results. First, these are preliminary results because I did not do the things necessary to make them real (explicitly quiet the machine, bind the processes to one cpu, etc.). But they should be good enough for discussion.

On Sat, 2011-10-29 at 12:30 -0500, Hal Finkel wrote: > Ralf, et al., > > Attached is the latest version of my autovectorization patch. llvmdev > has been CC'd (as had been suggested to me); this e-mail contains > additional benchmark results. > > First, these are preliminary results because I did not do the things > necessary to make them real (explicitly quiet the

pulling my hair out... If I rm -fr /root/.spamassassin/* then sa-learn a bunch of messages, it works... ]# sa-learn --dump magic 0.000 0 3 0 non-token data: bayes db version 0.000 0 3997 0 non-token data: nspam 0.000 0 0 0 non-token data: nham 0.000 0 202022 0 non-token data: ntokens 0.000

Hello R-help   I am working with large data table that have the occasional label,  a particular time point in an experiment. E.g: "Time (min)", "R1 R1", "R2 R1", "R3 R1", "R4 R1" .909, 1.117, 1.225, 1.048, 1.258 3.942, 1.113, 1.230, 1.049, 1.262 3.976, 1.105, 1.226, 1.051, 1.259 4.009, 1.114, 1.231, 1.053, 1.259 4.042, 1.107, 1.230, 1.048, 1.262

On Sat, 2011-10-29 at 14:02 -0500, Hal Finkel wrote: > On Sat, 2011-10-29 at 12:30 -0500, Hal Finkel wrote: > > Ralf, et al., > > > > Attached is the latest version of my autovectorization patch. llvmdev > > has been CC'd (as had been suggested to me); this e-mail contains > > additional benchmark results. > > > > First, these are preliminary

Dear R forum I have just started my venture with R. While I am trying to learn R through the tutorials, I think the current problem I need to address to is beyond my knowledge about R. I have a dataframe as defined below - rat_dat = data.frame(name = c("A", "A", "A", "B", "B", "B", "C", "C", "C",